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1,Geostatistical Analysis,"Your task is analyzing urban heat using Kriging interpolation techniques. The analysis should focus on understanding spatial patterns of urban heat islands by using point temperature data and interpolating these values across a city. You will have to use a demographic layer to extract and enhance the data visualization on the elder group. The goal is to apply the Kriging method to predict temperature across the urban area, and generate a choropleth map showing the average interpolated temperature surface in each census block group. Highlighting areas with interpolated temperatures greater than 60 and elderly population densities exceeding 100,000. The final output should be saved as ""interpolated_urban_heat.png"".","dataset/Temperature.geojson: Geojson file that stores temperature at different coordinates as points. 'TemperatureF'' Column stores the temperature information in Fahrenheit.
dataset/CensusBlock.geojson: Geojson file that stores the census block of Madison as polygons. ""Block_Groups_Over65Density"" column stores the density of the elderly population per census block.","heat distribution style:{""column"": <placeholder>, ""cmap"": ""OrRd"", ""alpha"": 0.9, ""label"": ""Heat Distribution""}
high risk areas style:{""facecolor"": ""none"", ""edgecolor"": ""blue"", ""linewidth"": 2, ""label"": ""High Risk Areas (>60F, Dense Elderly)""}",7,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/Temperature.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/CensusBlock.geojson""
}
},
{
""tool"": ""ordinary_kriging"",
""arguments"": {
""points_geojson_path"": ""dataset/Temperature.geojson"",
""value_property"": ""TemperatureF"",
""grid_bounds"": [
547865.7558000004,
273075.7083999999,
587617.1856000004,
311161.2719999999
],
""nx"": 100,
""ny"": 100,
""variogram_model"": ""spherical"",
""output_name"": ""krig_Temperature.tif""
}
},
{
""tool"": ""zonal_statistics"",
""arguments"": {
""raster_path"": ""output/krig_Temperature.tif"",
""polygons_path"": ""dataset/CensusBlock.geojson"",
""output_name"": ""agg_CensusBlock_mean.geojson"",
""band"": 1,
""stats"": [
""mean""
]
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""output/agg_CensusBlock_mean.geojson""
}
},
{
""tool"": ""filter_features_by_expression"",
""arguments"": {
""input_path"": ""output/agg_CensusBlock_mean.geojson"",
""output_name"": ""filter_agg_CensusBlock_mean.geojson"",
""expression"": ""prediction_mean > 60 and Block_Groups_Over65Density > 100000""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""output/agg_CensusBlock_mean.geojson"",
""style"": {
""column"": ""prediction_mean"",
""cmap"": ""OrRd"",
""alpha"": 0.9,
""label"": ""Heat Distribution""
}
},
{
""data"": ""output/filter_agg_CensusBlock_mean.geojson"",
""style"": {
""facecolor"": ""none"",
""edgecolor"": ""blue"",
""linewidth"": 2,
""label"": ""High Risk Areas (>60F, Dense Elderly)""
}
}
],
""output_name"": ""interpolated_urban_heat.png"",
""title"": ""Highlighted Heat Blocks (60F+) on Elders (Age 65+)""
}
}
]",interpolated_urban_heat.png,"agg_CensusBlock_mean.geojson
filter_agg_CensusBlock_mean.geojson",,,,,,,,,,
2,Vector Spatial Analysis,"Your task is performing analysis on public transit access in Hamilton County Tennessee. The goal is to map out existing bus stops service area with enriched map layers for different census blocks, including poverty, population density, and accessibility to a vehicle. You should overlay the three demographical factors with the bus service area using different transparent color schemes. Visualizing the resulting data as ""transit_access.png"".","dataset/BusServiceArea.geojson: Geojson file stores the bus stop service areas in polygons
dataset/HamiltonDemographics.geojson: Geojson file that stores census block demographic information in polygons. 'populationToPolygonSizeRating' column represents population density. 'households_ACSHHBPOV' represents poverty rate. 'AtRisk_ACSOVEH0' represents population without ownership to vehicles.","population density style:{""column"": <placeholder>, ""cmap"": ""Greens"", ""linewidth"": 0.1, ""alpha"": 0.5, ""label"": ""Population Density""}
no vehicle access style:{""column"": <placeholder>, ""cmap"": ""Blues"", ""linewidth"": 0.1, ""alpha"": 0.5, ""label"": ""No Vehicle Access""}
poverty households style:{""column"": <placeholder>, ""cmap"": ""Reds"", ""linewidth"": 0.1, ""alpha"": 0.5, ""label"": ""Poverty Households""}
bus service area style:{""color"": ""none"", ""edgecolor"": ""black"", ""linewidth"": 1.5, ""label"": ""Bus Service Area""}",3,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/BusServiceArea.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/HamiltonDemographics.geojson""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""dataset/HamiltonDemographics.geojson"",
""style"": {
""column"": ""populationToPolygonSizeRating"",
""cmap"": ""Greens"",
""linewidth"": 0.1,
""alpha"": 0.5,
""label"": ""Population Density""
}
},
{
""data"": ""dataset/HamiltonDemographics.geojson"",
""style"": {
""column"": ""AtRisk_ACSOVEH0"",
""cmap"": ""Blues"",
""linewidth"": 0.1,
""alpha"": 0.5,
""label"": ""No Vehicle Access""
}
},
{
""data"": ""dataset/HamiltonDemographics.geojson"",
""style"": {
""column"": ""households_ACSHHBPOV"",
""cmap"": ""Reds"",
""linewidth"": 0.1,
""alpha"": 0.5,
""label"": ""Poverty Households""
}
},
{
""data"": ""dataset/BusServiceArea.geojson"",
""style"": {
""color"": ""none"",
""edgecolor"": ""black"",
""linewidth"": 1.5,
""label"": ""Bus Service Area""
}
}
],
""output_name"": ""transit_access.png"",
""title"": ""Demographic Variables and Bus Service Area in Hamilton County, TN""
}
}
]",transit_access.png,"BusServiceArea.geojson
HamiltonDemographics.geojson",,,,,,,,,,
3,Raster Spatial Analysis,"Your task is assessing burn scars using satellite imagery and perform spatial analysis to understand the impact of wildfires. The goal is to use the satellite imagery data from 2014 and 2015 on analyzing burn scars by determining the change in Normalized Burn Ratio. Then you should generate a map that visualizes the spatial extent of the damage areas in vector data. The final output should be a visual representation of the burn scars, saved as ""burn_scar_analysis.png"".","dataset/G_2014.tif: Raster file of satellite imageries in 2014, taken by Landsat 8
dataset/G_2015.tif: Raster file of satellite imageries in 2015, taken by Landsat 8","burn severity style:{""cmap"": ""Reds"", ""label"": ""Burn Severity"", ""vmin"": 0, ""vmax"": 0.8}
burn scar boundary style:{""facecolor"": ""none"", ""edgecolor"": ""black"", ""linewidth"": 1, ""label"": ""Burn Scar Boundary""}",9,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/G_2014.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/G_2015.tif""
}
},
{
""tool"": ""compute_nbr"",
""arguments"": {
""raster_path"": ""dataset/G_2014.tif"",
""output_name"": ""nbr_G_2014.tif"",
""nir_band"": 5,
""swir_band"": 7
}
},
{
""tool"": ""compute_nbr"",
""arguments"": {
""raster_path"": ""dataset/G_2015.tif"",
""output_name"": ""nbr_G_2015.tif"",
""nir_band"": 5,
""swir_band"": 7
}
},
{
""tool"": ""calculate_raster_difference"",
""arguments"": {
""raster_a_path"": ""output/nbr_G_2014.tif"",
""raster_b_path"": ""output/nbr_G_2015.tif"",
""output_name"": ""nbr_diff.tif""
}
},
{
""tool"": ""threshold_raster"",
""arguments"": {
""raster_path"": ""output/nbr_diff.tif"",
""output_name"": ""nbr_burn_mask.tif"",
""threshold"": 0.2,
""true_value"": 1,
""false_value"": 0
}
},
{
""tool"": ""convert_mask_to_polygons"",
""arguments"": {
""mask_path"": ""output/nbr_burn_mask.tif"",
""output_name"": ""nbrdelta_nbr_G_2014_nbr_G_2015_burnscar.geojson"",
""mask_value"": 1
}
},
{
""tool"": ""apply_mask_to_raster"",
""arguments"": {
""input_raster_path"": ""output/nbr_diff.tif"",
""mask_raster_path"": ""output/nbr_burn_mask.tif"",
""output_name"": ""nbrdelta_nbr_G_2014_nbr_G_2015_burndisplay.tif"",
""mask_value"": 1
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""output/nbrdelta_nbr_G_2014_nbr_G_2015_burndisplay.tif"",
""type"": ""raster"",
""style"": {
""cmap"": ""Reds"",
""label"": ""Burn Severity"",
""vmin"": 0,
""vmax"": 0.8
}
},
{
""data"": ""output/nbrdelta_nbr_G_2014_nbr_G_2015_burnscar.geojson"",
""type"": ""vector"",
""style"": {
""facecolor"": ""none"",
""edgecolor"": ""black"",
""linewidth"": 1,
""label"": ""Burn Scar Boundary""
}
}
],
""output_name"": ""burn_scar_analysis.png"",
""title"": ""Burn Scar Area (2014 vs 2015) in Montana Fire""
}
}
]",burn_scar_analysis.png,"nbrdelta_nbr_G_2014_nbr_G_2015_burndisplay.tif
nbrdelta_nbr_G_2014_nbr_G_2015_burnscar.geojson",,,,,,,,,,
4,Raster Spatial Analysis,"Your task is to identify risk zones. First, project mc_soils.shp, mc_boundary.shp, and mc_land_cover.tif to the Lambert Conformal Conic projection (EPSG:32126). Next, using mc_boundary.shp as the processing extent and mc_land_cover.tif as both the cell size and snap raster, extract drainage conditions and water depth from mc_soils.shp as raster layers. Use these two rasters to perform suitability modeling and calculate the vulnerable_areas. Then, combine vulnerable_areas with land_cover in a second suitability modeling step to generate risk_zones. Finally, visualize the resulting risk zones and save the map as ""risk_zones.png"".
Suitability modeling is an analytical process used to identify the optimal location or suitability of geographic areas. It generally consists of three steps: data preparation, data reclassification, and weighted overlay. For preparation: Polygon data needs to be converted into Raster before suitability modeling. For reclassification: The reclassification of drainage_conditions is [[1, 3], [2, 1], [3, 4], [4, 5], [5, 2]]. The reclassification of water_depth is [[0, 10, 5], [10, 33, 4], [33, 61, 3], [61, 80, 2], [80, 92, 1]]. The reclassification of vulnerable_areas is standardization to [1, 10]. The reclassification of land_cover is [[11, 1],[21, 6],[22, 7],[23, 8],[24, 10],[31, 4],[41, 3],[42, 1],[43, 3],[52, 3],[71, 2],[81, 5],[82, 9],[90, 1],[95, 1]]. For weighted overlay: vulnerable_areas = drainage_conditions * 5 + water_depth * 4; risk_zones = vulnerable_areas * 8 + land_cover * 10.","dataset/mc_soils.shp: In this shapefile, three fields are important in groundwater analysis: Drainage Class – Dominant Conditions, Hydrologic Group – Dominant Conditions, and Water Table Depth – Annual – Minimum.
When converting the drainage class to raster, the following mapping is used to assign numeric values:{""well drained"": 1,""moderately well drained"": 2,""somewhat poorly drained"": 3,""poorly drained"": 4,""very poorly drained"": 5}
dataset/mc_boundary.shp: This shapefile contains the polygon region of study area.
dataset/mc_land_cover.tif: Raster file showing the land cover of study area, one band.","risk level style:{""cmap"": ""inferno"", ""legend"": True, ""legend_label"": ""Risk Level""}",15,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/mc_land_cover.tif""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/mc_soils.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/mc_boundary.shp""
}
},
{
""tool"": ""reproject_raster"",
""arguments"": {
""raster_path"": ""dataset/mc_land_cover.tif"",
""target_crs"": ""EPSG:32126"",
""output_name"": ""land_cover_32126.tif"",
""band"": 1
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""dataset/mc_soils.shp"",
""target_crs"": ""EPSG:32126"",
""output_name"": ""soils_32126.shp""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""dataset/mc_boundary.shp"",
""target_crs"": ""EPSG:32126"",
""output_name"": ""boundary_32126.shp""
}
},
{
""tool"": ""rasterize_polygons"",
""arguments"": {
""polygons_path"": ""output/soils_32126.shp"",
""boundary_path"": ""output/boundary_32126.shp"",
""reference_raster_path"": ""output/land_cover_32126.tif"",
""value_field"": ""drclassdcd"",
""output_name"": ""soils_drainage.tif"",
""value_map"": {
""well drained"": 1,
""moderately well drained"": 2,
""somewhat poorly drained"": 3,
""poorly drained"": 4,
""very poorly drained"": 5
},
""fill_value"": 0
}
},
{
""tool"": ""reclassify_raster_values"",
""arguments"": {
""raster_path"": ""output/soils_drainage.tif"",
""output_name"": ""soils_drainage_reclass.tif"",
""ranges"": [
[
1,
1,
3
],
[
2,
2,
1
],
[
3,
3,
4
],
[
4,
4,
5
],
[
5,
5,
2
]
],
""band"": 1
}
},
{
""tool"": ""rasterize_polygons"",
""arguments"": {
""polygons_path"": ""output/soils_32126.shp"",
""boundary_path"": ""output/boundary_32126.shp"",
""reference_raster_path"": ""output/land_cover_32126.tif"",
""value_field"": ""wdepannmin"",
""output_name"": ""soils_depth.tif"",
""fill_value"": 0
}
},
{
""tool"": ""reclassify_raster_values"",
""arguments"": {
""raster_path"": ""output/soils_depth.tif"",
""output_name"": ""soils_depth_reclass.tif"",
""ranges"": [
[
0,
10,
5
],
[
10,
33,
4
],
[
33,
61,
3
],
[
61,
80,
2
],
[
80,
92,
1
]
],
""band"": 1
}
},
{
""tool"": ""weighted_sum_rasters"",
""arguments"": {
""raster_paths"": [
""output/soils_drainage_reclass.tif"",
""output/soils_depth_reclass.tif""
],
""output_name"": ""soils_weighted.tif"",
""bands"": [
1,
1
],
""weights"": [
5.0,
4.0
]
}
},
{
""tool"": ""normalize_raster_values"",
""arguments"": {
""input_raster_path"": ""output/soils_weighted.tif"",
""output_name"": ""soils_weighted_norm.tif"",
""lower"": 1.0,
""upper"": 10.0
}
},
{
""tool"": ""reclassify_raster"",
""arguments"": {
""raster_path"": ""output/land_cover_32126.tif"",
""output_name"": ""land_cover_reclass.tif"",
""reclass_dict"": {
""11"": 1,
""21"": 6,
""22"": 7,
""23"": 8,
""24"": 10,
""31"": 4,
""41"": 3,
""42"": 1,
""43"": 3,
""52"": 3,
""71"": 2,
""81"": 5,
""82"": 9,
""90"": 1,
""95"": 1
},
""band"": 1
}
},
{
""tool"": ""weighted_sum_rasters"",
""arguments"": {
""raster_paths"": [
""output/soils_weighted_norm.tif"",
""output/land_cover_reclass.tif""
],
""output_name"": ""final_risk.tif"",
""bands"": [
1,
1
],
""weights"": [
8.0,
10.0
]
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""output/final_risk.tif"",
""output_name"": ""risk_zones.png"",
""cmap"": ""inferno"",
""title"": ""Risk Zones"",
""legend"": true,
""legend_label"": ""Risk Level""
}
}
]",risk_zones.png,final_risk.tif,,,,,,,,,,
5,Geostatistical Analysis,"Your task is visualizing data about children with elevated blood lead levels in a privacy-conscious manner. First, conduct Optimized Hot Spot Analysis to identify clusters without revealing individual points. Then, apply tessellation to summarize cases within hexagonal polygons. This approach protects sensitive data while clearly representing areas with higher occurrences of elevated blood lead levels. The goal is to find the area with potential blood lead rise with specific cases saved in point files. The final output, saved as ""blood_lead_hotspots_hex.png"", ensures stakeholders receive relevant information without accessing specific locations","dataset/High_Blood_Level_Results.shp: This shapefile contains point data representing incidents of elevated blood lead levels in children within the study area. Each point corresponds to a specific geographic location where a blood test was conducted, and the results indicated a high concentration of lead.
