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.github/workflows/sync-hf.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://giswqs:$HF_TOKEN@huggingface.co/spaces/giswqs/solara-geospatial main

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

Dockerfile

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+FROM jupyter/base-notebook:latest
+
+<<<<<<< HEAD
+RUN mamba install -c conda-forge leafmap geopandas localtileserver ipyleaflet ipywidgets -y && \
+=======
+RUN mamba install -c conda-forge leafmap geopandas localtileserver ipyleaflet -y && \
+>>>>>>> origin/main
+    fix-permissions "${CONDA_DIR}" && \
+    fix-permissions "/home/${NB_USER}"
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+<<<<<<< HEAD
+WORKDIR /home/${NB_USER}
+COPY /pages ./pages
+=======
+# Dir
+WORKDIR /home/${NB_USER}
+COPY pages ./pages
+>>>>>>> origin/main
+COPY data ./data
+
+ENV PROJ_LIB='/opt/conda/share/proj'
+
+USER root
+RUN chown -R ${NB_UID} ${HOME}
+USER ${NB_USER}
+
+EXPOSE 8765
+
+CMD ["solara", "run", "./pages", "--host=0.0.0.0"]
+

LICENSE

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+MIT License
+
+Copyright (c) 2023 Open Geospatial Solutions
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,11 +1,17 @@
 ---
-title: Servicios Apoyo
-emoji: 📊
-colorFrom: pink
-colorTo: yellow
+title: Solara Geospatial
+emoji: 🌍
+colorFrom: green
+colorTo: red
 sdk: docker
 pinned: false
 license: mit
+app_port: 8765
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Introduction
+
+A geospatial dashboard to understand the provision of support services for people in conditions of human mobility and its dynamics of concentration in the urban destination or transit within Ecuador. 
+
+- Diversa Website: <https://giswqs-solara-geospatial.hf.space>
+- Hugging Face: <https://huggingface.co/spaces/DiversaStudio/Servicios_apoyo>

Servicios_apoyo/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Servicios_apoyo/README.md

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+---
+title: Servicios Apoyo
+emoji: 📊
+colorFrom: pink
+colorTo: yellow
+sdk: docker
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

data/color_codes.csv

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+Categoria,Tipo,Color
+apoyo,iglesia,#7400B8
+apoyo,abogado,#6930C3
+apoyo,centro de apoyo,#5E60CE
+apoyo,albergue,#5390D9
+apoyo,ong,#4EA8DE
+apoyo,centro social,#48BFE3
+apoyo,acnur,#56CFE1
+apoyo,punto de acceso de emergencia,#64DFDF
+apoyo,warmi,#72EFDD
+centros de oportunidades laborales,"comercios, servicios y oficios",#7400B8
+centros de oportunidades laborales,"restaurantes, cafés y bebidas",#6930C3
+educación,escuela,#7400B8
+educación,colegio,#6930C3
+educación,universidad,#5E60CE
+educación,centro de formación técnica,#5390D9
+educación,jardín de infantes,#4EA8DE
+educación,centro de educación continua,#48BFE3
+educación,centros de formación técnica,#56CFE1
+espacio público,parques y naturaleza,#7400B8
+espacio público,deportes y recreación,#6930C3
+espacio público,turismo y cultura,#5E60CE
+espacio público,centro comunitario,#5390D9
+finanzas,banco,#7400B8
+finanzas,cajero automático,#6930C3
+finanzas,cooperativa,#5E60CE
+finanzas,casa de cambio,#5390D9
+finanzas,banco del barrio,#4EA8DE
+gobierno central,policia nacional y organismos de control,#7400B8
+gobierno central,bomberos,#6930C3
+gobierno central,ministerios,#5E60CE
+gobierno central,embajada,#5390D9
+gobierno central,instituciones de protección de derechos,#4EA8DE
+gobierno central,consulado,#48BFE3
+gobierno central,servicios públicos,#56CFE1
+gobierno central,secretarías,#64DFDF
+gobierno descentralizado,gad municipal,#7400B8
+gobierno descentralizado,servicios públicos,#6930C3
+gobierno descentralizado,instituciones de protección de derechos,#5E60CE
+gobierno descentralizado,junta cantonal de proteccion de derechos,#5390D9
+gobierno descentralizado,centro de información de movilidad humana,#4EA8DE
+gobierno descentralizado,gad provincial,#48BFE3
+gobierno descentralizado,policia nacional y organismos de control,#56CFE1
+gobierno descentralizado,centro de desarollo empresarial,#64DFDF
+gobierno descentralizado,distrito de educación,#72EFDD
+gobierno descentralizado,juntas parroquiales,#80FFDB
+hospedaje,hotel,#7400B8
+hospedaje,hostal,#6930C3
+necesidades básicas,lavandería,#7400B8
+necesidades básicas,oficina postal,#6930C3
+necesidades básicas,baño público y acceso a agua,#5E60CE
+salud,farmacia,#7400B8
+salud,consultorios,#6930C3
+salud,clínica,#5E60CE
+salud,hospital,#5390D9
+salud,centro de salud,#4EA8DE
+salud,cruz roja,#48BFE3
+salud,hogar de enfermería,#56CFE1
+salud,estación de ambulancia,#64DFDF
+transporte,parada de buses,#7400B8
+transporte,gasolinera,#6930C3
+transporte,agencia de viajes,#5E60CE
+transporte,terminal terrestre,#5390D9
+transporte,parada de taxis,#4EA8DE
+transporte,estación de tren,#48BFE3

