Available data in .csv format.
Relational Table
- Key1 PersonID : unique ID per virtual people (int)
- Key2 DateID : unique ID per day (int)
- Visit Duration : number of hours that visitors were seen in the area of interest ('Duration 2h', 'Duration 3h', 'Duration 4h', 'Duration 5h', 'Duration 6h', 'Duration 7h', 'Duration 8h', 'Duration 9h', 'Duration 10h', 'Duration 10h-18h') (str)
Personal Info Table
- PersonID : unique ID per virtual people (int)
- Name: fake name (str)
- Gender : the gender (masculine, feminine) (str)
- Age : age range (less than 18, 18-24, 25-34, 35-44, 45-54, 55-64, more than 65) (str)
- geoLife : socio-category profile ('NR', 'comfortable family pavilion','growing peri-urban', 'popular', 'secondary residence', 'dynamic rural', 'rural worker', 'traditional rural', 'middle-class urban', 'low-income urban', 'dynamic urban', 'comfortable family urban') (str)
- Visitor category : resident, French or foreign tourist (str)
- Region : the region or country the visitor is from (str)
- Sleeping area : the area which the visitor slept the previous night ('Agglomeration of Belfort', 'Agglomeration of Hericourt', 'Agglomeration of Montbeliard', 'North Haut Rhin', 'South Haut Rhin', 'Rest of Doubs', 'Rest of Haute Saone', 'Rest Territory of Belfort', 'City of Belfort', 'Vosges') (str)
Dates = {(1: 2017-05-31), (2: 2017-06-01), (3: 2017-06-02), (4: 2017-06-03), (5: 2017-06-04), (6: 2017-06-05), (7: 2017-06-06)}