dataset/Sacramento_ZIP_Codes.shp: This shapefile contains polygon data representing the boundaries of ZIP codes within Sacramento. Each polygon defines the geographic extent of a specific ZIP code.","hex bin counts style: {""label"": ""Hex Bin Counts"",""column"": <placeholder>,""scheme"": ""UserDefined"",""classification_kwds"": {""bins"": [1, 2]},""cmap"": ""Reds"",""legend"": True,""alpha"": 0.6,""edgecolor"": ""black"",""linewidth"": 0.3}
sacramento ZIP codes style: {""label"": ""Sacramento ZIP Codes"",""facecolor"": ""none"",""edgecolor"": ""grey"",""linewidth"": 1}",7,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/High_Blood_Level_Results.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/Sacramento_ZIP_Codes.shp""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""dataset/Sacramento_ZIP_Codes.shp"",
""target_crs"": ""EPSG:3857"",
""output_name"": ""sacramento_3857.shp""
}
},
{
""tool"": ""create_hexagonal_grid"",
""arguments"": {
""ref_gdf_path"": ""output/sacramento_3857.shp"",
""output_name"": ""sacramento_hex_grid.shp"",
""hex_size"": 500
}
},
{
""tool"": ""count_points_in_polygons"",
""arguments"": {
""polygons_path"": ""output/sacramento_hex_grid.shp"",
""points_path"": ""dataset/High_Blood_Level_Results.shp"",
""output_name"": ""sacramento_hex_counts.geojson"",
""count_field_name"": ""inc_cnt""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""output/sacramento_hex_counts.geojson""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""output/sacramento_hex_counts.geojson"",
""style"": {
""label"": ""Hex Bin Counts"",
""column"": ""inc_cnt"",
""scheme"": ""UserDefined"",
""classification_kwds"": {
""bins"": [
1,
2
]
},
""cmap"": ""Reds"",
""legend"": true,
""alpha"": 0.6,
""edgecolor"": ""black"",
""linewidth"": 0.3
}
},
{
""data"": ""output/sacramento_3857.shp"",
""style"": {
""label"": ""Sacramento ZIP Codes"",
""facecolor"": ""none"",
""edgecolor"": ""grey"",
""linewidth"": 1
}
}
],
""output_name"": ""blood_lead_hotspots_hex.png"",
""title"": ""Elevated Blood Lead Hotspots (Hex Tessellation)""
}
}
]",blood_lead_hotspots_hex.png,"sacramento_3857.shp
sacramento_hex_counts.geojson",,,,,,,,,,
6,Geostatistical Analysis,"Yout task is analyzing and visualizing Elk movements in the given dataset. The goal is to estimate home ranges and assess habitat preferences using spatial analysis techniques, including Minimum Bounding Geometry (Convex Hull), Kernel Density, and Density-based Clustering(DBSCAN). You should identify the spatial clusters of Elk movements and overlay the findings with maps and visualizations. Save the final output as ""Elk_Analysis.png"".",dataset/Elk_in_Southwestern_Alberta_2009.geojson: geojson files for storing points of Elk movements in Southwestern Alberta 2009.,"density style:{""label"": ""Density"", ""cmap"": ""viridis"", ""alpha"": 0.6, ""colorbar"": True}
convex hull style:{""facecolor"": ""none"", ""edgecolor"": ""red"", ""linewidth"": 1.0, ""alpha"": 0.8, ""label"": ""Convex Hull""}
clusters style:{""color"": ""orange"", ""alpha"": 0.6, ""label"": ""Clusters"", ""markersize"": 5}",6,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/Elk_in_Southwestern_Alberta_2009.geojson""
}
},
{
""tool"": ""calculate_bandwidth"",
""arguments"": {
""input_path"": ""dataset/Elk_in_Southwestern_Alberta_2009.geojson""
}
},
{
""tool"": ""compute_convex_hull"",
""arguments"": {
""input_path"": ""dataset/Elk_in_Southwestern_Alberta_2009.geojson"",
""output_name"": ""elk_convex_hull.geojson""
}
},
{
""tool"": ""compute_kernel_density"",
""arguments"": {
""input_path"": ""dataset/Elk_in_Southwestern_Alberta_2009.geojson"",
""bandwidth"": 11.2373,
""grid_res"": 100,
""output_name"": ""elk_kde.tif""
}
},
{
""tool"": ""density_clusters"",
""arguments"": {
""input_path"": ""dataset/Elk_in_Southwestern_Alberta_2009.geojson"",
""min_cluster_size"": 100,
""output_name"": ""elk_clusters.geojson""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""output/elk_kde.tif"",
""type"": ""raster"",
""style"": {
""label"": ""Density"",
""cmap"": ""viridis"",
""alpha"": 0.6,
""colorbar"": true
}
},
{
""data"": ""output/elk_convex_hull.geojson"",
""type"": ""vector"",
""style"": {
""facecolor"": ""none"",
""edgecolor"": ""red"",
""linewidth"": 1.0,
""alpha"": 0.8,
""label"": ""Convex Hull""
}
},
{
""data"": ""output/elk_clusters.geojson"",
""type"": ""vector"",
""style"": {
""color"": ""orange"",
""alpha"": 0.6,
""label"": ""Clusters"",
""markersize"": 5
}
}
],
""output_name"": ""Elk_Analysis.png"",
""title"": ""Elk Kernel Density, Convex Hull, and Clusters"",
""legend_loc"": ""best""
}
}
]",Elk_Analysis.png,"elk_clusters.geojson
elk_convex_hull.geojson
elk_kde.tif",,,,,,,,,,
7,Hydrological Analysis,"Your task is analyzing the impact of land subsidence on flooding based on future elevation data of the study area with a design water level of -200. Identify flood-prone areas and estimate potential building damage to support urban planning and mitigation strategies. Save the results to ""flooding_analysis.png"".","dataset/Elevation_2050.tif: Raster file for storing predicted flood elevation in 2050.
dataset/StudyAreaBuildings.shp: Polygon Shapefile for shape and features of buildings in the study area.","flood depth style:{""label"": ""Flood Depth"", ""cmap"": ""Blues"", ""alpha"": 0.6, ""colorbar"": True}
buildings style:{""facecolor"": ""none"", ""edgecolor"": ""red"", ""linewidth"": 0.5, ""alpha"": 0.8, ""label"": ""Buildings""}",5,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/StudyAreaBuildings.shp""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/Elevation_2050.tif""
}
},
{
""tool"": ""compute_flood_depth"",
""arguments"": {
""elevation_raster_path"": ""dataset/Elevation_2050.tif"",
""output_name"": ""flood_depth.tif"",
""threshold"": -200
}
},
{
""tool"": ""summarize_damage"",
""arguments"": {
""buildings_in_flood_path"": ""dataset/StudyAreaBuildings.shp"",
""original_buildings_path"": ""dataset/StudyAreaBuildings.shp"",
""raster_path"": ""./output\\flood_depth.tif"",
""output_name"": ""damage_assessment.geojson"",
""group_field"": null,
""sample_band"": 1
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""./output\\flood_depth.tif"",
""type"": ""raster"",
""style"": {
""label"": ""Flood Depth"",
""cmap"": ""Blues"",
""alpha"": 0.6,
""colorbar"": true
}
},
{
""data"": ""./output\\damage_assessment.geojson"",
""type"": ""vector"",
""style"": {
""facecolor"": ""none"",
""edgecolor"": ""red"",
""linewidth"": 0.5,
""alpha"": 0.8,
""label"": ""Buildings""
}
}
],
""output_name"": ""flooding_analysis.png"",
""title"": ""Flood Depth and Affected Buildings"",
""legend_loc"": ""best""
}
}
]",flooding_analysis.png,"flood_depth.tif
damage_assessment.geojson",,,,,,,,,,
8,Vector Spatial Analysis,"Your task is analyzing Toronto fire stations and their service coverage. The goal is visualize the current fire station coverage in Etobicoke by buffering to identify coverage gaps. Save the figure as ""Fire_Service_Analysis.png"".
Note: If any input GeoJSON file does not explicitly define a coordinate reference system (CRS), assume and assign it as WGS84 (EPSG:4326).","dataset/etobicoke.geojson: Polygon Geojson file for neighborhood region for Etobicoke.
dataset/fire_stations.geojson: Point Geojson file for current fire stations distribution as points.","etobicoke area style:{""facecolor"": ""lightgrey"", ""edgecolor"": ""black"", ""alpha"": 0.8, ""label"": ""Etobicoke Area""}
no service area style:{""facecolor"": ""blue"", ""edgecolor"": ""black"", ""alpha"": 0.5, ""label"": ""No Service Area""}",9,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/fire_stations.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/etobicoke.geojson""
}
},
{
""tool"": ""set_geojson_crs"",
""arguments"": {
""geojson_path"": ""dataset/fire_stations.geojson"",
""output_name"": ""fire_stations_crs.geojson"",
""crs"": ""EPSG:4326""
}
},
{
""tool"": ""set_geojson_crs"",
""arguments"": {
""geojson_path"": ""dataset/etobicoke.geojson"",
""output_name"": ""etobicoke_crs.geojson"",
""crs"": ""EPSG:4326""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""./output\\fire_stations_crs.geojson"",
""target_crs"": ""EPSG:32617"",
""output_name"": ""fire_stations_32617.geojson""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""./output\\etobicoke_crs.geojson"",
""target_crs"": ""EPSG:32617"",
""output_name"": ""etobicoke_32617.geojson""
}
},
{
""tool"": ""buffer_vector"",
""arguments"": {
""input_path"": ""./output\\fire_stations_32617.geojson"",
""distance"": 2500,
""output_name"": ""fire_buffer_2500.geojson""
}
},
{
""tool"": ""overlay_difference"",
""arguments"": {
""a_path"": ""./output\\etobicoke_32617.geojson"",
""b_path"": ""./output\\fire_buffer_2500.geojson"",
""output_name"": ""no_service.geojson""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""./output\\etobicoke_32617.geojson"",
""type"": ""vector"",
""style"": {
""facecolor"": ""lightgrey"",
""edgecolor"": ""black"",
""alpha"": 0.8,
""label"": ""Etobicoke Area""
}
},
{
""data"": ""./output\\no_service.geojson"",
""type"": ""vector"",
""style"": {
""facecolor"": ""blue"",
""edgecolor"": ""black"",
""alpha"": 0.5,
""label"": ""No Service Area""
}
}
],
""output_name"": ""Fire_Service_Analysis.png"",
""title"": ""No Fire Service Area in Etobicoke"",
""legend_loc"": ""best""
}
}
]",Fire_Service_Analysis.png,"etobicoke_32617.geojson
no_service.geojson",,,,,,,,,,
9,Vector Spatial Analysis,"Your task is to find the deforestation rate for the Brazilian state of Rondônia. The goal is to calculate the percentage of deforested area within a 5.5 km buffer zone around road layers in Rondônia. Optimization Requirement : Since the road network is complex and the dataset is large, please optimize the processing workflow to minimize execution time. Consider simplifying or merging geometric features before intersection analysis to avoid performance bottlenecks.
Save the percentage result in a CSV file named ""deforestation_rate.csv"".
Note: If any input GeoJSON file does not explicitly define a coordinate reference system (CRS), assume and assign it as WGS84 (EPSG:4326).","dataset/roads.geojson: A LineString GeoJSON file representing the current road network in the Brazilian state of Rondônia.
dataset/deforestedArea.geojson: A Polygon GeoJSON file containing the total deforested area in Rondônia as a single polygon feature.",,12,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/roads.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/deforestedArea.geojson""
}
},
{
""tool"": ""set_geojson_crs"",
""arguments"": {
""geojson_path"": ""dataset/roads.geojson"",
""output_name"": ""roads_crs.geojson"",
""crs"": ""EPSG:4326""
}
},
{
""tool"": ""set_geojson_crs"",
""arguments"": {
""geojson_path"": ""dataset/deforestedArea.geojson"",
""output_name"": ""deforest_crs.geojson"",
""crs"": ""EPSG:4326""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""./output\\roads_crs.geojson"",
""target_crs"": ""EPSG:32723"",
""output_name"": ""roads_32723.geojson""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""./output\\deforest_crs.geojson"",
""target_crs"": ""EPSG:32723"",
""output_name"": ""deforest_32723.geojson""
}
},
{
""tool"": ""buffer_vector"",
""arguments"": {
""input_path"": ""./output\\roads_32723.geojson"",
""distance"": 5500,
""output_name"": ""roads_buffer_5500.geojson""
}
},
{
""tool"": ""dissolve_polygons"",
""arguments"": {
""input_path"": ""./output\\roads_buffer_5500.geojson"",
""output_name"": ""roads_buffer_dissolved.geojson""
}
},
{
""tool"": ""compute_area_sum"",
""arguments"": {
""vector_path"": ""./output\\roads_buffer_dissolved.geojson""
}
},
{
""tool"": ""clip_by_attribute"",
""arguments"": {
""input_geojson_path"": ""./output\\roads_buffer_dissolved.geojson"",
""mask_geojson_path"": ""./output\\deforest_32723.geojson"",
""output_name"": ""deforested_clip.geojson"",
""attribute_field"": null,
""attribute_value"": null
}
},
{
""tool"": ""compute_area_sum"",
""arguments"": {
""vector_path"": ""./output\\deforested_clip.geojson""
}
},
{
""tool"": ""calculate_deforestation_percentage"",
""arguments"": {
""output_name"": ""deforestation_rate.csv"",
""buffer_area_csv"": null,
""deforest_area_csv"": null,
""buffer_area_field"": ""area_sum"",
""deforest_area_field"": ""area_sum"",
""buffer_area_value"": 179792795540.404,
""deforest_area_value"": 86176671033.82933
}
}
]",deforestation_rate.csv,deforestation_rate.csv,,,,,,,,,,
10,Vector Spatial Analysis,"Your task is to predict the effect of future proposed roads on deforestation in the Brazilian state of Rondônia. A planned road network will be added to Rondônia; create a 5.5 km buffer zone around these new roads. Visualize the potential impact by overlaying the planned roads, existing roads, deforested areas, protected forests, and the administrative boundary of Rondônia. Save the output map as ""predictedRiskyArea.png"".
Note: If any input GeoJSON file does not explicitly define a coordinate reference system (CRS), assume and assign it as WGS84 (EPSG:4326).","dataset/roads.geojson: A LineString GeoJSON file storing the current road network in the Brazilian state of Rondônia.
dataset/deforestedArea.geojson: A Polygon GeoJSON file containing the entire deforested area in Rondônia as a single polygon feature.
dataset/protectedForest.geojson: A Polygon GeoJSON file storing multiple legally designated natural reserve or protected forest areas in Rondônia.
dataset/planned_road.geojson: A LineString GeoJSON file containing a single proposed road from future urban planning documents.","protected forest style: {""color"": ""green"", ""label"": ""Protected Forest""}
roads style: {""color"": ""grey"", ""label"": ""Roads""}
risky area style: {""color"": ""red"", ""label"": ""Risky Area""}
proposed road style: {""color"": ""yellow"", ""label"": ""Proposed Road""}",14,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/roads.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/deforestedArea.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/protectedForest.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/planned_road.geojson""
}
},
{
""tool"": ""set_geojson_crs"",
""arguments"": {
""geojson_path"": ""dataset/roads.geojson"",
""output_name"": ""roads_crs.geojson"",
""crs"": ""EPSG:4326""
}
},
{
""tool"": ""set_geojson_crs"",
""arguments"": {
""geojson_path"": ""dataset/deforestedArea.geojson"",
""output_name"": ""deforest_crs.geojson"",
""crs"": ""EPSG:4326""
}
},
{
""tool"": ""set_geojson_crs"",
""arguments"": {
""geojson_path"": ""dataset/protectedForest.geojson"",
""output_name"": ""protected_crs.geojson"",
""crs"": ""EPSG:4326""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""./output\\roads_crs.geojson"",
""target_crs"": ""EPSG:32723"",
""output_name"": ""roads_32723.geojson""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""./output\\deforest_crs.geojson"",
""target_crs"": ""EPSG:32723"",
""output_name"": ""deforest_32723.geojson""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""./output\\protected_crs.geojson"",
""target_crs"": ""EPSG:32723"",
""output_name"": ""protected_32723.geojson""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""dataset/planned_road.geojson"",
""target_crs"": ""EPSG:32723"",
""output_name"": ""planned_32723.geojson""
}
},
{
""tool"": ""buffer_vector"",
""arguments"": {
""input_path"": ""./output\\planned_32723.geojson"",
""distance"": 5500,
""output_name"": ""planned_road_buffer_5500.geojson""
}
},
{
""tool"": ""overlay_difference"",
""arguments"": {
""a_path"": ""./output\\planned_road_buffer_5500.geojson"",
""b_path"": ""./output\\deforest_32723.geojson"",
""output_name"": ""risky_area.geojson""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""./output\\protected_32723.geojson"",
""type"": ""vector"",
""style"": {
""color"": ""green"",
""label"": ""Protected Forest""
}
},
{
""data"": ""./output\\roads_32723.geojson"",
""type"": ""vector"",
""style"": {
""color"": ""grey"",
""label"": ""Roads""
}
},
{
""data"": ""./output\\risky_area.geojson"",
""type"": ""vector"",
""style"": {
""color"": ""red"",
""label"": ""Risky Area""
}
},
{
""data"": ""./output\\planned_32723.geojson"",
""type"": ""vector"",
""style"": {
""color"": ""yellow"",
""label"": ""Proposed Road""
}
}
],
""output_name"": ""predictedRiskyArea.png"",
""title"": ""Risky Area created by Proposed Road in Protected Area"",
""legend_loc"": ""best""
}
}
]",predictedRiskyArea.png,"risky_area.geojson
protected_32723.geojson
planned_32723.geojson
roads_32723.geojson",,,,,,,,,,
11,Raster Spatial Analysis,"Your task is performing raster analysis on the elevation and environmental factors influencing coral and sponge distribution at Catalina Island. Update the data with the slope and aspect analysis results. Use the updated data to visualize the mean slope and aspect distribution of each Coral and Sponge species in Catalina Island as ""CoralandSponge.png"".","dataset/CatalinaBathymetry.tif: Raster files for storing Bathymetry data in the Catalina Island area.