pages/Servicios_apoyo.py

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+#Libraries
+import solara
+<<<<<<< HEAD
+import solara.website
+from ipyleaflet import Map, GeoJSON, GeoData, LayersControl, Marker, MarkerCluster, basemaps, Choropleth, ZoomControl, LayerGroup, CircleMarker, Popup, LegendControl
+from ipywidgets import HTML
+from branca.colormap import linear
+import plotly.express as px
+import matplotlib
+import seaborn as sns
+=======
+from ipyleaflet import Map, GeoJSON, GeoData, LayersControl, Marker, MarkerCluster, basemaps, Choropleth
+from branca.colormap import linear
+import plotly.express as px
+import matplotlib
+>>>>>>> origin/main
+import geopandas as gpd
+import pandas as pd
+import numpy as np
+from shapely import wkt
+import json
+import re
+<<<<<<< HEAD
+from pathlib import Path
+
+#Read and clean Pois Data Set
+df = pd.read_csv('./data/pois_final_control.csv')
+df = df[(df['EnEstudio'] == True)]
+df = df[df['Geometry'].notnull()]
+df['Tipo'] = df['Tipo'].str.lower()
+df['Tipo'] = df['Tipo'].replace('terminal terrestre de buses', 'terminal terrestre')
+df['Tipo'] = df['Tipo'].replace('comercios,servicios y oficios', 'comercios, servicios y oficios')
+df['Tipo'] = df['Tipo'].replace('alimentos y bebidas', 'restaurantes, cafés y bebidas')
+excluded_cantons = ['HUACA', 'EL ORO', 'TUNGURAHUA', 'MIRA', 'MONTUFAR', 'BOLIVAR', 'SAN GABRIEL']
+df = df[~df['Canton'].isin(excluded_cantons)]
+df['geometry'] = df['Geometry'].apply(wkt.loads)
+#Color codes
+color_codes = pd.read_csv('./data/color_codes.csv')
+df = pd.merge(df, color_codes, on=['Categoria', 'Tipo'], how='left')
+#Make a geo dataframe
+=======
+
+#Read Pois Data Set
+df = pd.read_csv('./data/pois_final_control.csv')
+df = df[(df['EnEstudio'] == True)]
+df = df[df['Geometry'].notnull()]
+df['geometry'] = df['Geometry'].apply(wkt.loads)
+>>>>>>> origin/main
+pois = gpd.GeoDataFrame(df, geometry='geometry')
+pois = pois.set_crs(epsg=4326, inplace=True)
+pois = pois.drop('EnEstudio', axis=1)
+pois = pois.drop('Geometry', axis=1)
+
+#Read H3 Data Set
+df1 = pd.read_csv('./data/poiscount_r8hex_large.csv', index_col=0)
+df1 = df1[df1['Conteo'] > 0]
+df1 = df1.drop('EnEstudio', axis=1)
+<<<<<<< HEAD
+df1['Canton'] = df1['Canton'].replace('RUMIÑAHUI', 'RUMINAHUI')
+=======
+>>>>>>> origin/main
+df1['geometry'] = df1['geometry'].apply(wkt.loads)
+h3 = gpd.GeoDataFrame(df1, geometry='geometry')
+h3 = h3.set_crs(epsg=4326, inplace=True)
+
+#Set controls values
+cantones = [str(k) for k in pois['Canton'].unique().tolist()]
+categorias = [str(k) for k in pois['Categoria'].unique().tolist()]
+<<<<<<< HEAD
+canton = solara.reactive('GUAYAQUIL')
+categoria = solara.reactive('salud')
+tipo = solara.reactive([])
+limit = solara.reactive(1000)
+
+#Images path
+logo1 = "./data/logo_diversa.png"
+logo2 = "./data/logo_giz.png"
+=======
+canton = solara.reactive('QUITO')
+categoria = solara.reactive('salud')
+limit = solara.reactive(2500)
+>>>>>>> origin/main
+
+#Filter function
+def data_filter(df, city, category):
+    df_filtered = df.loc[df['Canton']==city]
+<<<<<<< HEAD
+    df_filtered = df_filtered[df_filtered['Categoria']==category]   
+    return df_filtered
+
+#Dynamic filter function
+def dynamic_filter(df, types):
+    return df[df['Tipo'].isin(types)]
+    
+=======
+    df_filtered = df_filtered[df_filtered['Categoria']==category]
+    return df_filtered
+
+>>>>>>> origin/main
+#Map function
+def ipyleaflet_map(geo_df, h3_df, h3_geojson):
+    # Define map center and initial zoom level
+    center = (geo_df.geometry.y.mean(), geo_df.geometry.x.mean())
+<<<<<<< HEAD
+    zoom = 12
+    # Create the map
+    m = Map(center=center, 
+            zoom=zoom, zoom_control=False, scroll_wheel_zoom=True,
+            basemap=basemaps.CartoDB.Positron)
+    m.add(ZoomControl(position='bottomleft'))
+    
+    # Initialize a LayerGroup to hold all CircleMarkers
+    markers_layer = LayerGroup(name='Servicios')
+
+    # Iterate over geo_df rows to create CircleMarker for each point with dynamic fillColor
+    for index, row in geo_df.iterrows():
+        marker = CircleMarker()
+        marker.location = (row.geometry.y, row.geometry.x)
+        marker.radius = 3
+        marker.color = row['Color']
+        marker.fill_color = row['Color']  
+        marker.fill_opacity = 0.65
+        marker.weight = 1
+        marker.popup = HTML("<b>Nombre:</b> " + str(row['Nombre']) + "<br>" +
+                    "<b>Tipo:</b> " + str(row['Tipo']) + "<br>" +
+                    "<b>Categoría:</b> " + str(row['Categoria']))
+        markers_layer.add_layer(marker)
+
+    # Add the LayerGroup to the map
+    m.add_layer(markers_layer)
+       
+    #h3 Density
+    value_min = min(h3_df['Conteo'])
+    value_max = max(h3_df['Conteo'])
+    value_med = int(np.median(h3_df['Conteo']))
+    legend_labels = {
+    f"Min: {value_min}": "#FFEDA0",
+    f"Med: {value_med}": "#FEB24C", 
+    f"Max: {value_max}": "#F03B20"}
+    legend = LegendControl(legend_labels, title="Concentración de servicios", position="topright")
+    m.add(legend)
+    if value_min == value_max:
+        value_max += 1 
+    colormap = linear.YlOrRd_04.scale(value_min, value_max)
+    choro_data = dict(zip(h3_df.index.astype(str), h3_df['Conteo']))
+        
+=======
+    zoom = 11
+    # Create the map
+    m = Map(center=center, zoom=zoom)
+    
+    # Create GeoData from the GeoDataFrame
+    services = GeoData(
+        geo_dataframe=geo_df,
+        style={
+            'color': 'black', 
+            'radius': 7, 
+            'fillColor': '#3366cc', 
+            'opacity': 0.5, 
+            'weight': 1.9, 
+            'dashArray': '2', 
+            'fillOpacity': 0.6
+        },
+        hover_style={
+            'fillColor': 'red', 
+            'fillOpacity': 0.2
+        },
+        point_style={
+            'radius': 7, 
+            'color': 'red', 
+            'fillOpacity': 0.8, 
+            'fillColor': 'blue', 
+            'weight': 3
+        },
+        name='Servicios de Apoyo'
+    )
+    
+    #h3 Density 
+    colormap = linear.YlOrRd_04.scale(min(h3_df['Conteo']), max(h3_df['Conteo']))
+    choro_data = dict(zip(h3_df.index.astype(str), h3_df['Conteo']))
+>>>>>>> origin/main
+    h3_choropleth = Choropleth(
+        geo_data=h3_geojson,
+        choro_data=choro_data,
+        colormap=colormap,
+        border_color='black',
+<<<<<<< HEAD
+        style={'fillOpacity': 0.85},#, 'dashArray': '5, 5'},