dataset/CoralandSpongeCatalina.geojson: Geojson file for storing Coral and Sponge species distributions as point along with their attributes.If the projection is undefined, reproject it to EPSG:4326.","slope style:{""ylabel"": ""Mean Slope (degrees)"", ""title"": ""Mean Slope per Species"", ""color"": ""b"", ""rotation"": 45}
aspect style:{""ylabel"": ""Mean Aspect (degrees)"", ""title"": ""Mean Aspect per Species"", ""color"": ""g"", ""rotation"": 45}",10,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/CatalinaBathymetry.tif""
}
},
{
""tool"": ""compute_slope"",
""arguments"": {
""raster_path"": ""dataset/CatalinaBathymetry.tif"",
""band"": 1,
""output_name"": ""CatalinaBathymetry_band1_slope.tif""
}
},
{
""tool"": ""compute_aspect"",
""arguments"": {
""raster_path"": ""dataset/CatalinaBathymetry.tif"",
""band"": 1,
""output_name"": ""CatalinaBathymetry_band1_aspect.tif""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/CoralandSpongeCatalina.geojson""
}
},
{
""tool"": ""set_geojson_crs"",
""arguments"": {
""geojson_path"": ""dataset/CoralandSpongeCatalina.geojson"",
""crs"": ""EPSG:4326"",
""output_name"": ""setcrs_CoralandSpongeCatalina_4326.geojson""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""./output\\setcrs_CoralandSpongeCatalina_4326.geojson"",
""target_crs"": ""EPSG:3310"",
""output_name"": ""reproj_setcrs_CoralandSpongeCatalina_4326_3310.geojson""
}
},
{
""tool"": ""sample_slope_aspect_at_points"",
""arguments"": {
""points_geojson_path"": ""./output\\reproj_setcrs_CoralandSpongeCatalina_4326_3310.geojson"",
""slope_raster_path"": ""./output\\CatalinaBathymetry_band1_slope.tif"",
""aspect_raster_path"": ""./output\\CatalinaBathymetry_band1_aspect.tif"",
""group_field_name"": ""VernacularNameCategory"",
""output_name"": ""sample_reproj_setcrs_CoralandSpongeCatalina_4326_3310.csv""
}
},
{
""tool"": ""get_csv_info"",
""arguments"": {
""csv_path"": ""./output\\sample_reproj_setcrs_CoralandSpongeCatalina_4326_3310.csv""
}
},
{
""tool"": ""calculate_group_statistics"",
""arguments"": {
""csv_path"": ""./output\\sample_reproj_setcrs_CoralandSpongeCatalina_4326_3310.csv"",
""group_field_name"": ""VernacularNameCategory"",
""metric"": ""mean"",
""output_name"": ""group_statistics.csv""
}
},
{
""tool"": ""plot_group_metrics_layers"",
""arguments"": {
""analysis_results"": [
{
""VernacularNameCategory_"": ""black coral"",
""slope_mean"": 0.3269673849527652,
""aspect_mean"": 233.81641534658576
},
{
""VernacularNameCategory_"": ""demosponge"",
""slope_mean"": 0.3679851280054548,
""aspect_mean"": 255.86757299402257
},
{
""VernacularNameCategory_"": ""fish"",
""slope_mean"": 0.0975223337353645,
""aspect_mean"": 121.2392498907915
},
{
""VernacularNameCategory_"": ""glass sponge"",
""slope_mean"": 0.3486687039713497,
""aspect_mean"": 156.27366312949553
},
{
""VernacularNameCategory_"": ""gorgonian coral"",
""slope_mean"": 0.4213578264930752,
""aspect_mean"": 278.6544107677556
},
{
""VernacularNameCategory_"": ""lace coral"",
""slope_mean"": 0.6919144994482912,
""aspect_mean"": 242.20085074983797
},
{
""VernacularNameCategory_"": ""sea pen"",
""slope_mean"": 0.359573580546687,
""aspect_mean"": 191.31000264064627
},
{
""VernacularNameCategory_"": ""soft coral"",
""slope_mean"": 0.1624816978971162,
""aspect_mean"": 150.0
},
{
""VernacularNameCategory_"": ""sponge (unspecified)"",
""slope_mean"": 0.29971130866412,
""aspect_mean"": 210.371756987334
},
{
""VernacularNameCategory_"": ""stoloniferan coral"",
""slope_mean"": 0.1865470707416534,
""aspect_mean"": 316.0655822753906
},
{
""VernacularNameCategory_"": ""stony coral (branching)"",
""slope_mean"": 0.1107268181184063,
""aspect_mean"": 240.60196022365403
},
{
""VernacularNameCategory_"": ""stony coral (cup coral)"",
""slope_mean"": 0.4480728265146414,
""aspect_mean"": 221.99097834269205
},
{
""VernacularNameCategory_"": ""stony coral (unspecified)"",
""slope_mean"": 0.4539072378724813,
""aspect_mean"": 155.59400884628295
}
],
""group_field"": ""VernacularNameCategory"",
""layers"": [
{
""key"": ""slope_mean"",
""style"": {
""ylabel"": ""Mean Slope (degrees)"",
""title"": ""Mean Slope per Species"",
""color"": ""b"",
""rotation"": 45
}
},
{
""key"": ""aspect_mean"",
""style"": {
""ylabel"": ""Mean Aspect (degrees)"",
""title"": ""Mean Aspect per Species"",
""color"": ""g"",
""rotation"": 45
}
}
],
""output_name"": ""CoralandSponge.png""
}
}
]",CoralandSponge.png,group_statistics.csv,,,,,,,,,,
12,Raster Spatial Analysis,"Your task is evaluating mountain lion habitat suitability, and the first step is calculating terrain ruggedness using elevation data. Visualizing the ruggedness and save the outputs as ""ruggedness.png"".",dataset/Elevation.tif: Raster files for storing elevation data.,"ruggedness style:{""cmap"": ""viridis"", ""legend"": True, ""legend_label"": ""Ruggedness""}",3,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/Elevation.tif""
}
},
{
""tool"": ""compute_ruggedness"",
""arguments"": {
""input_path"": ""dataset/Elevation.tif"",
""window_size"": 3,
""output_name"": ""rugged_Elevation.tif""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\rugged_Elevation.tif"",
""output_name"": ""ruggedness.png"",
""cmap"": ""viridis"",
""title"": ""Ruggedness"",
""legend"": true,
""legend_label"": ""Ruggedness""
}
}
]",ruggedness.png,rugged_Elevation.tif,,,,,,,,,,
13,3D Modeling and Analysis,"Your task is using sea temperature and salinity data from the South Atlantic Ocean to calculate and analyze a vertical temperature-salinity profile within a specified range of latitude, longitude, and depth. Plot the profile and save the figure as 'ocean_profiles_vis.png'.",dataset/atlantic_profiles.nc: NetCDF profile data of partial Atlantic ocean with temperature and salinity data.,"temperature style:{""x_col"": <placeholder>, ""y_col"": ""depth"", ""label"": ""Temperature"", ""color"": (0.3, 0.4, 0.5), ""linewidth"": 2, ""axis"": ""primary""}
salinity style:{""x_col"": <placeholder>, ""y_col"": ""depth"", ""label"": ""Salinity"", ""color"": (0.6, 0.1, 0.15), ""linewidth"": 2, ""axis"": ""secondary""}",3,"[
{
""tool"": ""get_nc_info"",
""arguments"": {
""nc_path"": ""dataset/atlantic_profiles.nc""
}
},
{
""tool"": ""extract_netcdf_profile"",
""arguments"": {
""nc_path"": ""dataset/atlantic_profiles.nc"",
""output_name"": ""profile_atlantic_profiles.csv"",
""variables"": [
""salinity"",
""theta""
],
""lon_value"": 330.5,
""lat_min"": -10.0,
""lat_max"": -9.0,
""depth_max"": 1000.0
}
},
{
""tool"": ""create_chart"",
""arguments"": {
""layers"": [
{
""data"": ""output/profile_atlantic_profiles.csv"",
""style"": {
""x_col"": ""theta"",
""y_col"": ""depth"",
""label"": ""Temperature"",
""color"": [
0.3,
0.4,
0.5
],
""linewidth"": 2,
""axis"": ""primary""
}
},
{
""data"": ""output/profile_atlantic_profiles.csv"",
""style"": {
""x_col"": ""salinity"",
""y_col"": ""depth"",
""label"": ""Salinity"",
""color"": [
0.6,
0.1,
0.15
],
""linewidth"": 2,
""axis"": ""secondary""
}
}
],
""output_name"": ""ocean_profiles_vis.png"",
""title"": ""Atlantic Profiles"",
""xlabel"": ""Potential Temperature / K"",
""ylabel"": ""Depth / m"",
""invert_y"": true,
""secondary_xlabel"": ""Salinity / PSU""
}
}
]",ocean_profiles_vis.png,profile_atlantic_profiles.csv,,,,,,,,,,
14,Geostatistical Analysis,Your task is using 240 years of annual average surface temperature data from North America to calculate and plot the number of occurrences where the temperature exceeds 280K for five consecutive years. Save the figure as 'temperature_statistic_vis.png'.,dataset/E1_north_america.nc: NetCDF Temperature data in North America for a 240 year period.,"warm spells style: {""cmap"": ""RdYlBu_r"", ""title"":""Warm Spells"", ""legend"": True, ""legend_label"": ""Warm spell count""}",3,"[
{
""tool"": ""get_nc_info"",
""arguments"": {
""nc_path"": ""dataset/E1_north_america.nc""
}
},
{
""tool"": ""count_netcdf_spells"",
""arguments"": {
""nc_path"": ""dataset/E1_north_america.nc"",
""var_name"": ""air_temperature"",
""output_name"": ""spell_E1_north_america.tif"",
""threshold"": 280.0,
""spell_length"": 5
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""output/spell_E1_north_america.tif"",
""output_name"": ""temperature_statistic_vis.png"",
""bands"": null,
""cmap"": ""RdYlBu_r"",
""stretch"": true,
""title"": ""Warm Spells"",
""legend"": true,
""legend_label"": ""Warm spell count""
}
}
]",temperature_statistic_vis.png,spell_E1_north_america.tif,,,,,,,,,,
15,Raster Spatial Analysis,Your task is analyzing the distribution of Total Electron Content. Load Total Electron Content (TEC) data from a space weather NetCDF file and visualize it. Save the figure as 'TEC_vis.png'.,dataset/space_weather.nc: Total ELectron Content data in netCDF format.,"total electron content style: {""cmap"": ""viridis"",""title"": ""Total Electron Content"", ""legend"": True, ""legend_label"": ""TEC""}",3,"[
{
""tool"": ""get_nc_info"",
""arguments"": {
""nc_path"": ""dataset/space_weather.nc""
}
},
{
""tool"": ""export_nc_var"",
""arguments"": {
""nc_path"": ""dataset/space_weather.nc"",
""var_name"": ""TEC"",
""output_name"": ""nc_space_weather_TEC.tif""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""output/nc_space_weather_TEC.tif"",
""output_name"": ""TEC_vis.png"",
""cmap"": ""viridis"",
""title"": ""Total Electron Content"",
""legend"": true,
""legend_label"": ""TEC""
}
}
]",TEC_vis.png,nc_space_weather_TEC.tif,,,,,,,,,,
16,Geostatistical Analysis,"Load North America climate data in NetCDF file and extract temperature data along the time series, then perform a quadratic polynomial fit analysis on the temperature data, finally plot the curve as 'polynomial_fit_pred.png'.",dataset/A1B_north_america.nc: NetCDF data of climate data and temperature data in North America.,"fitted style: {""x_col"": ""Year"", ""y_col"": ""Fitted_Temperature"", ""label"": ""Fitted"", ""color"": ""red"", ""linewidth"": 2}",4,"[
{
""tool"": ""get_nc_info"",
""arguments"": {
""nc_path"": ""dataset/A1B_north_america.nc""
}
},
{
""tool"": ""fit_netcdf_timeseries"",
""arguments"": {
""nc_path"": ""dataset/A1B_north_america.nc"",
""output_name"": ""polyfit_A1B_north_america.csv"",
""degree"": 2,
""var_constraint"": null
}
},
{
""tool"": ""get_csv_info"",
""arguments"": {
""csv_path"": ""output/polyfit_A1B_north_america.csv""
}
},
{
""tool"": ""create_chart"",
""arguments"": {
""layers"": [
{
""data"": ""output/polyfit_A1B_north_america.csv"",
""style"": {
""x_col"": ""Year"",
""y_col"": ""Fitted_Temperature"",
""label"": ""Fitted"",
""color"": ""red"",
""linewidth"": 2
}
}
],
""output_name"": ""polynomial_fit_pred.png"",
""title"": ""Polynomial Fit"",
""xlabel"": ""Year"",
""ylabel"": ""Temperature""
}
}
]",polynomial_fit_pred.png,polyfit_A1B_north_america.csv,,,,,,,,,,
17,Geostatistical Analysis,Your task is using data on the locations and related information of fatal car crashes in New York City to analyze fatal car crash distribution in 2016. You will use the car crash data along with the GeoJSON data of NYC administrative regions to plot the geographical distribution of fatal crashes in 2016 on a map. Save the figure as 'collisions_map_vis.png'.,"dataset/nyc_boroughs.geojson:Multipolygon Geojson file storing boroughs in the NewYork city.
dataset/fatal_collisions.geojson: Point Geojson file storing fatal collisions in each boroughs along with the number of persons killed in 2016.","boroughs style:{""facecolor"": ""none"", ""edgecolor"": ""black"", ""linewidth"": 1, ""label"": ""Boroughs""}
fatal collisions style:{""color"": ""red"", ""markersize"": 5, ""label"": ""Fatal Collisions""}",3,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/nyc_boroughs.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/fatal_collisions.geojson""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""dataset/nyc_boroughs.geojson"",
""type"": ""vector"",
""style"": {
""facecolor"": ""none"",
""edgecolor"": ""black"",
""linewidth"": 1,
""label"": ""Boroughs""
}
},
{
""data"": ""dataset/fatal_collisions.geojson"",
""type"": ""vector"",
""style"": {
""color"": ""red"",
""markersize"": 5,
""label"": ""Fatal Collisions""
}
}
],
""output_name"": ""collisions_map_vis.png"",
""title"": ""Fatal Crashes in New York City, 2016""
}
}
]",collisions_map_vis.png,"nyc_boroughs.geojson
fatal_collisions.geojson",,,,,,,,,,
18,Vector Spatial Analysis,Your task is using the distribution data data about San Francisco street trees and shapefiles of administrative regions to analyze the tree species NULL percentage in different regions. Visualize the results in a quadtree format map. Save the figure as 'trees_count_vis.png'.,"dataset/street_trees_sample.geojson: Point Geojson file storing tree sample locations along with their species information.
dataset/sfo_boroughs.geojson: One row of polygon Geojson file storing boroughs in the San Francisco.","tree density style: {""column"": ""<placeholder>"",""cmap"": ""Greens"",""legend"": True,""scheme"": ""Quantiles"",""k"": 5,""edgecolor"": ""white"",""linewidth"": 1, ""label"": ""Tree Density""}
boroughs style: {""facecolor"": ""none"",""edgecolor"": ""gray"",""linewidth"": 1,""zorder"": 2, ""label"": ""Boroughs""}",5,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/street_trees_sample.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/sfo_boroughs.geojson""
}
},
{
""tool"": ""generate_quadtree_cells"",
""arguments"": {
""points_geojson_path"": ""dataset/street_trees_sample.geojson"",
""polygons_geojson_path"": ""dataset/sfo_boroughs.geojson"",
""output_name"": ""quadtree_street_trees_sample.geojson"",
""value_field"": ""Species"",
""indicator_field"": ""nullity"",
""nmax"": 100,
""max_depth"": 10,
""target_cells"": null,
""min_quadrant_points"": 0
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""output/quadtree_street_trees_sample.geojson""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""output/quadtree_street_trees_sample.geojson"",
""type"": ""vector"",
""style"": {
""column"": ""non_null_ratio"",
""cmap"": ""Greens"",
""legend"": true,
""scheme"": ""Quantiles"",
""k"": 5,
""edgecolor"": ""white"",
""linewidth"": 1,
""label"": ""Tree Density""
}
},
{
""data"": ""dataset/sfo_boroughs.geojson"",
""type"": ""vector"",
""style"": {
""facecolor"": ""none"",
""edgecolor"": ""gray"",
""linewidth"": 1,
""zorder"": 2,
""label"": ""Boroughs""
}
}
],
""output_name"": ""trees_count_vis.png"",
""title"": ""Quadtree Cells""
}
}
]",trees_count_vis.png,"quadtree_street_trees_sample.geojson
sfo_boroughs.geojson",,,,,,,,,,
19,Geostatistical Analysis,"Your task is to model water quality using spatial interpolation techniques. The analysis should focus on understanding spatial patterns of water quality by using point sample data and interpolating these values across a broader area, including unsampled locations. Your goal is to load the water quality sample data and apply Kernel Density Estimation (KDE) as the interpolation method to predict water quality across the region. The final output should be a map showing the KDE-interpolated water quality surface, saved as ""interploated_water_quality.png"".","dataset/DissolvedO2.geojson: Point Geojson file storing sampled O2 data in different area of the given bay area.
dataset/Bay.geojson: One row of Multipolygon Geojson file storing the shape of the Bay area.","dissolved O2 style:{""cmap"": ""Spectral"", ""label"": ""Dissolved O2"", ""colorbar"": True}
bay boundary style:{""facecolor"": ""none"", ""edgecolor"": ""white"", ""linewidth"": 2, ""label"": ""Bay Boundary""}",4,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/DissolvedO2.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/Bay.geojson""
}
},
{
""tool"": ""kde_interpolate"",
""arguments"": {
""points_path"": ""dataset/DissolvedO2.geojson"",
""polygons_path"": ""dataset/Bay.geojson"",
""output_name"": ""kde_DissolvedO2.tif"",
""value_field"": ""MeasureValue""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""output/kde_DissolvedO2.tif"",
""type"": ""raster"",
""style"": {
""cmap"": ""Spectral"",
""label"": ""Dissolved O2"",
""colorbar"": true
}
},
{
""data"": ""dataset/Bay.geojson"",
""type"": ""vector"",
""style"": {
""facecolor"": ""none"",
""edgecolor"": ""white"",
""linewidth"": 2,
""label"": ""Bay Boundary""
}
}
],
""output_name"": ""interploated_water_quality.png"",
""title"": ""Interpolated Data""
}
}
]",interploated_water_quality.png,"kde_DissolvedO2.tif
Bay.geojson",,,,,,,,,,
20,Raster Spatial Analysis,"Your task is performing a random forest prospectivity analysis for tin-tungsten deposits in Tasmania. The analysis should focus on training and applying a model using tools on some mineral ocurrence point data and geo-related evidence raster layers. Your goal is to load and inspect the data, build a random forest classifier model to identify the probability proximal of the area. Save the final predicted graph as ""mineral_prospectivity.png"".","dataset/sn_w_minoccs.gpkg: gpkg file storing point data of mineral ocurrence, similar to point Geojson file.
Several GeoTIFF files with geo-related evidence raster layers:
dataset/tasgrav_IR_1VD.tif
dataset/tasgrav_IR.tif
dataset/tasmag_TMI_1VD.tif
dataset/tasmag_TMI.tif
dataset/tasrad_K_pct.tif
dataset/tasrad_Th_ppm.tif
dataset/tasrad_U_ppm.tif","style: {""cmap"": ""viridis"", ""vmin"": 0.0, ""vmax"": 1.0, ""label"": ""Probability"", ""colorbar"": True}",17,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/sn_w_minoccs.gpkg""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasgrav_IR_1VD.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasgrav_IR.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasmag_TMI_1VD.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasmag_TMI.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasrad_K_pct.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasrad_Th_ppm.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasrad_U_ppm.tif""
}
},
{
""tool"": ""extract_raster_variables"",
""arguments"": {
""raster_paths"": [
""dataset/tasgrav_IR_1VD.tif"",
""dataset/tasgrav_IR.tif"",
""dataset/tasmag_TMI_1VD.tif"",
""dataset/tasmag_TMI.tif"",
""dataset/tasrad_K_pct.tif"",
""dataset/tasrad_Th_ppm.tif"",
""dataset/tasrad_U_ppm.tif""
],
""output_name"": ""data"",
""band"": 1
}
},
{
""tool"": ""rasterize_vector_labels"",
""arguments"": {
""point_path"": ""dataset/sn_w_minoccs.gpkg"",
""metadata_path"": ""./output\\data_metadata.pkl"",
""output_name"": ""data_labels.npy"",
""buffer_meters"": 1000.0,
""label_value"": 1,
""fill_value"": 0
}
},
{
""tool"": ""undersample_raster_data"",
""arguments"": {
""data_path"": ""./output\\data.npy"",
""labels_path"": ""./output\\data_labels.npy"",
""metadata_path"": ""./output\\data_metadata.pkl"",
""output_name"": ""data"",
""random_state"": 42
}
},
{
""tool"": ""split_train_test"",
""arguments"": {
""csv_path"": ""./output\\data_undersampled.csv"",
""output_name"": ""data"",
""label_column"": ""label"",
""test_size"": 0.3,
""random_state"": 42,
""stratify"": true,
""index"": false
}
},
{
""tool"": ""train_random_forest_model"",
""arguments"": {
""X_train_path"": ""./output\\data_X_train.csv"",
""output_name"": ""data"",
""y_train_path"": ""./output\\data_y_train.csv"",
""label_column"": ""label"",
""n_estimators"": 100,
""max_depth"": null,
""random_state"": 42
}
},
{
""tool"": ""generate_model_predictions"",
""arguments"": {
""model_path"": ""./output\\data.joblib"",
""X_test_path"": ""./output\\data_X_test.csv"",
""output_name"": ""data"",
""output_type"": ""proba"",
""pred_column"": ""y_pred""
}
},
{
""tool"": ""evaluate_classification_auc"",
""arguments"": {
""labels_path"": ""./output\\data_y_test.csv"",
""predictions_path"": ""./output\\data_y_proba.csv"",
""output_name"": ""data"",
""label_column"": ""label"",
""class_index"": 1
}
},
{
""tool"": ""generate_probability_raster"",
""arguments"": {
""model_path"": ""./output\\data.joblib"",
""data_path"": ""./output\\data.npy"",
""metadata_path"": ""./output\\data_metadata.pkl"",
""output_name"": ""data""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""./output\\data_proba.tif"",
""type"": ""raster"",
""style"": {
""cmap"": ""viridis"",
""vmin"": 0.0,
""vmax"": 1.0,
""label"": ""Probability"",
""colorbar"": true
}
}
],
""output_name"": ""mineral_prospectivity.png"",
""title"": ""Probability Map"",
""legend_loc"": ""best""
}
}
]",mineral_prospectivity.png,data_proba.tif,,,,,,,,,,
21,Raster Spatial Analysis,"Your task is standardizing land cover datasets for consistency in analysis for mountain lion habitat. Reclassify this categorical dataset to a common scale using geospatial tools, and visualize the reclassified results, saving them as ""reclassified_landCover.png"".
The land cover data are reclassified according to the following mapping: 11 (Open Water) → 10, 21 (Developed, Open Space) → 8, 22 (Developed, Low Intensity) → 7, 23 (Developed, Medium Intensity) → 8, 24 (Developed, High Intensity) → 9, 31 (Barren Land) → 6, 41 (Deciduous Forest) → 2, 42 (Evergreen Forest) → 1, 43 (Mixed Forest) → 2, 52 (Shrub/Scrub) → 3, 71 (Grassland/Herbaceous) → 3, 72 (Sedge/Herbaceous) → 3, 81 (Hay/Pasture) → 4, 82 (Cultivated Crops) → 6, 90 (Woody Wetlands) → 4, 95 (Emergent Herbaceous Wetlands) → 4, and 255 (NoData or undefined) → 10.
These reclassification schemes align the original categorical values to a unified scale suitable for mountain lion habitat suitability modeling.",dataset/landCover.tif: GeoTiff file storing the landcover types defined by National Land Cover Database,"style:{""cmap"": ""tab20"", ""legend"": True}",3,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/landCover.tif""
}
},
{
""tool"": ""reclassify_raster"",
""arguments"": {
""raster_path"": ""dataset/landCover.tif"",
""output_name"": ""landCover_reclassified.tif"",
""reclass_dict"": {
""11"": 10,
""21"": 8,
""22"": 7,
""23"": 8,
""24"": 9,
""31"": 6,
""41"": 2,
""42"": 1,
""43"": 2,
""52"": 3,
""71"": 3,
""72"": 3,
""81"": 4,
""82"": 6,
""90"": 4,
""95"": 4,
""255"": 10
}
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\landCover_reclassified.tif"",
""output_name"": ""reclassified_landCover.png"",
""cmap"": ""tab20"",
""legend"": true,
""title"": ""Landcover (reclassified)""
}
}
]",reclassified_landCover.png,landCover_reclassified.tif,,,,,,,,,,
22,Raster Spatial Analysis,"Your task is integrating ruggedness, road distance, land cover, and protected status into a single cost surface. Assign weights to each criterion—specifically, weight 1.0 for road distance, 1.25 for ruggedness, 1.0 for protected status, and 1.25 for land cover—to create a comprehensive habitat suitability map. Save the composite cost surface as ""mountainLionCorridor.png"".","dataset/landCover_reclassified.tif: GeoTiff file storing the reclassified and normalized landcover types.
dataset/Protected_Status_reclassified.tif: GeoTiff file storing the reclassified Protected status in 5 levels as well as the null data.
dataset/distance.tif: GeoTiff file storing the distance of each location to the nearest mountain lion habitats.
dataset/ruggedness.tif: GeoTiff file storing the calculated ruggedness of the area.","style: {""cmap"": ""viridis"", ""legend"": True}",6,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/distance.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/ruggedness.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/landCover_reclassified.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/Protected_Status_reclassified.tif""
}
},
{
""tool"": ""overlay_weighted_rasters"",
""arguments"": {
""raster_paths"": [
""dataset/distance.tif"",
""dataset/ruggedness.tif"",
""dataset/Protected_Status_reclassified.tif"",
""dataset/landCover_reclassified.tif""
],
""bands"": [
1,
1,
1,
1
],
""weights"": [
1.0,
1.25,
1.0,
1.25
],
""output_name"": ""weighted_overlay.tif""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\weighted_overlay.tif"",
""output_name"": ""mountainLionCorridor.png"",
""cmap"": ""viridis"",
""legend"": true,
""title"": ""Raster Visualization with Viridis Colormap""
}
}
]",mountainLionCorridor.png,weighted_overlay.tif,,,,,,,,,,
23,Spatial Data Management,"Your task is to process a de-identified point-level dataset that includes all variables of interest for each child as well as their approximate locations. You need to load the point data, ensure a consistent Coordinate Reference System (CRS), round the coordinates to two decimal places, save the result as ""rounded_points_plot.png"", and visualize it.","dataset/High_Blood_Level_Results.shp: This shapefile contains point data representing incidents of elevated blood lead levels in children within the study area. Each point corresponds to a specific geographic location where a blood test was conducted, and the results indicated a high concentration of lead.
dataset/Sacramento_ZIP_Codes.shp: This shapefile contains polygon data representing the boundaries of ZIP codes within Sacramento. Each polygon defines the geographic extent of a specific ZIP code.","style: {""color"": ""blue"",""markersize"": 5,""alpha"": 1.0}",6,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/High_Blood_Level_Results.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/Sacramento_ZIP_Codes.shp""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""dataset/High_Blood_Level_Results.shp"",
""target_crs"": ""EPSG:4326"",
""output_name"": ""reproj_High_Blood_Level_Results_4326.geojson""
}
},
{
""tool"": ""add_vector_coords"",
""arguments"": {
""vector_path"": ""./output\\reproj_High_Blood_Level_Results_4326.geojson"",
""output_name"": ""coords_reproj_High_Blood_Level_Results_4326.geojson""
}
},
{
""tool"": ""round_coordinate_values"",
""arguments"": {
""points_path"": ""./output\\coords_reproj_High_Blood_Level_Results_4326.geojson"",
""output_name"": ""rounded_coords_reproj_High_Blood_Level_Results_4326.geojson"",
""lat_col"": ""Latitude"",
""lon_col"": ""Longitude"",
""decimals"": 2
}
},
{
""tool"": ""visualize_vector"",
""arguments"": {
""input_path"": ""./output\\rounded_coords_reproj_High_Blood_Level_Results_4326.geojson"",
""output_name"": ""rounded_points_plot.png"",
""style"": {
""color"": ""blue"",
""markersize"": 5,
""alpha"": 1.0
},
""title"": ""Rounded Points"",
""legend"": true,
""legend_kwds"": null
}
}
]",rounded_points_plot.png,rounded_coords_reproj_High_Blood_Level_Results_4326.geojson,,,,,,,,,,
24,Geostatistical Analysis,"Your task is to analyze COVID-19 risk in HKG. First, use similarity search to map transmission risk from hkg_constituency.shp and target_risk.shp. Second, join transmission risk map. Then do the same search and join functions for the other three risk map (susceptibility risk, healthcare resource scarcity risk, and exposure risk). Finally, use multivariate clustering to map the COVID-19 risk profiles of HKG as ""hkg_covid_risk_profiles.png"".","dataset/hkg_constituency.shp: This shapefile contains data for 431 Hong Kong constituencies. The data includes demographic and spatial information that can be used to assess the risk of disease transmission, COVID- 19 susceptibility, and healthcare resource scarcity.
dataset/target_risk.shp: The attributes for the target_risk shapefile are similar to those for the hkg_constituency.shp. The values in the target_risk, however, are the worst-case values found throughout Hong Kong. They include the densest population value, the largest number of seniors per 1,000 people, the highest index for tobacco, the smallest purchasing power value, and so on. These worst-case values will be used as the target against which all other constituencies will be ranked in order to determine risk.","style: {""column"": <placeholder>,""cmap"": ""tab20"", ""edgecolor"": ""black"", ""linewidth"": 0.2, ""categorical"": True}",14,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/hkg_constituency.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/target_risk.shp""
}
},
{
""tool"": ""search_similarity"",
""arguments"": {
""candidates_path"": ""dataset/hkg_constituency.shp"",
""targets_path"": ""dataset/target_risk.shp"",
""attrs"": [
""PopDensity"",
""SpInterIdx""
],
""output_name"": ""similarity_trans.geojson"",
""id_field"": ""ID""
}
},
{
""tool"": ""join_similarity_attributes"",
""arguments"": {
""candidates_path"": ""dataset/hkg_constituency.shp"",
""simrank_path"": ""./output\\similarity_trans.geojson"",
""output_name"": ""candidates_trans.geojson"",
""id_field"": ""ID"",
""label"": ""trans""
}
},
{
""tool"": ""search_similarity"",
""arguments"": {
""candidates_path"": ""dataset/hkg_constituency.shp"",
""targets_path"": ""dataset/target_risk.shp"",
""attrs"": [
""PopDensity"",
""Seniors60t"",
""spending_c"",
""keyfacts_p""
],
""output_name"": ""similarity_susc.geojson"",
""id_field"": ""ID""
}
},
{
""tool"": ""join_similarity_attributes"",
""arguments"": {
""candidates_path"": ""./output\\candidates_trans.geojson"",
""simrank_path"": ""./output\\similarity_susc.geojson"",
""output_name"": ""candidates_susc.geojson"",
""id_field"": ""ID"",
""label"": ""susc""
}
},
{
""tool"": ""search_similarity"",
""arguments"": {
""candidates_path"": ""dataset/hkg_constituency.shp"",
""targets_path"": ""dataset/target_risk.shp"",
""attrs"": [
""Seniors60t"",
""SUM_Beds""
],
""output_name"": ""similarity_insr.geojson"",
""id_field"": ""ID""
}
},
{
""tool"": ""join_similarity_attributes"",
""arguments"": {
""candidates_path"": ""./output\\candidates_susc.geojson"",
""simrank_path"": ""./output\\similarity_insr.geojson"",
""output_name"": ""candidates_insr.geojson"",
""id_field"": ""ID"",
""label"": ""insr""
}
},
{
""tool"": ""search_similarity"",
""arguments"": {
""candidates_path"": ""dataset/hkg_constituency.shp"",
""targets_path"": ""dataset/target_risk.shp"",
""attrs"": [
""RelativeCa""
],
""output_name"": ""similarity_expo.geojson"",
""id_field"": ""ID""
}
},
{
""tool"": ""join_similarity_attributes"",
""arguments"": {
""candidates_path"": ""./output\\candidates_insr.geojson"",
""simrank_path"": ""./output\\similarity_expo.geojson"",
""output_name"": ""candidates_expo.geojson"",
""id_field"": ""ID"",
""label"": ""expo""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""./output\\candidates_expo.geojson""
}
},
{
""tool"": ""multivariate_clusters"",
""arguments"": {
""candidates_path"": ""./output\\candidates_expo.geojson"",
""risk_fields"": [
""trans_risk"",
""susc_risk"",
""insr_risk"",
""expo_risk""
],
""output_name"": ""risk_profiles_candidates_expo.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""./output\\risk_profiles_candidates_expo.geojson""
}
},
{
""tool"": ""visualize_vector"",
""arguments"": {
""input_path"": ""./output\\risk_profiles_candidates_expo.geojson"",
""output_name"": ""hkg_covid_risk_profiles.png"",
""style"": {
""column"": ""CLUSTER_ID"",
""cmap"": ""tab20"",
""edgecolor"": ""black"",
""linewidth"": 0.2,
""categorical"": true
},
""title"": ""COVID-19 Risk Profiles (HKG)"",
""legend"": true,
""legend_kwds"": null
}
}
]",hkg_covid_risk_profiles.png,risk_profiles_candidates_expo.geojson,,,,,,,,,,
25,Raster Spatial Analysis,"Your task is to identify groundwater-vulnerable areas that require protection. First, project mc_soils.shp, mc_boundary.shp, and mc_land_cover.tif to the Lambert Conformal Conic projection (EPSG:32126). Next, using mc_boundary.shp as the processing extent and mc_land_cover.tif as both the cell size and snap raster, extract drainage conditions and water depth from mc_soils.shp as raster layers. Use these two rasters to perform suitability modeling and calculate the vulnerable_areas. Finally, visualize the resulting vulnerable areas and save the map as ""vulnerable_areas.png"".
Suitability modeling is an analytical process used to identify the optimal location or suitability of geographic areas. It generally consists of three steps: data preparation, data reclassification, and weighted overlay.
For preparation: Polygon data needs to be converted into Raster before suitability modeling.
For reclassification: The reclassification of drainage_conditions is [[1, 3], [2, 1], [3, 4], [4, 5], [5, 2]]. The reclassification of water_depth is [[0, 10, 5], [10, 33, 4], [33, 61, 3], [61, 80, 2], [80, 92, 1]].
For weighted overlay: vulnerable_areas = drainage_conditions * 5 + water_depth * 4","dataset/mc_soils.shp: In this shapefile, three fields are important in groundwater analysis: Drainage Class – Dominant Conditions, Hydrologic Group – Dominant Conditions, and Water Table Depth – Annual – Minimum.