+        #hover_style={"fillColor": "red"},
+        name='Densidad'
+    )
+    
+    # Add h3 layer to the map
+=======
+        style={'fillOpacity': 0.8, 'dashArray': '5, 5'},
+        name='Densidad'
+    )
+
+    # Add GeoData to the map
+    m.add_layer(services)
+>>>>>>> origin/main
+    m.add_layer(h3_choropleth)
+    
+    # Add Cluster Markers
+    markers = [Marker(location=(point.y, point.x)) for point in geo_df.geometry]
+
+    # Create and add MarkerCluster to the map
+<<<<<<< HEAD
+    marker_cluster = MarkerCluster(markers=markers, name='Cluster')
+    m.add_layer(marker_cluster)
+
+    # Add layer control
+    m.add_control(LayersControl(position='topleft', collapsed=False))
+=======
+    marker_cluster = MarkerCluster(markers=markers, name='Cluster Markers')
+    m.add_layer(marker_cluster)
+
+    # Add layer control
+    m.add_control(LayersControl())
+>>>>>>> origin/main
+
+    # Display the map
+    display(m)
+
+#Interactive bar chart function
+def type_chart_plotly(df):
+<<<<<<< HEAD
+    tipo_counts = df.groupby('Tipo')['Color'].first()
+    counts = df['Tipo'].value_counts().sort_values(ascending=False)
+    plot_df = counts.rename_axis('Tipo').reset_index(name='Counts')
+    plot_df['Color'] = plot_df['Tipo'].map(tipo_counts)
+    fig = px.bar(plot_df, orientation='h', 
+                 x='Counts', y='Tipo',
+                 labels={'Counts': 'Conteo', 'Tipo': 'Tipo'},
+                 title='Conteo',
+                 color='Tipo',  # Use 'Tipo' for color mapping
+                 color_discrete_map=tipo_counts.to_dict())
+    fig.update_layout(showlegend=False, xaxis_title=None, height=450) 
+    solara.FigurePlotly(fig)
+
+def type_chart_plotly_donut(df):
+    # Count the occurrences of each 'Tipo'
+    tipo_counts = df['Tipo'].value_counts().reset_index(name='Counts')
+    tipo_counts.rename(columns={'index': 'Tipo'}, inplace=True)
+    
+    # Get the first color for each 'Tipo' for coloring the pie chart
+    colors = df.groupby('Tipo')['Color'].first().reindex(tipo_counts['Tipo']).to_dict()
+    
+    # Create the pie (donut) chart
+    fig = px.pie(tipo_counts, names='Tipo', values='Counts',
+                 title='Porcentaje',
+                 color='Tipo',
+                 color_discrete_map=colors,
+                 hole=0.6)  # Adjust the hole size to create a donut chart
+    
+    # Optional: Customize chart appearance
+    fig.update_traces(textinfo='percent')
+    fig.update_layout(showlegend=False, height=450)
+    solara.FigurePlotly(fig)
+  
+#Main panel layout
+@solara.component
+def View():
+    global tipo
+    dff = data_filter(pois, canton.value, categoria.value)
+    if tipo.value:  # Check if 'tipo' is not empty
+        dff = dynamic_filter(dff, tipo.value)
+=======
+    tipo_counts = df['Tipo'].value_counts().sort_values(ascending=True)
+
+    fig = px.bar(tipo_counts, orientation='h', 
+                 x=tipo_counts.values, y=tipo_counts.index,
+                 labels={'x': 'Conteo', 'y': 'Tipo'},
+                 title='Servicios')
+    solara.FigurePlotly(fig)
+    
+#Main panel layout
+@solara.component
+def View():
+    dff = data_filter(pois, canton.value, categoria.value)
+>>>>>>> origin/main
+    h3f = data_filter(h3, canton.value, categoria.value)
+    geo_json_H3 = json.loads(h3f.to_json())
+    row_count = len(dff)
+    if row_count > limit.value:
+<<<<<<< HEAD
+        solara.Warning(f"En el mapa sólo se muestran {limit.value} de {row_count:,} puntos")
+    with solara.Card(""):
+        ipyleaflet_map(dff.iloc[:limit.value], h3f, geo_json_H3)
+    if row_count > 0:
+        with solara.Columns([1.75, 1.25]):
+            with solara.Card(""):
+                type_chart_plotly(dff)
+            with solara.Card(""):    
+                type_chart_plotly_donut(dff)
+    else:
+        solara.Warning("Has filtrado todos los datos, no se muestran gráficos")
+    
+@solara.component
+def Controls():
+    global tipo  # Refe
+    solara.Select('Cantón', values=cantones, value=canton)
+    solara.Select('Categoría', values=categorias, value=categoria)
+    # Dynamically update 'tipos' based on the current selection of 'canton' and 'categoria'
+    dff = data_filter(pois, canton.value, categoria.value)
+    tipos = [str(k) for k in dff['Tipo'].unique().tolist()]
+    solara.SelectMultiple('Tipo', all_values=tipos, values=tipo)   
+    solara.Text("Número máximo de puntos a mostrar en el mapa")
+=======
+        solara.Warning(f"Only showing the first {limit.value} of {row_count:,} services on map")
+    ipyleaflet_map(dff.iloc[:limit.value], h3f, geo_json_H3)
+    if row_count > 0:
+        with solara.Card(''):
+            type_chart_plotly(dff)
+    else:
+        solara.Warning("You filtered out all the data, no charts shown")
+        
+#Controls
+@solara.component
+def Controls():
+    solara.Select('Cantón', values=cantones, value=canton)
+    solara.Select('Categoría', values=categorias, value=categoria)
+    #solara.SelectMultiple('Fuente', all_values=fuentes, values=fuente)
+    solara.Text("Maximum number of pois to show on map")
+>>>>>>> origin/main
+    solara.SliderInt('', value=limit, min=1, max=5000)
+    
+#Dashboard Layout
+@solara.component
+def Page():
+    with solara.Column():
+<<<<<<< HEAD
+        solara.Title('Servicios de apoyo para Movilidad Humana')
+        with solara.Sidebar():
+            with solara.Card(elevation=0.1):
+                with solara.Row(gap="0px", justify="space-around"):
+                    solara.Image(logo1, width='55%')
+                    solara.Image(logo2, width='50%')
+                solara.Markdown('Este panel interactivo, fruto de la colaboración entre [Diversa Studio](https://diversa.studio/) y GIZ, busca ayudar a personas en condición de movilidad humana y al planteamiento de acciones de protección.\n'
+                                '\n'
+                                'Aquí se puede explorar servicios de apoyo para la movilidad humana en diversos cantones del Ecuador. Visualice la distribución de los servicios, analice las agrupaciones y obtenga información a través de mapas y gráficos interactivos.')
+=======
+        solara.Title('Servicios de Apoyo para Migrantes')
+        with solara.Sidebar():
+>>>>>>> origin/main
+            with solara.Card('Filtros'):
+                with solara.Column():
+                    Controls()
+        View()
+Page()