When converting the drainage class to raster, the following mapping is used to assign numeric values:{""well drained"": 1, ""moderately well drained"": 2, ""somewhat poorly drained"": 3, ""poorly drained"": 4, ""very poorly drained"": 5}
dataset/mc_boundary.shp: This shapefile contains the polygon region of study area.
dataset/mc_land_cover.tif: Raster file showing the land cover of study area, one band.","style: {""cmap"": ""inferno"", ""legend"": True}",12,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/mc_land_cover.tif""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/mc_soils.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/mc_boundary.shp""
}
},
{
""tool"": ""reproject_raster"",
""arguments"": {
""raster_path"": ""dataset/mc_land_cover.tif"",
""target_crs"": ""EPSG:32126"",
""output_name"": ""reproj_mc_land_cover_32126.tif"",
""band"": null
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""dataset/mc_soils.shp"",
""target_crs"": ""EPSG:32126"",
""output_name"": ""reproj_mc_soils_32126.shp""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""dataset/mc_boundary.shp"",
""target_crs"": ""EPSG:32126"",
""output_name"": ""reproj_mc_boundary_32126.shp""
}
},
{
""tool"": ""rasterize_polygons"",
""arguments"": {
""polygons_path"": ""output/reproj_mc_soils_32126.shp"",
""reference_raster_path"": ""output/reproj_mc_land_cover_32126.tif"",
""value_field"": ""drclassdcd"",
""output_name"": ""poly2ras_reproj_mc_soils_32126_drclassdcd.tif"",
""boundary_path"": ""output/reproj_mc_boundary_32126.shp"",
""value_map"": {
""well drained"": 1,
""moderately well drained"": 2,
""somewhat poorly drained"": 3,
""poorly drained"": 4,
""very poorly drained"": 5
},
""fill_value"": 0
}
},
{
""tool"": ""reclassify_raster_values"",
""arguments"": {
""raster_path"": ""output/poly2ras_reproj_mc_soils_32126_drclassdcd.tif"",
""output_name"": ""poly2ras_reproj_mc_soils_32126_drclassdcd_reclass.tif"",
""ranges"": [
[
1,
1,
3
],
[
2,
2,
1
],
[
3,
3,
4
],
[
4,
4,
5
],
[
5,
5,
2
]
],
""band"": 1
}
},
{
""tool"": ""rasterize_polygons"",
""arguments"": {
""polygons_path"": ""output/reproj_mc_soils_32126.shp"",
""reference_raster_path"": ""output/reproj_mc_land_cover_32126.tif"",
""value_field"": ""wdepannmin"",
""output_name"": ""poly2ras_reproj_mc_soils_32126_wdepannmin.tif"",
""boundary_path"": ""output/reproj_mc_boundary_32126.shp"",
""value_map"": null,
""fill_value"": 0
}
},
{
""tool"": ""reclassify_raster_values"",
""arguments"": {
""raster_path"": ""output/poly2ras_reproj_mc_soils_32126_wdepannmin.tif"",
""output_name"": ""poly2ras_reproj_mc_soils_32126_wdepannmin_reclass.tif"",
""ranges"": [
[
0,
10,
5
],
[
10,
33,
4
],
[
33,
61,
3
],
[
61,
80,
2
],
[
80,
92,
1
]
],
""band"": 1
}
},
{
""tool"": ""weighted_sum_rasters"",
""arguments"": {
""raster_paths"": [
""output/poly2ras_reproj_mc_soils_32126_drclassdcd_reclass.tif"",
""output/poly2ras_reproj_mc_soils_32126_wdepannmin_reclass.tif""
],
""output_name"": ""wsum_poly2ras_reproj_mc_soils_32126_drclassdcd_reclass_poly2ras_reproj_mc_soils_32126_wdepannmin_reclass.tif"",
""bands"": [
1,
1
],
""weights"": [
5,
4
]
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""output/wsum_poly2ras_reproj_mc_soils_32126_drclassdcd_reclass_poly2ras_reproj_mc_soils_32126_wdepannmin_reclass.tif"",
""output_name"": ""vulnerable_areas.png"",
""bands"": null,
""cmap"": ""inferno"",
""stretch"": true,
""title"": ""Groundwater Vulnerable Areas"",
""legend"": true,
""legend_label"": null
}
}
]",vulnerable_areas.png,wsum_poly2ras_reproj_mc_soils_32126_drclassdcd_reclass_poly2ras_reproj_mc_soils_32126_wdepannmin_reclass.tif,,,,,,,,,,
26,Raster Spatial Analysis,"Your task is to identify those high risk zones of groundwater vulnerable areas that need protection. First, use vulnerable_areas and land_cover to perform suitability modeling to calculate risk_zones. Next, filter out high_risk_zones, and then undeveloped_areas based on risk_zones, and visualize the result as 'undeveloped_areas.png'.
The reclassification of vulnerable_areas is standardization to [1, 10].
The reclassification of land_cover is [[11, 1],[21, 6],[22, 7],[23, 8],[24, 10],[31, 4],[41, 3],[42, 1],[43, 3],[52, 3],[71, 2],[81, 5],[82, 9],[90, 1],[95, 1]].
risk_zones = vulnerable_areas * 8 + land_cover * 10
Undeveloped areas in the high risk zones (risk_zones > 100) need more protection. Seek for the areas in land_cover masking high risk zone, use the following rule: [31, 41, 43, 52, 71, 81, 90, 95]","dataset/vulnerable_areas.tif: Raster file showing the whole groundwater vulnerable areas of study area, one band.
dataset/land_cover.tif: Raster file showing the land cover of study area, one band.","water risk style: {""cmap"": ""viridis"", ""alpha"": 0.9, ""label"": ""Risk"", ""colorbar"": True}
undeveloped areas style: {""cmap"": ""Greys"", ""alpha"": 0.5, ""label"": ""Undeveloped""}",10,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/vulnerable_areas.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/land_cover.tif""
}
},
{
""tool"": ""reproject_raster"",
""arguments"": {
""raster_path"": ""dataset/vulnerable_areas.tif"",
""target_crs"": ""EPSG:32126"",
""output_name"": ""reproj_vul_32126.tif"",
""band"": null
}
},
{
""tool"": ""align_raster_to_reference"",
""arguments"": {
""raster_path"": ""output/reproj_vul_32126.tif"",
""reference_raster_path"": ""dataset/land_cover.tif"",
""output_name"": ""aligned_vul_32126.tif"",
""resampling"": ""bilinear"",
""band"": null
}
},
{
""tool"": ""normalize_raster_values"",
""arguments"": {
""input_raster_path"": ""output/aligned_vul_32126.tif"",
""output_name"": ""norm_vul.tif"",
""lower"": 1.0,
""upper"": 10.0
}
},
{
""tool"": ""reclassify_raster"",
""arguments"": {
""raster_path"": ""dataset/land_cover.tif"",
""output_name"": ""reclass_lc.tif"",
""reclass_dict"": {
""11"": 1,
""21"": 6,
""22"": 7,
""23"": 8,
""24"": 10,
""31"": 4,
""41"": 3,
""42"": 1,
""43"": 3,
""52"": 3,
""71"": 2,
""81"": 5,
""82"": 9,
""90"": 1,
""95"": 1
},
""band"": 1
}
},
{
""tool"": ""weighted_sum_rasters"",
""arguments"": {
""raster_paths"": [
""output/norm_vul.tif"",
""output/reclass_lc.tif""
],
""output_name"": ""wsum_risk.tif"",
""bands"": [
1,
1
],
""weights"": [
8,
10
]
}
},
{
""tool"": ""threshold_raster"",
""arguments"": {
""raster_path"": ""output/wsum_risk.tif"",
""output_name"": ""high_risk_mask.tif"",
""threshold"": 100.0,
""band"": 1,
""true_value"": 1,
""false_value"": 0
}
},
{
""tool"": ""extract_raster_classes_by_mask"",
""arguments"": {
""lc_path"": ""dataset/land_cover.tif"",
""high_risk_mask_path"": ""output/high_risk_mask.tif"",
""output_name"": ""undeveloped_high_risk.tif"",
""undeveloped_codes"": [
31,
41,
43,
52,
71,
81,
90,
95
],
""lc_band"": 1,
""mask_band"": 1,
""mask_true_value"": 1
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""output/wsum_risk.tif"",
""type"": ""raster"",
""style"": {
""cmap"": ""viridis"",
""alpha"": 0.9,
""label"": ""Risk"",
""colorbar"": true
}
},
{
""data"": ""output/undeveloped_high_risk.tif"",
""type"": ""raster"",
""style"": {
""cmap"": ""Greys"",
""alpha"": 0.5,
""label"": ""Undeveloped""
}
}
],
""output_name"": ""undeveloped_areas.png"",
""title"": ""Water Risk and Undeveloped Areas"",
""legend_loc"": ""best""
}
}
]",undeveloped_areas.png,"undeveloped_high_risk.tif
wsum_risk.tif",,,,,,,,,,
27,Geostatistical Analysis,"Your task is to estimate the interaction strengths between subregions using a Random Forest model. First, load the OD flow data and socio-economic attribute data for each subregion. Second, aggregate the OD flow data to the subregion scale to create subregion spatial interaction data. Third, prepare the dataset by merging the aggregated OD flows with the socio-economic attributes of origin and destination subregions. Finally, build a Random Forest model with hyperparameter tuning using GridSearchCV to predict interaction strengths and evaluate the model performance using mean squared error. Save the resulting MSE score in a JSON file named ""model_performance.json"" with a key ""mse"".","dataset/od_data.csv: Represents the movement volume between subregions, used for analyzing interaction strengths and regional connectivity.
dataset/socioeconomic_data.csv: Contains socio-economic details for each subregion, providing contextual data that can be merged with OD flow data to enhance analysis by incorporating socio-economic factors influencing interactions.",no style,8,"[
{
""tool"": ""get_csv_info"",
""arguments"": {
""csv_path"": ""dataset/socioeconomic_data.csv""
}
},
{
""tool"": ""get_csv_info"",
""arguments"": {
""csv_path"": ""dataset/od_data.csv""
}
},
{
""tool"": ""aggregate_od_flows"",
""arguments"": {
""od_path"": ""dataset/od_data.csv"",
""attributes_path"": ""dataset/socioeconomic_data.csv"",
""flow_field"": ""FlowVolume"",
""output_name"": ""28_merged_data.csv"",
""origin_field"": ""Origin"",
""destination_field"": ""Destination"",
""join_key"": ""Subregion"",
""undirected"": false
}
},
{
""tool"": ""prepare_model_data"",
""arguments"": {
""data_path"": ""./output\\28_merged_data.csv"",
""output_name"": ""28"",
""target_column"": ""TotalFlow"",
""feature_columns"": null,
""drop_columns"": null,
""index"": false
}
},
{
""tool"": ""split_train_test"",
""arguments"": {
""csv_path"": ""./output\\28_prepared.csv"",
""output_name"": ""28"",
""label_column"": ""TotalFlow"",
""test_size"": 0.2,
""random_state"": 42,
""stratify"": false,
""index"": false
}
},
{
""tool"": ""tune_model_hyperparameters"",
""arguments"": {
""X_train_path"": ""./output\\28_X_train.csv"",
""output_name"": ""28"",
""y_train_path"": ""./output\\28_y_train.csv"",
""target_column"": ""TotalFlow""
}
},
{
""tool"": ""generate_model_predictions"",
""arguments"": {
""model_path"": ""./output\\28_best_model.joblib"",
""X_test_path"": ""./output\\28_X_test.csv"",
""output_name"": ""28"",
""output_type"": ""labels"",
""pred_column"": ""y_pred""
}
},
{
""tool"": ""evaluate_regression_mse"",
""arguments"": {
""labels_path"": ""./output\\28_y_test.csv"",
""predictions_path"": ""./output\\28_predictions.csv"",
""output_name"": ""model_performance"",
""label_column"": ""TotalFlow"",
""pred_column"": ""y_pred""
}
}
]",CHECK:JSON_VALUE:model_performance.json:mse:<6000,model_performance.json,,,,,,,,,,
28,3D Modeling and Analysis,"To calculate tsunami travel time, set up your project with bathymetric and coastal data, then identify the tsunami origin. Simulate wave travel over the bathymetric surface to create an arrival time map. Visualize and analyze the results, and save the final visualization as ""TsunamiRiskMap.png"".","dataset/AtlanticDEM.tif: A raster Digital Elevation Model (DEM) representing bathymetry (seafloor depth) of the Atlantic Ocean. This dataset is essential for modeling tsunami wave propagation, as ocean depth directly influences wave speed and travel time.
dataset/TsunamiPaths.shp: A polyline shapefile containing precomputed tsunami travel paths and associated attributes. It is used to visualize and analyze potential tsunami wave routes and their estimated arrival characteristics along coastal regions.","elevation background style: {""cmap"": ""terrain"", ""alpha"": 0.6}
tsunami travel time style: {""column"": <placeholder>, ""cmap"": ""viridis"", ""linewidth"": 3, ""legend"": True, ""legend_kwds"": {""label"": ""Travel Time (Hours)""}}",8,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/AtlanticDEM.tif""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/TsunamiPaths.shp""
}
},
{
""tool"": ""invert_dem_values"",
""arguments"": {
""raster_path"": ""dataset/AtlanticDEM.tif"",
""output_name"": ""Atlantic_Depth.tif"",
""band"": 1
}
},
{
""tool"": ""calculate_speed"",
""arguments"": {
""depth_path"": ""./output\\Atlantic_Depth.tif"",
""output_name"": ""Atlantic_Speed.tif"",
""band"": 1
}
},
{
""tool"": ""extract_route_origins"",
""arguments"": {
""paths_path"": ""dataset/TsunamiPaths.shp"",
""output_name"": ""Route_Origins.geojson"",
""route_field"": ""Route""
}
},
{
""tool"": ""densify_paths"",
""arguments"": {
""paths_path"": ""dataset/TsunamiPaths.shp"",
""output_name"": ""Densified_Paths.shp"",
""spacing_m"": 5000
}
},
{
""tool"": ""calculate_travel_time"",
""arguments"": {
""speed_path"": ""./output\\Atlantic_Speed.tif"",
""densified_paths_path"": ""./output\\Densified_Paths.shp"",
""output_name"": ""Path_TravelTime.shp""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""dataset/AtlanticDEM.tif"",
""type"": ""raster"",
""style"": {
""cmap"": ""terrain"",
""alpha"": 0.6
}
},
{
""data"": ""./output\\Path_TravelTime.shp"",
""type"": ""vector"",
""style"": {
""column"": ""TravelTime"",
""cmap"": ""viridis"",
""linewidth"": 3,
""legend"": true,
""legend_kwds"": {
""label"": ""Travel Time (Hours)""
}
}
}
],
""output_name"": ""TsunamiRiskMap.png"",
""title"": ""Tsunami Travel Time Segments with Elevation Background"",
""legend_loc"": ""best""
}
}
]",TsunamiRiskMap.png,"AtlanticDEM.tif
Path_TravelTime.shp",,,,,,,,,,
29,Geostatistical Analysis,"Your task is to analyze the spatial aggregation patterns and scales of geographical flows using the L-function. First, load the flow data, origin area data , and destination area data. Second, calculate the local K-function for each flow by iterating over flow pairs and applying distance-based analysis with edge correction. Third, compute the global L-function by averaging the local K-functions and transforming the result. Finally, visualize the L-function as ""l_function.png"".","dataset/flow.csv: Contains the geographical flows represented as connected origin-destination (OD) point pairs.
dataset/o_area.csv: Contains the boundary coordinates for the region where the origin points lie.
dataset/d_area.csv: Contains the boundary coordinates for the region where the destination points lie.","style: {""x_col"": ""r"", ""y_col"": ""L"", ""label"": ""L(r)"", ""color"": ""blue"", ""linewidth"": 2}",7,"[
{
""tool"": ""get_csv_info"",
""arguments"": {
""csv_path"": ""dataset/o_area.csv""
}
},
{
""tool"": ""get_csv_info"",
""arguments"": {
""csv_path"": ""dataset/d_area.csv""
}
},
{
""tool"": ""get_csv_info"",
""arguments"": {
""csv_path"": ""dataset/flow.csv""
}
},
{
""tool"": ""local_k_function"",
""arguments"": {
""flow_path"": ""dataset/flow.csv"",
""output_name"": ""localK_flow.csv""
}
},
{
""tool"": ""global_l_function"",
""arguments"": {
""localK_path"": ""./output\\localK_flow.csv"",
""o_area_path"": ""dataset/o_area.csv"",
""d_area_path"": ""dataset/d_area.csv"",
""output_name"": ""L_localK_flow.csv""
}
},
{
""tool"": ""create_chart"",
""arguments"": {
""layers"": [
{
""data"": ""./output\\L_localK_flow.csv"",
""style"": {
""x_col"": ""r"",
""y_col"": ""L"",
""label"": ""L(r)"",
""color"": ""blue"",
""linewidth"": 2
}
}
],
""output_name"": ""l_function.png"",
""title"": ""L-function Analysis"",
""xlabel"": ""Distance r (km)"",
""ylabel"": ""L(r)"",
""invert_y"": false,
""secondary_xlabel"": null
}
}
]",l_function.png,L_localK_flow.csv,,,,,,,,,,
30,Hydrological Analysis,"Your task is analyzing the impact of land subsidence on flooding based on future elevation data of the study area. You will calculate the flood damage to building in the future given the elevation of sea level in 2050 and shapefile of study area. You need find the flood depth given the elevation and a design water level of -200. Then you will filter and select the buildings within the flooded area. The final goal is summarizing the flood depth in each building area and calculate the final flood damage. Finally, visualize the results and save the output as ""Damage_Costs.png"".","dataset/Elevation_2050.tif: Raster file for storing predicted flood evelation in 2050.