requirements.txt

@@ -0,0 +1,20 @@
+solara
+<<<<<<< HEAD
+=======
+geemap>=0.21.0
+>>>>>>> origin/main
+leafmap
+mapwidget
+geopandas
+pandas
+<<<<<<< HEAD
+numpy
+plotly
+matplotlib
+seaborn
+=======
+plotly
+matplotlib
+>>>>>>> origin/main
+shapely
+pydantic<2.0

testing_nb.ipynb

@@ -0,0 +1,427 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import solara\n",
+    "from ipyleaflet import Map, GeoJSON, GeoData, LayersControl, Marker, MarkerCluster\n",
+    "import plotly.express as px\n",
+    "import matplotlib\n",
+    "import geopandas as gpd\n",
+    "import pandas as pd\n",
+    "from shapely import wkt\n",
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_wkt_point(geometry_str):\n",
+    "    # Maneja casos de puntos vacíos\n",
+    "    if geometry_str == 'POINT ()':\n",
+    "        return None  # O puedes devolver un valor predeterminado, si lo prefieres\n",
+    "\n",
+    "    # Corrige el formato para casos con espacios o comas mal colocados y espacio extra después de \"POINT (\"\n",
+    "    corrected_str = re.sub(r\"POINT\\s*\\(\\s*(\\-?\\d+(\\.\\d+)?)\\s*,\\s*(\\-?\\d+(\\.\\d+)?)\\s*\\)\", r\"POINT (\\1 \\3)\", geometry_str)\n",
+    "\n",
+    "    # Caso especial para registros con comas entre coordenadas sin espacio adecuado\n",
+    "    corrected_str = re.sub(r\"POINT\\(\\s*(\\-?\\d+(\\.\\d+)?),(\\-?\\d+(\\.\\d+)?)\\s*\\)\", r\"POINT (\\1 \\3)\", corrected_str)\n",
+    "\n",
+    "    # Caso especial para registros como 'POINT (-77.74394625316971)0.7859743813128189, '\n",
+    "    corrected_str = re.sub(r\"POINT\\s*\\((\\-?\\d+(\\.\\d+)?)\\)(\\-?\\d+(\\.\\d+)?),\", r\"POINT (\\1 \\3)\", corrected_str)\n",
+    "\n",
+    "    # Verifica si la cadena corregida tiene el formato correcto\n",
+    "    if re.match(r\"POINT \\(\\-?\\d+(\\.\\d+)? \\-?\\d+(\\.\\d+)?\\)\", corrected_str):\n",
+    "        return corrected_str\n",
+    "    \n",
+    "    # Devuelve None si la cadena no se puede corregir\n",
+    "    return None\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "77098\n",
+      "77098\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Nombre</th>\n",
+       "      <th>Geometry</th>\n",
+       "      <th>Tipo</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Canton</th>\n",
+       "      <th>Provincia</th>\n",
+       "      <th>Source</th>\n",
+       "      <th>Observación</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Produbanco-Servipagos</td>\n",
+       "      <td>POINT (-79.6516941 0.9651532999999998)</td>\n",
+       "      <td>cajero automático</td>\n",
+       "      <td>finanzas</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>POINT (-79.65169 0.96515)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ATM Banco Bolivariano</td>\n",
+       "      <td>POINT (-79.671904 0.9323994000000002)</td>\n",
+       "      <td>cajero automático</td>\n",
+       "      <td>finanzas</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>POINT (-79.67190 0.93240)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CPN</td>\n",
+       "      <td>POINT (-79.6617982 0.9326862999999997)</td>\n",
+       "      <td>cajero automático</td>\n",
+       "      <td>finanzas</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>POINT (-79.66180 0.93269)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Banco Solidario</td>\n",
+       "      <td>POINT (-79.65241619999999 0.9639383999999998)</td>\n",
+       "      <td>banco</td>\n",
+       "      <td>finanzas</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>POINT (-79.65242 0.96394)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Agencia BGR Esmeraldas |Banco General Rumiñahui</td>\n",
+       "      <td>POINT (-79.65119609999999 0.9630196000000001)</td>\n",
+       "      <td>banco</td>\n",
+       "      <td>finanzas</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>ESMERALDAS</td>\n",
+       "      <td>Google</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>POINT (-79.65120 0.96302)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            Nombre  \\\n",
+       "                                                     \n",
+       "1                            Produbanco-Servipagos   \n",
+       "2                            ATM Banco Bolivariano   \n",
+       "3                                              CPN   \n",
+       "4                                  Banco Solidario   \n",
+       "5  Agencia BGR Esmeraldas |Banco General Rumiñahui   \n",
+       "\n",
+       "                                        Geometry               Tipo Categoria  \\\n",
+       "                                                                                \n",
+       "1         POINT (-79.6516941 0.9651532999999998)  cajero automático  finanzas   \n",
+       "2          POINT (-79.671904 0.9323994000000002)  cajero automático  finanzas   \n",
+       "3         POINT (-79.6617982 0.9326862999999997)  cajero automático  finanzas   \n",
+       "4  POINT (-79.65241619999999 0.9639383999999998)              banco  finanzas   \n",
+       "5  POINT (-79.65119609999999 0.9630196000000001)              banco  finanzas   \n",
+       "\n",
+       "       Canton   Provincia  Source Observación                   geometry  \n",
+       "                                                                          \n",
+       "1  ESMERALDAS  ESMERALDAS  Google         NaN  POINT (-79.65169 0.96515)  \n",
+       "2  ESMERALDAS  ESMERALDAS  Google         NaN  POINT (-79.67190 0.93240)  \n",
+       "3  ESMERALDAS  ESMERALDAS  Google         NaN  POINT (-79.66180 0.93269)  \n",
+       "4  ESMERALDAS  ESMERALDAS  Google         NaN  POINT (-79.65242 0.96394)  \n",
+       "5  ESMERALDAS  ESMERALDAS  Google         NaN  POINT (-79.65120 0.96302)  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('data/pois_final.csv', index_col=0, low_memory=False)\n",
+    "df = df[(df['EnEstudio'] == 'TRUE') | (df['EnEstudio'] == 'VERDADERO')]\n",
+    "df['Geometry'] = df['Geometry'].apply(normalize_wkt_point)\n",
+    "print(len(df))\n",
+    "df = df[df['Geometry'].notnull()]\n",
+    "print(len(df))\n",
+    "df['geometry'] = df['Geometry'].apply(wkt.loads)\n",
+    "pois = gpd.GeoDataFrame(df, geometry='geometry')\n",
+    "pois = pois.set_crs(epsg=4326, inplace=True)\n",
+    "pois = pois.drop('EnEstudio', axis=1)\n",
+    "pois = pois.drop('Geometry', axis=1)\n",
+    "pois.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cantones = [str(k) for k in pois['Canton'].unique().tolist()]\n",
+    "categorias = [str(k) for k in pois['Categoria'].unique().tolist()]\n",
+    "fuentes = [str(k) for k in pois['Source'].unique().tolist()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "canton = solara.reactive('CUENCA')\n",
+    "categoria = solara.reactive(['Salud'])\n",
+    "fuente = solara.reactive(['OSM'])\n",
+    "limit = solara.reactive(1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def data_filter(df, city, category, source):\n",
+    "    df_filtered = df.loc[df['Canton']==city]\n",
+    "    df_filtered = df_filtered.loc[df_filtered['Categoria'].isin(category)]\n",
+    "    df_filtered = df_filtered.loc[df_filtered['Source'].isin(source)]\n",
+    "    return df_filtered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ipyleaflet_map(geo_df):\n",
+    "    # Define map center and initial zoom level\n",
+    "    center = (geo_df.geometry.y.mean(), geo_df.geometry.x.mean())\n",
+    "    zoom = 12\n",
+    "\n",
+    "    # Create the map\n",
+    "    m = Map(center=center, zoom=zoom)\n",
+    "\n",
+    "    # Create GeoData from the GeoDataFrame\n",
+    "    geo_data = GeoData(\n",
+    "        geo_dataframe=geo_df,\n",
+    "        style={\n",