dataset/StudyAreaBuildings.shp: Polygon Shapefile for shape and features of buildings in the study area.","Flood Damage style: {""column"": <placeholder>, ""cmap"": ""YlOrRd"", ""edgecolor"": ""black"", ""linewidth"": 0.1, ""legend"": True, ""legend_kwds"": {""label"": ""Damage Costs ($)"", ""orientation"": ""vertical""}} ",7,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/Elevation_2050.tif""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/StudyAreaBuildings.shp""
}
},
{
""tool"": ""compute_flood_depth"",
""arguments"": {
""elevation_raster_path"": ""dataset/Elevation_2050.tif"",
""output_name"": ""flood_depth.tif"",
""threshold"": -200
}
},
{
""tool"": ""raster_to_polygons"",
""arguments"": {
""raster_path"": ""./output\\flood_depth.tif"",
""output_name"": ""flood_extent.geojson"",
""band"": 1
}
},
{
""tool"": ""zonal_statistics"",
""arguments"": {
""raster_path"": ""./output\\flood_depth.tif"",
""polygons_path"": ""dataset/StudyAreaBuildings.shp"",
""output_name"": ""Buildings_Flooded_Stats.geojson"",
""band"": 1,
""stats"": [
""mean""
]
}
},
{
""tool"": ""calculate_flood_damage_costs"",
""arguments"": {
""buildings_stats_path"": ""./output\\Buildings_Flooded_Stats.geojson"",
""output_name"": ""Damage_Costs.geojson"",
""mean_field"": ""mean"",
""cost_field"": ""DmgCost"",
""area_field"": null
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""./output\\Damage_Costs.geojson"",
""type"": ""vector"",
""style"": {
""column"": ""DmgCost"",
""cmap"": ""YlOrRd"",
""edgecolor"": ""black"",
""linewidth"": 0.1,
""legend"": true,
""legend_kwds"": {
""label"": ""Damage Costs ($)"",
""orientation"": ""vertical""
}
}
}
],
""output_name"": ""Damage_Costs.png"",
""title"": ""Projected Flood Damage Costs (2050)"",
""legend_loc"": ""best""
}
}
]",Damage_Costs.png,Damage_Costs.geojson,,,,,,,,,,
31,Vector Spatial Analysis,"To promote sustainable development in the nation's rural areas, your task is estimating access to all-season roads. First, you'll add population and road data and limit your study area to rural regions. With the shape of rural area, you will clip the regional population of the rural areas.Then, you'll create a 2-kilometer buffer around all-season roads and clip the rural area out. The final goal is calculating the percentage of the rural population within that buffer to get the percentage of population in the rural area that has access to roads in 2km. Finally, visualize the population percentage in each rural area with a choropleth map. Save the result as ""accessibility.png"".","dataset/ShikokuMetropolitan.geojson: Geojson file that stores the shape and features for towns in Shikoku area of Japan as polygons. Area type indicate whether an area is rural.
dataset/AllSeasonRoads.geojson: Geojson file that stores all season emergency roads in Shikoku area as lines.
dataset/ShikokuPopulation.geojson: Geojson file that stores the shape and features for areas in Shikoku. D0001 columns refers to population of that area in 2015.","population proportion style: {""column"": <placeholder>, ""cmap"": ""Greens"", ""legend"": True, ""vmin"": 0, ""vmax"": 1.0, ""legend_kwds"": {""label"": ""Access Proportion"", ""orientation"": ""vertical""}}
roads style: {""color"": ""black"", ""linewidth"": 0.5, ""label"": ""Roads""}",8,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/AllSeasonRoads.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/ShikokuMetropolitan.geojson""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/ShikokuPopulation.geojson""
}
},
{
""tool"": ""clip_by_attribute"",
""arguments"": {
""input_geojson_path"": ""dataset/ShikokuPopulation.geojson"",
""mask_geojson_path"": ""dataset/ShikokuMetropolitan.geojson"",
""output_name"": ""rural_population.geojson"",
""attribute_field"": ""AREATYPE"",
""attribute_value"": ""RURAL""
}
},
{
""tool"": ""buffer_vector"",
""arguments"": {
""input_path"": ""dataset/AllSeasonRoads.geojson"",
""distance"": 2000.0,
""output_name"": ""roads_buffer.geojson""
}
},
{
""tool"": ""clip_by_attribute"",
""arguments"": {
""input_geojson_path"": ""./output\\rural_population.geojson"",
""mask_geojson_path"": ""./output\\roads_buffer.geojson"",
""output_name"": ""rural_roads_buffer.geojson"",
""attribute_field"": null,
""attribute_value"": ""RURAL""
}
},
{
""tool"": ""calculate_population_access_proportion"",
""arguments"": {
""roads_rural_path"": ""./output\\rural_roads_buffer.geojson"",
""population_rural_path"": ""./output\\rural_population.geojson"",
""output_name"": ""accessibility_proportion.geojson"",
""population_field"": ""D0001""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""./output\\accessibility_proportion.geojson"",
""type"": ""vector"",
""style"": {
""column"": ""population_proportion"",
""cmap"": ""Greens"",
""legend"": true,
""vmin"": 0,
""vmax"": 1.0,
""legend_kwds"": {
""label"": ""Access Proportion"",
""orientation"": ""vertical""
}
}
},
{
""data"": ""dataset/AllSeasonRoads.geojson"",
""type"": ""vector"",
""style"": {
""color"": ""black"",
""linewidth"": 0.5,
""label"": ""Roads""
}
}
],
""output_name"": ""accessibility.png"",
""title"": ""Proportion of Rural Population with Road Access (2km)"",
""legend_loc"": ""best""
}
}
]",accessibility.png,"accessibility_proportion.geojson
AllSeasonRoads.geojson",,,,,,,,,,
32,Raster Spatial Analysis,"Your task is to assess the impact of hail damage on cornfields using pre- and post-storm satellite imagery. You will calculate vegetation loss due to the storm by applying the Soil-Adjusted Vegetation Index (SAVI) to multispectral imagery from the study area. This involves computing SAVI for both the pre-storm and post-storm images, then subtracting them to obtain a pixel-level vegetation loss map. Finally, visualize the vegetation loss and save the output as ""Veg_Loss.png"".","dataset/Before_Storm.tif: Raster file containing multispectral satellite imagery before the hailstorm.
dataset/After_Storm.tif: Raster file containing multispectral satellite imagery after the hailstorm. ","style: vegetation loss style: {""cmap"": ""RdYlGn_r"", ""vmin"": -1.0, ""vmax"": 1.0, ""legend"": True, ""legend_label"": ""Vegetation Loss (Before - After)""}",7,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/Before_Storm.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/After_Storm.tif""
}
},
{
""tool"": ""calculate_savi"",
""arguments"": {
""input_raster_path"": ""dataset/Before_Storm.tif"",
""output_name"": ""SAVI_Before.tif"",
""nir_band"": 4,
""red_band"": 3
}
},
{
""tool"": ""calculate_savi"",
""arguments"": {
""input_raster_path"": ""dataset/After_Storm.tif"",
""output_name"": ""SAVI_After.tif"",
""nir_band"": 4,
""red_band"": 3
}
},
{
""tool"": ""align_raster_to_reference"",
""arguments"": {
""raster_path"": ""./output\\SAVI_After.tif"",
""reference_raster_path"": ""./output\\SAVI_Before.tif"",
""output_name"": ""SAVI_After_Aligned.tif"",
""resampling"": ""nearest"",
""band"": null
}
},
{
""tool"": ""calculate_raster_difference"",
""arguments"": {
""raster_a_path"": ""./output\\SAVI_Before.tif"",
""raster_b_path"": ""./output\\SAVI_After_Aligned.tif"",
""output_name"": ""Veg_Loss.tif""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\Veg_Loss.tif"",
""output_name"": ""Veg_Loss.png"",
""bands"": null,
""cmap"": ""RdYlGn_r"",
""stretch"": true,
""title"": ""Vegetation Loss (SAVI Difference)"",
""legend"": true,
""legend_label"": ""Vegetation Loss (Before - After)"",
""vmin"": -1.0,
""vmax"": 1.0
}
}
]",Veg_Loss.png,Veg_Loss.tif,,,,,,,,,,
33,Vector Spatial Analysis,"Your task is to calculate the travel time from Natural History Building(Geography department) at University of Illinois Urbana-Champaign to the other buildings on the campus. Natural History Building's location is latitude = 40.109442588847976, longitude = -88.22625178710955. You will retrieve the place information as graph from Open Street Map network data. Using the graph, you will find out the speeds and required travel time between nodes. Calculate the driving-time distances for the location nodes from the nearest node next to Natural History Building. Set the node attributes with the travel time. Finally, visualize the travel time by plotting the nodes with specific colors and save the graph as ""travel_time.png"".","dataset/street_graph.graphml: GraphML file containing the street network of UIUC campus extracted from Open Street Map, with nodes representing intersections and edges representing road segments including attributes like length and speed limits.
dataset/distances.pkl: Pickle file containing pre-calculated mappings between campus buildings and their nearest nodes in the street network, along with spatial relationships and distance metrics.","edge layer style:{""column"": <placeholder>, ""cmap"": ""RdYlBu"", ""linewidth"": 0.6, ""alpha"": 0.7, ""legend"": True, ""legend_kwds"": {""label"": ""Edge Travel Time (s)"", ""orientation"": ""horizontal"", ""pad"": 0.01}}
node layer style:{""column"": <placeholder>, ""cmap"": ""RdYlBu"", ""markersize"": 3, ""alpha"": 0.9, ""legend"": True, ""legend_kwds"": {""label"": ""Node Travel Time"", ""orientation"": ""vertical""}}",11,"[
{
""tool"": ""get_graphml_info"",
""arguments"": {
""graphml_path"": ""dataset/street_graph.graphml""
}
},
{
""tool"": ""get_pkl_info"",
""arguments"": {
""pkl_path"": ""dataset/distances.pkl""
}
},
{
""tool"": ""save_network_graph"",
""arguments"": {
""graph_path"": ""dataset/street_graph.graphml"",
""output_name"": ""street_graph_copy.graphml""
}
},
{
""tool"": ""annotate_edges_with_attributes"",
""arguments"": {
""graph_path"": ""dataset/street_graph.graphml"",
""output_name"": ""graph_with_speed.graphml"",
""default_speed_kmh"": 30.0,
""extra_annotations"": null,
""expressions"": null,
""highway_speed_defaults"": null
}
},
{
""tool"": ""annotate_edges_with_attributes"",
""arguments"": {
""graph_path"": ""./output\\graph_with_speed.graphml"",
""output_name"": ""graph_with_travel_time.graphml"",
""default_speed_kmh"": 30.0,
""extra_annotations"": null,
""expressions"": null,
""highway_speed_defaults"": null
}
},
{
""tool"": ""find_nearest_node"",
""arguments"": {
""graph_path"": ""./output\\graph_with_travel_time.graphml"",
""lat"": 40.109442588847976,
""lon"": -88.22625178710955
}
},
{
""tool"": ""compute_shortest_path_lengths"",
""arguments"": {
""graph_path"": ""./output\\graph_with_travel_time.graphml"",
""source_node"": 5902412844,
""output_name"": ""distances_to_nearest.pkl""
}
},
{
""tool"": ""summarize_distances"",
""arguments"": {
""graph_path"": ""./output\\graph_with_travel_time.graphml"",
""distances"": {
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""38024753"": 129.275921653215,
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""12727068120"": 130.0612874579608,
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""37979006"": 131.83144002748713,
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""10978213921"": 135.26898784839076,
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""2702125263"": 156.38653885715297,
""37976727"": 158.31331931715846,
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""38153883"": 166.65126928995878,
""1579076654"": 169.22157743574837,
""38020614"": 169.54878873817427,
""38003560"": 173.50474298667916,
""37991559"": 182.538348007504,
""37976729"": 182.9790673865155
},
""output_name"": ""network_statistics.txt""
}
},
{
""tool"": ""build_nodes_points_layer_from_mapping"",
""arguments"": {
""graph_path"": ""./output\\graph_with_travel_time.graphml"",
""times_path"": ""./output\\distances_to_nearest.pkl"",
""output_name"": ""nodes_time_layer.geojson"",
""attr_name"": ""time2nhb"",
""fill_missing"": 0.0,
""scale_from_max"": 1.5,
""x_attr"": ""x"",
""y_attr"": ""y"",
""crs"": null,
""drop_missing_values"": false
}
},
{
""tool"": ""build_edges_lines_layer_from_graph"",
""arguments"": {
""graph_path"": ""./output\\graph_with_travel_time.graphml"",
""output_name"": ""edges_time_layer.geojson"",
""attr_name"": ""travel_time_s"",
""drop_missing_values"": true
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""D:\\study_up\\geobench\\output\\edges_time_layer.geojson"",
""type"": ""vector"",
""style"": {
""column"": ""travel_time_s"",
""cmap"": ""RdYlBu"",
""linewidth"": 0.6,
""alpha"": 0.7,
""legend"": true,
""legend_kwds"": {
""label"": ""Edge Travel Time (s)"",
""orientation"": ""horizontal"",
""pad"": 0.01
}
}
},
{
""data"": ""D:\\study_up\\geobench\\output\\nodes_time_layer.geojson"",
""type"": ""vector"",
""style"": {
""column"": ""time2nhb"",
""cmap"": ""RdYlBu"",
""markersize"": 3,
""alpha"": 0.9,
""legend"": true,
""legend_kwds"": {
""label"": ""Node Travel Time"",
""orientation"": ""vertical""
}
}
}
],
""output_name"": ""travel_time.png"",
""title"": ""Street Network Travel Time"",
""legend_loc"": ""best""
}
}
]",travel_time.png,"edges_time_layer.geojson
nodes_time_layer.geojson",,,,,,,,,,
34,Raster Spatial Analysis,"Your task is to analyze changes in malaria prevalence among children aged 2–10 in Sub-Saharan Africa from 2000 to 2015. Using raster data from the Malaria Atlas Project, calculate the difference between the prevalence rates in 2015 and 2000 using the Minus raster function (i.e., 2015 minus 2000). Then, visualize the resulting change in malaria prevalence with a diverging color scheme (e.g., blue for decreases, red for increases) and adjust the symbology to clearly highlight areas where malaria prevalence has increased or decreased. Finally, save the visualization as ""Malaria_Change.png""","dataset/2015_Nature_Africa_PR.2000.tif: GeoTIFF raster file storing malaria prevalence rates among children aged 2–10 in Sub-Saharan Africa for the year 2000.
dataset/2015_Nature_Africa_PR.2015.tif: GeoTIFF raster file storing malaria prevalence rates among children aged 2–10 in Sub-Saharan Africa for the year 2015.","style: {""cmap"": ""RdBu_r"",""vmin"": -1.0,""vmax"": 1.0,""legend"": True,""legend_label"":""Change in Prevalence""}",4,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/2015_Nature_Africa_PR.2000.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/2015_Nature_Africa_PR.2015.tif""
}
},
{
""tool"": ""calculate_raster_difference"",
""arguments"": {
""raster_a_path"": ""dataset/2015_Nature_Africa_PR.2015.tif"",
""raster_b_path"": ""dataset/2015_Nature_Africa_PR.2000.tif"",
""output_name"": ""Malaria_Change.tif""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\Malaria_Change.tif"",
""output_name"": ""Malaria_Change.png"",
""bands"": null,
""cmap"": ""RdBu_r"",
""stretch"": true,
""title"": ""Malaria Prevalence Change (2000-2015)"",
""legend"": true,
""legend_label"": ""Change in Prevalence"",
""vmin"": -1.0,
""vmax"": 1.0
}
}
]",Malaria_Change.png,Malaria_Change.tif,,,,,,,,,,
35,Geostatistical Analysis,"Your task is to perform spatial autocorrelation analysis on airbnb prices in Berlin. The analysis involves loading geospatial data of Berlin neighborhoods and real estate listings. You will perform a spatial join to associate listings with neighborhoods and calculate the median price for each neighborhood group. After handling missing data, you will analyze spatial similarity by creating a spatial weights matrix based on their median prices. The Local Moran's I statistic will be used to identify significant cold spots and hot spots. Finally, you will visualize these spatial patterns on a map, highlighting areas with significant spatial autocorrelation, and save the output as ""moran_local.png"".","dataset/berlin_neighbourhoods.geojson:Geojson file for multipolygons of neighbourhoods in Berling
dataset/berlin_listings.csv: Csv file of Berling Airbnb information, with lat and lng of Airbnb.","style: {""column"": <placeholder>,""categorical"": True,""cmap"": ""Set1"",""linewidth"": 0.1,""edgecolor"": ""white"",""legend"": True, ""legend_kwds"": {""title"": ""Cluster Type""}}",6,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/berlin_neighbourhoods.geojson""
}
},
{
""tool"": ""get_csv_info"",
""arguments"": {
""csv_path"": ""dataset/berlin_listings.csv""
}
},
{
""tool"": ""csv_to_points"",
""arguments"": {
""csv_path"": ""dataset/berlin_listings.csv"",
""output_name"": ""berlin_listings.shp"",
""crs"": ""EPSG:4326"",
""lon_field"": ""longitude"",
""lat_field"": ""latitude""
}
},
{
""tool"": ""create_spatial_join"",
""arguments"": {
""left_path"": ""dataset/berlin_neighbourhoods.geojson"",
""right_path"": ""./output\\berlin_listings.shp"",
""output_name"": ""berlin_listings_join.geojson"",
""predicate"": ""intersects"",
""how"": ""inner"",
""max_distance"": null,
""distance_col"": null
}
},
{
""tool"": ""local_moran"",
""arguments"": {
""input_path"": ""./output\\berlin_listings_join.geojson"",
""value_field"": ""price"",
""output_name"": ""moran_local.geojson"",
""p_threshold"": 0.05,
""group_field"": ""neighbourhood_group"",
""polygons_path"": ""dataset/berlin_neighbourhoods.geojson""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""./output\\moran_local.geojson"",
""type"": ""vector"",
""style"": {
""column"": ""cl"",
""categorical"": true,
""cmap"": ""Set1"",
""linewidth"": 0.1,
""edgecolor"": ""white"",
""legend"": true,
""legend_kwds"": {
""title"": ""Cluster Type""
}
}
}
],
""output_name"": ""moran_local.png"",
""title"": ""Local Moran's I"",
""legend_loc"": ""best""
}
}
]",moran_local.png,moran_local.geojson,,,,,,,,,,
36,Raster Spatial Analysis,"Your task is to evaluate mountain lion habitat suitability by calculating terrain ruggedness from elevation data. The analysis applies focal statistics with a 3x3 moving window to measure terrain variation, followed by rescaling to standardize the ruggedness values. The final result should be visualized as ""ruggedness_norm.png"".",dataset/Elevation.tif: Raster files for storing elevation data.,"style: {""cmap"": ""viridis"", ""legend"": True, ""legend_label"": ""Ruggedness""}",4,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/Elevation.tif""
}
},
{
""tool"": ""compute_ruggedness"",
""arguments"": {
""input_path"": ""dataset/Elevation.tif"",
""output_name"": ""ruggedness.tif"",
""window_size"": 3
}
},
{
""tool"": ""normalize_raster_values"",
""arguments"": {
""input_raster_path"": ""./output\\ruggedness.tif"",
""output_name"": ""ruggedness_norm.tif"",
""lower"": 1.0,
""upper"": 10.0
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\ruggedness_norm.tif"",
""output_name"": ""ruggedness_norm.png"",
""bands"": null,
""cmap"": ""viridis"",
""stretch"": true,
""title"": ""Ruggedness"",
""legend"": true,
""legend_label"": ""Ruggedness"",
""vmin"": null,
""vmax"": null
}
}
]",ruggedness_norm.png,ruggedness_norm.tif,,,,,,,,,,
37,Raster Spatial Analysis,"Your task is to calculate impervious surface area by combining the impervious surface raster data with land parcel polygons. Use the Tabulate Area tool to compute the area of impervious surfaces within each parcel, then join these area statistics back to the parcel layer. Finally, visualize the resulting impervious surface distribution and save the map as ""impervious_area.png"".","dataset/parcels.shp: The parcels of land in the neighborhood.
dataset/impervious.tif: Raster file contains the impervious rates of the whole study area.","style: {""column"":<placeholder>,""cmap"": ""Reds"", ""edgecolor"": ""black"",""linewidth"":0.5, ""legend"": True, ""legend_kwds"": {""label"": ""Impervious Area (m²)""}}",6,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/impervious.tif""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/parcels.shp""
}
},
{
""tool"": ""reproject_vector"",
""arguments"": {
""vector_path"": ""dataset/parcels.shp"",
""target_crs"": ""EPSG:2246"",
""output_name"": ""parcels_reprojected.geojson""
}
},
{
""tool"": ""calculate_area_above_threshold"",
""arguments"": {
""parcels_path"": ""./output\\parcels_reprojected.geojson"",
""raster_path"": ""dataset/impervious.tif"",
""output_name"": ""impervious_area.csv"",
""parcel_id_field"": ""Parcel_ID"",
""threshold"": 0.0
}
},
{
""tool"": ""join_table_to_vector"",
""arguments"": {
""vector_path"": ""./output\\parcels_reprojected.geojson"",
""table_path"": ""output/impervious_area.csv"",
""vector_key"": ""Parcel_ID"",
""table_key"": ""Parcel_ID"",
""output_name"": ""parcels_impervious_join.geojson""
}
},
{
""tool"": ""visualize_vector"",
""arguments"": {
""input_path"": ""./output\\parcels_impervious_join.geojson"",
""output_name"": ""impervious_area.png"",
""style"": {
""column"": ""impervious_area"",
""cmap"": ""Reds"",
""edgecolor"": ""black"",
""linewidth"": 0.5
},
""title"": ""Impervious Area Distribution"",
""legend"": true,
""legend_kwds"": {
""label"": ""Impervious Area (m²)""
}
}
}
]",impervious_area.png,parcels_impervious_join.geojson,,,,,,,,,,
38,Geostatistical Analysis,"Your task is to analyze how location impacts interest rates. First, select and copy the tracts with at least 30 loans. Find interest rate hot spots on the selected loan tracts. Then create a Geographically Weighted Regression (GWR) model on the selected data to determine how location impacts interest rates. Finally, visualize the interest rate hot spots and save the output as ""loan_hot_spots.png"".","dataset/loan_data.shp: The loan data from August 2007 to September 2015, obtained from LendingClub Statistics.","style: {""column"": <placeholder>,""categorical"": True,""cmap"":""RdYlBu_r"",""legend"": True,""legend_kwds"": {""title"": ""Hot Spot Type""}}",5,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/loan_data.shp""
}
},
{
""tool"": ""filter_features_by_expression"",
""arguments"": {
""input_path"": ""dataset/loan_data.shp"",
""output_name"": ""loan_data_selected.geojson"",
""expression"": ""AcceptedLo >= 30""
}
},
{
""tool"": ""classify_hotspots"",
""arguments"": {
""input_vector_path"": ""./output\\loan_data_selected.geojson"",
""field"": ""AveInteres"",
""output_name"": ""hot_spots_result.geojson""
}
},
{
""tool"": ""ols_regression"",
""arguments"": {
""input_vector_path"": ""./output\\loan_data_selected.geojson"",
""y_field"": ""AveInteres"",
""x_field"": ""AveLoanGra"",
""output_name"": ""ols_result.geojson""
}
},
{
""tool"": ""visualize_vector"",
""arguments"": {
""input_path"": ""./output\\hot_spots_result.geojson"",
""output_name"": ""loan_hot_spots.png"",
""style"": {
""column"": ""hspot_type"",
""categorical"": true,
""cmap"": ""RdYlBu_r""
},
""title"": ""Hot Spot Analysis Result"",
""legend"": true,
""legend_kwds"": {
""title"": ""Hot Spot Type""
}
}
}
]",loan_hot_spots.png,hot_spots_result.geojson,,,,,,,,,,
39,Spatial Data Management,"Your task is to analyze homelessness trends in North Dakota during the 2013 oil boom. Join the homelessness tabular data to the U.S. states spatial layer using state abbreviations, and save the resulting map as ""us_homeless_2013.png"".","dataset/homeless_data.xlsx: containing state-level homelessness data in the United States for 2013. The dataset includes the following columns:State, Change, Pop13, Homeless13
dataset/USA_States.shp: This shapefile contains polygon boundaries for the U.S. states, providing geographic reference for spatial analysis. ","style: {""column"": <placeholder>, ""legend"": True, ""cmap"": ""Reds""}",4,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/USA_States.shp""
}
},
{
""tool"": ""join_table_to_vector"",
""arguments"": {
""vector_path"": ""dataset/USA_States.shp"",
""table_path"": ""dataset/homeless_data.xlsx"",
""vector_key"": ""L0USA_St_1"",
""table_key"": ""State"",
""output_name"": ""joined_USA_States.geojson""
}
},
{
""tool"": ""calculate_vector_field"",
""arguments"": {
""input_path"": ""./output\\joined_USA_States.geojson"",
""output_name"": ""joined_USA_States_Rate.geojson"",
""new_field"": ""HomelessRate"",
""expression"": ""(Homeless13 / Pop13) * 10000""
}
},
{
""tool"": ""visualize_vector"",
""arguments"": {
""input_path"": ""./output\\joined_USA_States_Rate.geojson"",
""output_name"": ""us_homeless_2013.png"",
""style"": {
""column"": ""HomelessRate"",
""cmap"": ""Reds""
},
""title"": ""Homeless Population per 10k"",
""legend"": true,
""legend_kwds"": null
}
}
]",us_homeless_2013.png,joined_USA_States_Rate.geojson,,,,,,,,,,
40,Geostatistical Analysis,"Your task is to predict seagrass habitats with machine learning methods. First, dissolve the USA seagrass layer and generate 5,000 random points within the dissolved polygon. Then, use the Empirical Bayesian Kriging (EBK) geostatistical method to interpolate raster surfaces for the environmental variables—temperature and salinity—stored in the Global ocean measurements point layer, processing each variable separately. Finally, use the two resulting kriging rasters as explanatory variables in a presence-only habitat suitability model (MaxEnt). The outputs should include the training feature data and the predicted seagrass habitat raster. Visualize the final seagrass prediction raster as ""seagrass_prediction.png"".","dataset/Global_ocean_measurements.shp: Ecological Marine Units point data that contains ocean measurements up to a 90-meter water depth.
dataset/Global_shallow_waters.shp: Global shallow bathymetry polygon used to predict seagrass globally.
dataset/USA_seagrass.shp: For seagrass occurrence, every polygon in USA seagrass is an identified seagrass habitat.
dataset/USA_shallow_waters.shp: Shallow bathymetry polygon for the continental United States used as the study area for model training.","style: {""cmap"": ""YlGn"", ""legend"": true, ""legend_label"": ""Habitat Suitability Probability""}",11,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/USA_seagrass.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/USA_shallow_waters.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/Global_ocean_measurements.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/Global_shallow_waters.shp""
}
},
{
""tool"": ""dissolve_polygons"",
""arguments"": {
""input_path"": ""dataset/USA_seagrass.shp"",
""output_name"": ""seagrass_dissolved.shp""
}
},
{
""tool"": ""generate_random_points"",
""arguments"": {
""reference_polygon_path"": ""./output\\seagrass_dissolved.shp"",
""num_points"": 5000,
""output_name"": ""presence_points.shp"",
""target_crs"": ""EPSG:8857"",
""label_field"": ""presence"",
""label_value"": 1
}
},
{
""tool"": ""generate_random_points"",
""arguments"": {
""reference_polygon_path"": ""dataset/USA_shallow_waters.shp"",
""num_points"": 10000,
""output_name"": ""background_points.shp"",
""target_crs"": ""EPSG:8857"",
""label_field"": ""presence"",
""label_value"": 0
}
},
{
""tool"": ""interpolate_points_to_raster"",
""arguments"": {
""points_path"": ""dataset/Global_ocean_measurements.shp"",
""value_column"": ""temp"",
""mask_path"": ""dataset/Global_shallow_waters.shp"",
""output_name"": ""temp_grid.tif"",
""cell_size"": 25000,
""target_crs"": ""EPSG:8857""
}
},
{
""tool"": ""interpolate_points_to_raster"",
""arguments"": {
""points_path"": ""dataset/Global_ocean_measurements.shp"",
""value_column"": ""salinity"",
""mask_path"": ""dataset/Global_shallow_waters.shp"",
""output_name"": ""salinity_grid.tif"",
""cell_size"": 25000,
""target_crs"": ""EPSG:8857""
}
},
{
""tool"": ""predict_habitat_suitability"",
""arguments"": {
""presence_points_path"": ""./output\\presence_points.shp"",
""background_points_path"": ""./output\\background_points.shp"",
""prediction_area_path"": ""dataset/Global_shallow_waters.shp"",
""raster_paths"": {
""temp"": ""./output\\temp_grid.tif"",
""salinity"": ""./output\\salinity_grid.tif""
},
""output_name"": ""seagrass_predict2"",
""n_estimators"": 100,
""random_state"": 42
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\seagrass_predict2.tif"",
""output_name"": ""seagrass_prediction.png"",
""bands"": null,
""cmap"": ""YlGn"",
""stretch"": true,
""title"": ""Predicted Seagrass Habitat Suitability (Global)"",
""legend"": true,
""legend_label"": ""Habitat Suitability Probability"",
""vmin"": null,
""vmax"": null
}
}
]",seagrass_prediction.png,seagrass_predict2.tif,,,,,,,,,,
41,Raster Spatial Analysis,"Your task is standardizing protected status datasets for consistency in analysis for mountain lion habitat. Reclassify this categorical dataset to a common scale using geospatial tools, and visualize the reclassified results, saving them as ""reclassified_protectedStatus.png"".
The protected status data are reclassified as follows: 0 (Unprotected) → 1, 1 (Low protection) → 3, 2 (Moderate protection) → 6, 3 (High protection) → 9, 4 (Highest/strict protection) → 10, and 255 (NoData or undefined) → 10.