+    "            'color': 'black', \n",
+    "            'radius': 7, \n",
+    "            'fillColor': '#3366cc', \n",
+    "            'opacity': 0.5, \n",
+    "            'weight': 1.9, \n",
+    "            'dashArray': '2', \n",
+    "            'fillOpacity': 0.6\n",
+    "        },\n",
+    "        hover_style={\n",
+    "            'fillColor': 'red', \n",
+    "            'fillOpacity': 0.2\n",
+    "        },\n",
+    "        point_style={\n",
+    "            'radius': 7, \n",
+    "            'color': 'red', \n",
+    "            'fillOpacity': 0.8, \n",
+    "            'fillColor': 'blue', \n",
+    "            'weight': 3\n",
+    "        },\n",
+    "        name='Servicios de Apoyo'\n",
+    "    )\n",
+    "\n",
+    "    # Add GeoData to the map\n",
+    "    m.add_layer(geo_data)\n",
+    "    \n",
+    "    # Add Cluster Markers\n",
+    "    markers = [Marker(location=(point.y, point.x)) for point in geo_df.geometry]\n",
+    "\n",
+    "    # Create and add MarkerCluster to the map\n",
+    "    marker_cluster = MarkerCluster(markers=markers, name='Cluster Markers')\n",
+    "    m.add_layer(marker_cluster)\n",
+    "\n",
+    "    # Add layer control\n",
+    "    m.add_control(LayersControl())\n",
+    "\n",
+    "    # Display the map\n",
+    "    display(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Interactive bar chart function\n",
+    "def type_chart_plotly(df):\n",
+    "    tipo_counts = df['Tipo'].value_counts().sort_values(ascending=True)\n",
+    "\n",
+    "    fig = px.bar(tipo_counts, orientation='h', \n",
+    "                 x=tipo_counts.values, y=tipo_counts.index,\n",
+    "                 labels={'x': 'Conteo', 'y': 'Tipo'},\n",
+    "                 title='Servicios')\n",
+    "    solara.FigurePlotly(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@solara.component\n",
+    "def View():\n",
+    "    dff = data_filter(pois, canton.value, categoria.value, fuente.value)\n",
+    "    row_count = len(dff)\n",
+    "    if row_count > limit.value:\n",
+    "        solara.Warning(f\"Only showing the first {limit.value} of {row_count:,} services on map\")\n",
+    "    ipyleaflet_map(dff.iloc[:limit.value])\n",
+    "    if row_count > 0:\n",
+    "        with solara.Card(''):\n",
+    "            type_chart_plotly(dff)\n",
+    "    else:\n",
+    "        solara.Warning(\"You filtered out all the data, no charts shown\")\n",
+    "#View()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@solara.component\n",
+    "def Controls():\n",
+    "    solara.Select('Cantón', values=cantones, value=canton)\n",
+    "    solara.SelectMultiple('Categoría', all_values=categorias, values=categoria)\n",
+    "    solara.SelectMultiple('Fuente', all_values=fuentes, values=fuente)\n",
+    "    solara.Text(\"Maximum number of pois to show on map\")\n",
+    "    solara.SliderInt('', value=limit, min=1, max=1000)\n",
+    "#Controls()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0820e0d59ee746fea8779a184f7057f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "Cannot show widget. You probably want to rerun the code cell above (<i>Click in the code cell, and press Shift+Enter <kbd>⇧</kbd>+<kbd>↩</kbd></i>)."
+      ],
+      "text/plain": [
+       "Cannot show ipywidgets in text"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "@solara.component\n",
+    "def Page():\n",
+    "    with solara.Column():\n",
+    "        solara.Title('Servicios de Apoyo para Migrantes')\n",
+    "        with solara.Sidebar():\n",
+    "            with solara.Card('Filtros'):\n",
+    "                with solara.Column():\n",
+    "                    Controls()\n",
+    "        View()\n",
+    "Page()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "solara",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}