These reclassification schemes align the original categorical values to a unified scale suitable for mountain lion habitat suitability modeling.",dataset/Protected_Status.tif: GeoTiff file storing the Protected status in 5 levels as well as the null data.,"style:{""cmap"": ""tab20"", ""legend"": True}",3,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/Protected_Status.tif""
}
},
{
""tool"": ""reclassify_raster"",
""arguments"": {
""raster_path"": ""dataset/Protected_Status.tif"",
""output_name"": ""Protected_Status_reclassified.tif"",
""reclass_dict"": {
""0"": 1,
""1"": 3,
""2"": 6,
""3"": 9,
""4"": 10,
""255"": 10
}
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\Protected_Status_reclassified.tif"",
""output_name"": ""reclassified_protectedStatus.png"",
""cmap"": ""tab20"",
""legend"": true,
""title"": ""Protected Status (reclassified)""
}
}
]",reclassified_protectedStatus.png,Protected_Status_reclassified.tif,,,,,,,,,,
42,3D Modeling and Analysis,"This task performs a 3D line-of-sight (LOS) analysis between two specified points (Observer ID 0 and Target ID 2) from a 3D point shapefile, using 3D building data as potential obstacles. The result indicates whether the target is visible from the observer’s position in 3D space, and is saved to ""los_stats.json"".","dataset/buildings.shp: MultiPatch data (representing buildings as 3D solids)
dataset/point3d.shp: 3D point vector data containing observer and target points",no style,5,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/point3d.shp""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/buildings.shp""
}
},
{
""tool"": ""extract_control_points"",
""arguments"": {
""input_path"": ""dataset/point3d.shp"",
""observer_id"": 0,
""target_id"": 2,
""id_field"": ""FID""
}
},
{
""tool"": ""extract_obstacle_vertices"",
""arguments"": {
""input_path"": ""dataset/buildings.shp"",
""output_name"": ""building_points.json""
}
},
{
""tool"": ""check_line_of_sight"",
""arguments"": {
""observer"": [
-13278.191700000316,
6712896.986199997,
36.0
],
""target"": [
-13440.065900001675,
6712930.1426,
160.0
],
""obstacles_path"": ""./output\\building_points.json"",
""output_name"": ""los_stats.json"",
""distance_threshold"": 2.0
}
}
]",CHECK:JSON_VALUE:los_stats.json:visible:==True,los_stats.json,,,,,,,,,,
43,3D Modeling and Analysis,"Your task is processing 3D building data to create a unified 2D coverage map. First, flatten the 3D polygons to generate 2D footprints, then dissolve them into a unified layer to eliminate overlaps. Finally, visualize the merged geometry and save the result as 'buildings_viz.png'.",dataset/buildings.shp: MultiPatch data (representing buildings as 3D solids),"style: {""color"": ""lightblue"", ""edgecolor"": ""blue"", ""alpha"": 0.7}",4,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/buildings.shp""
}
},
{
""tool"": ""flatten_3d_polygons"",
""arguments"": {
""input_path"": ""dataset/buildings.shp"",
""output_name"": ""buildings_2d.shp""
}
},
{
""tool"": ""dissolve_polygons"",
""arguments"": {
""input_path"": ""./output\\buildings_2d.shp"",
""output_name"": ""buildings_merged.geojson""
}
},
{
""tool"": ""visualize_vector"",
""arguments"": {
""input_path"": ""./output\\buildings_merged.geojson"",
""output_name"": ""buildings_viz.png"",
""style"": {
""color"": ""lightblue"",
""edgecolor"": ""blue"",
""alpha"": 0.7
},
""title"": ""Merged Buildings Coverage"",
""legend"": true,
""legend_kwds"": null
}
}
]",buildings_viz.png,buildings_merged.geojson,,,,,,,,,,
44,Hydrological Analysis,"Your task is generating a hydrologically corrected Digital Elevation Model (DEM) by removing surface depressions. Apply a depression-filling algorithm to the original DEM to ensure water can flow continuously across the terrain. Finally, visualize the resulting depressionless DEM using a terrain colormap and save the output map as 'dem_filled_viz.png'.",dataset/dem.tif: Raster data (Digital Elevation Model) representing the raw terrain surface with elevation values,"style: {""cmap"": ""terrain"", ""legend"": True, ""legend_label"": ""Elevation (m)""}",3,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/dem.tif""
}
},
{
""tool"": ""fill_depressions"",
""arguments"": {
""raster_path"": ""dataset/dem.tif"",
""output_name"": ""dem_filled.tif""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\dem_filled.tif"",
""output_name"": ""dem_filled_viz.png"",
""bands"": null,
""cmap"": ""terrain"",
""stretch"": true,
""title"": ""Depressionless DEM (Filled)"",
""legend"": true,
""legend_label"": ""Elevation (m)"",
""vmin"": null,
""vmax"": null
}
}
]",dem_filled_viz.png,dem_filled.tif,,,,,,,,,,
45,Hydrological Analysis,"Your task is performing hydrological flow accumulation analysis. First, use the pre-processed filled DEM to compute the D8 flow direction raster. Next, calculate the flow accumulation raster based on the flow direction to determine the accumulated upstream contribution for each cell. Finally, visualize the flow accumulation result using a gray colormap with the title ""Flow Accumulation (D8)"" and save the output map as ""FlowAccumulation.png"".",dataset/dem_filled.tif: Raster data (Filled Digital Elevation Model) with depressions removed to ensure hydrological connectivity,"style: {""cmap"": ""gray"", ""legend"": True, ""legend_label"": ""Flow Accumulation""}",4,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/dem_filled.tif""
}
},
{
""tool"": ""compute_flow_direction"",
""arguments"": {
""raster_path"": ""dataset/dem_filled.tif"",
""output_name"": ""flow_direction.tif""
}
},
{
""tool"": ""compute_flow_accumulation"",
""arguments"": {
""flow_direction_path"": ""./output\\flow_direction.tif"",
""output_name"": ""flow_accumulation.tif"",
""weight_raster_path"": null
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\flow_accumulation.tif"",
""output_name"": ""FlowAccumulation.png"",
""bands"": null,
""cmap"": ""gray"",
""stretch"": true,
""title"": ""Flow Accumulation (D8)"",
""legend"": true,
""legend_label"": ""Flow Accumulation"",
""vmin"": null,
""vmax"": null
}
}
]",FlowAccumulation.png,flow_accumulation.tif,,,,,,,,,,
46,Hydrological Analysis,"Your task is identifying and quantifying topographic depressions (sinks) in the study area. Starting with the raw Digital Elevation Model (DEM), first generate a depression-filled DEM to simulate a hydrologically corrected surface. Then, calculate the sink depth raster by computing the vertical difference between the filled and original elevations. Finally, visualize the resulting sink depth map to highlight the location and magnitude of filled areas, saving the output as 'Sink_Depth_Analysis.png'.","dataset/dem.tif: Raster data (Digital Elevation Model) representing the raw terrain surface, which serves as the baseline for detecting depressions.","style: {""cmap"": ""viridis"", ""legend"": True, ""legend_label"": ""Depth (m)""}",4,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/dem.tif""
}
},
{
""tool"": ""fill_depressions"",
""arguments"": {
""raster_path"": ""dataset/dem.tif"",
""output_name"": ""dem_filled.tif""
}
},
{
""tool"": ""compute_depression_depth"",
""arguments"": {
""dem_path"": ""dataset/dem.tif"",
""filled_dem_path"": ""./output\\dem_filled.tif"",
""output_name"": ""sink_depth.tif""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\sink_depth.tif"",
""output_name"": ""Sink_Depth_Analysis.png"",
""bands"": null,
""cmap"": ""viridis"",
""stretch"": true,
""title"": ""Topographic Sink Depth Analysis"",
""legend"": true,
""legend_label"": ""Depth (m)"",
""vmin"": null,
""vmax"": null
}
}
]",Sink_Depth_Analysis.png,sink_depth.tif,,,,,,,,,,
47,Hydrological Analysis,"Your task is performing a downstream flow length analysis to determine the distance from each cell to the watershed outlet. Using the provided D8 flow direction raster, calculate the maximum distance along the flow path from each cell to its downstream sink or outlet. Finally, visualize the downstream flow length result using a 'Blues' colormap and save the output map as 'FlowLength_Downstream.png'.","dataset/flow_direction.tif: Raster data (D8 Flow Direction) where pixel values (1, 2, 4, ..., 128) represent the direction of flow from each cell to its steepest downslope neighbor.","style: {""cmap"": ""Blues"", ""legend"": True, ""legend_label"": ""Downstream Length (m)""}",3,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/flow_direction.tif""
}
},
{
""tool"": ""compute_flow_length_downstream"",
""arguments"": {
""flow_direction_path"": ""dataset/flow_direction.tif"",
""output_name"": ""flow_length_downstream.tif"",
""weight_raster_path"": null
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\flow_length_downstream.tif"",
""output_name"": ""FlowLength_Downstream.png"",
""bands"": null,
""cmap"": ""Blues"",
""stretch"": true,
""title"": ""Flow Length (Downstream)"",
""legend"": true,
""legend_label"": ""Downstream Length (m)"",
""vmin"": null,
""vmax"": null
}
}
]",FlowLength_Downstream.png,flow_length_downstream.tif,,,,,,,,,,
48,Hydrological Analysis,"Your task is performing an upstream flow length analysis to determine the distance from each cell to its flow source. Using the provided D8 flow direction raster, calculate the maximum distance along the flow path from each cell back to its upstream starting point (watershed divide). Finally, visualize the upstream flow length result using a 'Greens' colormap and save the output map as 'FlowLength_Upstream.png'.","dataset/flow_direction.tif: Raster data (D8 Flow Direction) where pixel values (1, 2, 4, ..., 128) represent the direction of flow from each cell to its steepest downslope neighbor.","style: {""cmap"": ""Greens"", ""legend"": True, ""legend_label"": ""Upstream Length (m)""}",3,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/flow_direction.tif""
}
},
{
""tool"": ""compute_flow_length_upstream"",
""arguments"": {
""flow_direction_path"": ""dataset/flow_direction.tif"",
""output_name"": ""flow_length_upstream.tif"",
""weight_raster_path"": null
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\flow_length_upstream.tif"",
""output_name"": ""FlowLength_Upstream.png"",
""bands"": null,
""cmap"": ""Greens"",
""stretch"": true,
""title"": ""Flow Length (Upstream)"",
""legend"": true,
""legend_label"": ""Upstream Length (m)"",
""vmin"": null,
""vmax"": null
}
}
]",FlowLength_Upstream.png,flow_length_upstream.tif,,,,,,,,,,
49,Hydrological Analysis,"Your task is extracting and vectorizing the stream network from a hydrologically corrected DEM. First, calculate flow direction and flow accumulation to identify areas of high runoff concentration. Next, apply a threshold to the accumulation raster to define the stream network raster. Then, convert the raster streams into a vector shapefile. Finally, visualize the extracted river network over the accumulation map and save the result as 'Vector_Stream_Network.png'",dataset/dem_filled.tif: Raster data (Filled Digital Elevation Model) with depressions removed to ensure hydrological connectivity,"style: {""color"": ""blue"", ""linewidth"": 1.0, ""legend"": True}",6,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/dem_filled.tif""
}
},
{
""tool"": ""compute_flow_direction"",
""arguments"": {
""raster_path"": ""dataset/dem_filled.tif"",
""output_name"": ""flow_direction.tif""
}
},
{
""tool"": ""compute_flow_accumulation"",
""arguments"": {
""flow_direction_path"": ""./output\\flow_direction.tif"",
""output_name"": ""flow_accumulation.tif"",
""weight_raster_path"": null
}
},
{
""tool"": ""extract_stream_network"",
""arguments"": {
""flowacc_path"": ""./output\\flow_accumulation.tif"",
""output_name"": ""streamnet.tif"",
""threshold"": 100
}
},
{
""tool"": ""vectorize_stream_network"",
""arguments"": {
""stream_raster_path"": ""./output\\streamnet.tif"",
""flow_direction_path"": ""./output\\flow_direction.tif"",
""output_name"": ""streamfea.shp""
}
},
{
""tool"": ""visualize_vector"",
""arguments"": {
""input_path"": ""./output\\streamfea.shp"",
""output_name"": ""Vector_Stream_Network.png"",
""style"": {
""color"": ""blue"",
""linewidth"": 1.0
},
""title"": ""Vectorized Stream Network"",
""legend"": true,
""legend_kwds"": null
}
}
]",Vector_Stream_Network.png,streamfea.shp,,,,,,,,,,
50,Hydrological Analysis,"Your task is analyzing the hierarchical structure of the river network using the Strahler stream ordering method. Starting from a hydrologically corrected DEM, first generate the stream network raster by calculating flow direction and accumulation. Then, apply the Strahler ordering algorithm to assign a hierarchy level to each stream segment, where 1st order streams are headwaters and order increases only at intersections of same-order streams. Finally, visualize the result using a categorical colormap and save the map as 'Strahler_Order.png'.",dataset/dem_filled.tif: Raster data (Filled Digital Elevation Model) with depressions removed to ensure hydrological connectivity,"style: {""cmap"": ""Set1"", ""legend"": True, ""legend_label"": ""Stream Order""}",6,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/dem_filled.tif""
}
},
{
""tool"": ""compute_flow_direction"",
""arguments"": {
""raster_path"": ""dataset/dem_filled.tif"",
""output_name"": ""flow_direction.tif""
}
},
{
""tool"": ""compute_flow_accumulation"",
""arguments"": {
""flow_direction_path"": ""./output\\flow_direction.tif"",
""output_name"": ""flow_accumulation.tif"",
""weight_raster_path"": null
}
},
{
""tool"": ""extract_stream_network"",
""arguments"": {
""flowacc_path"": ""./output\\flow_accumulation.tif"",
""output_name"": ""streamnet.tif"",
""threshold"": 100
}
},
{
""tool"": ""compute_stream_order"",
""arguments"": {
""stream_raster_path"": ""./output\\streamnet.tif"",
""flow_direction_path"": ""./output\\flow_direction.tif"",
""output_name"": ""Stream_Order_Strahler.tif"",
""method"": ""strahler""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\Stream_Order_Strahler.tif"",
""output_name"": ""Strahler_Order.png"",
""bands"": null,
""cmap"": ""Set1"",
""stretch"": true,
""title"": ""Strahler Stream Order (Hierarchy)"",
""legend"": true,
""legend_label"": ""Stream Order"",
""vmin"": null,
""vmax"": null
}
}
]",Strahler_Order.png,Stream_Order_Strahler.tif,,,,,,,,,,
51,Hydrological Analysis,"Your task is analyzing the discharge magnitude of the river network using the Shreve stream ordering method. Starting from a hydrologically corrected DEM, first generate the stream network raster. Then, apply the Shreve ordering algorithm, which treats the stream network as a magnitude system where downstream orders are the sum of upstream tributaries (e.g., 1+1=2, 2+1=3). Finally, visualize the result using a spectral colormap to represent accumulation intensity and save the map as 'Shreve_Order.png'.",dataset/dem_filled.tif: Raster data (Filled Digital Elevation Model) with depressions removed to ensure hydrological connectivity,"style: {""cmap"": ""jet"", ""legend"": True, ""legend_label"": ""Stream Magnitude""}",6,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/dem_filled.tif""
}
},
{
""tool"": ""compute_flow_direction"",
""arguments"": {
""raster_path"": ""dataset/dem_filled.tif"",
""output_name"": ""flow_direction.tif""
}
},
{
""tool"": ""compute_flow_accumulation"",
""arguments"": {
""flow_direction_path"": ""./output\\flow_direction.tif"",
""output_name"": ""flow_accumulation.tif"",
""weight_raster_path"": null
}
},
{
""tool"": ""extract_stream_network"",
""arguments"": {
""flowacc_path"": ""./output\\flow_accumulation.tif"",
""output_name"": ""streamnet.tif"",
""threshold"": 100
}
},
{
""tool"": ""compute_stream_order"",
""arguments"": {
""stream_raster_path"": ""./output\\streamnet.tif"",
""flow_direction_path"": ""./output\\flow_direction.tif"",
""output_name"": ""Stream_Order_Shreve.tif"",
""method"": ""shreve""
}
},
{
""tool"": ""visualize_raster"",
""arguments"": {
""input_path"": ""./output\\Stream_Order_Shreve.tif"",
""output_name"": ""Shreve_Order.png"",
""bands"": null,
""cmap"": ""jet"",
""stretch"": true,
""title"": ""Shreve Stream Order (Magnitude)"",
""legend"": true,
""legend_label"": ""Stream Magnitude"",
""vmin"": null,
""vmax"": null
}
}
]",Shreve_Order.png,Stream_Order_Shreve.tif,,,,,,,,,,
52,Hydrological Analysis,"Your task is delineating watershed basins to identify independent catchment areas within the study region. Using the D8 flow direction raster, partition the landscape into distinct basins where all connected cells drain to a common outlet. Finally, visualize the spatial relationship between the hydrological units and the river system by creating a multi-layer map. Use the generated basin raster as the semi-transparent base layer (using the 'tab20b' colormap) and overlay the provided vector stream network on top. Save the combined visualization as 'Watershed_Basins.png'.","dataset/flow_direction.tif: Raster data (D8 Flow Direction) used to trace flow paths and determine basin boundaries.
dataset/streamfea.shp: Vector line data (Stream Network) representing the river system, used for overlay visualization.","Watershed Basins style: {""cmap"": ""tab20b"", ""alpha"": 0.8, ""label"": ""Watershed Basins""}
Stream Network style: {""color"": ""black"", ""linewidth"": 0.8, ""label"": ""Stream Network""}",4,"[
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/flow_direction.tif""
}
},
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/streamfea.shp""
}
},
{
""tool"": ""compute_watershed_basins"",
""arguments"": {
""flow_direction_path"": ""dataset/flow_direction.tif"",
""output_name"": ""basin.tif""
}
},
{
""tool"": ""create_multilayer_map"",
""arguments"": {
""layers"": [
{
""data"": ""D:\\study_up\\geobench\\output\\basin.tif"",
""type"": ""raster"",
""style"": {
""cmap"": ""tab20b"",
""alpha"": 0.8,
""label"": ""Watershed Basins""
}
},
{
""data"": ""dataset/streamfea.shp"",
""type"": ""vector"",
""style"": {
""color"": ""black"",
""linewidth"": 0.8,
""label"": ""Stream Network""
}
}
],
""output_name"": ""Watershed_Basins.png"",
""title"": ""Watershed Basins with Stream Network"",
""legend_loc"": ""best""
}
}
]",Watershed_Basins.png,"basin.tif
streamfea.shp",,,,,,,,,,
53,Raster Spatial Analysis,"Your task is performing a random forest prospectivity analysis for tin-tungsten deposits in Tasmania. The analysis should focus on training and evaluating a model using tools on some mineral ocurrence point data and geo-related evidence raster layers. Your goal is to load and inspect the data, build a random forest classifier model to calculate the performance metrics. The final auc should be at least 0.9.","dataset/sn_w_minoccs.gpkg: gpkg file storing point data of mineral ocurrence, similar to point Geojson file.
Several GeoTIFF files with geo-related evidence raster layers:
dataset/tasgrav_IR_1VD.tif
dataset/tasgrav_IR.tif
dataset/tasmag_TMI_1VD.tif
dataset/tasmag_TMI.tif
dataset/tasrad_K_pct.tif
dataset/tasrad_Th_ppm.tif
dataset/tasrad_U_ppm.tif",,15,"[
{
""tool"": ""get_vector_info"",
""arguments"": {
""vector_path"": ""dataset/sn_w_minoccs.gpkg""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasgrav_IR_1VD.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasgrav_IR.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasmag_TMI_1VD.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasmag_TMI.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasrad_K_pct.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasrad_Th_ppm.tif""
}
},
{
""tool"": ""get_raster_info"",
""arguments"": {
""raster_path"": ""dataset/tasrad_U_ppm.tif""
}
},
{
""tool"": ""extract_raster_variables"",
""arguments"": {
""raster_paths"": [
""dataset/tasgrav_IR_1VD.tif"",
""dataset/tasgrav_IR.tif"",
""dataset/tasmag_TMI_1VD.tif"",
""dataset/tasmag_TMI.tif"",
""dataset/tasrad_K_pct.tif"",
""dataset/tasrad_Th_ppm.tif"",
""dataset/tasrad_U_ppm.tif""
],
""output_name"": ""data"",
""band"": 1
}
},
{
""tool"": ""rasterize_vector_labels"",
""arguments"": {
""point_path"": ""dataset/sn_w_minoccs.gpkg"",
""metadata_path"": ""./output\\data_metadata.pkl"",
""output_name"": ""data_labels.npy"",
""buffer_meters"": 1000.0,
""label_value"": 1,
""fill_value"": 0
}
},
{
""tool"": ""undersample_raster_data"",
""arguments"": {
""data_path"": ""./output\\data.npy"",
""labels_path"": ""./output\\data_labels.npy"",
""metadata_path"": ""./output\\data_metadata.pkl"",
""output_name"": ""data"",
""random_state"": 42
}
},
{
""tool"": ""split_train_test"",
""arguments"": {
""csv_path"": ""./output\\data_undersampled.csv"",
""output_name"": ""data"",
""label_column"": ""label"",
""test_size"": 0.3,
""random_state"": 42,
""stratify"": true,
""index"": false
}
},
{
""tool"": ""train_random_forest_model"",
""arguments"": {
""X_train_path"": ""./output\\data_X_train.csv"",
""output_name"": ""data"",
""y_train_path"": ""./output\\data_y_train.csv"",
""label_column"": ""label"",
""n_estimators"": 100,
""max_depth"": null,
""random_state"": 42
}
},
{
""tool"": ""generate_model_predictions"",
""arguments"": {
""model_path"": ""./output\\data.joblib"",
""X_test_path"": ""./output\\data_X_test.csv"",
""output_name"": ""data"",
""output_type"": ""proba"",
""pred_column"": ""y_pred""
}
},
{
""tool"": ""evaluate_classification_auc"",
""arguments"": {
""labels_path"": ""./output\\data_y_test.csv"",
""predictions_path"": ""./output\\data_y_proba.csv"",
""output_name"": ""data"",
""label_column"": ""label"",
""class_index"": 1
}
}
]",CHECK:JSON_VALUE:*rf_auc_metrics.json:roc_auc:>=0.9,data_rf_auc_metrics.json,,,,,,,,,, |