Geoprocess of geospatial urban data in Tallinn, Estonia

Data were acquired via geoprocessing, programming, and analysis.

The application of an ascending hierarchical grid system is based on the theory of dynamic urban heterogeneity and considers data schema, features, and location. Data processing was done using Python programming packages and the QGIS Tool for geoprocessing and analysis.

The extensive multidisciplinary presented dataset is collected with 34,001 building samples (34,001 raws) and 31 features (31 columns) from all 8 districts of Tallinn, including location, building characteristics, urban characteristics, UHI data, and climate data. The current work methodology proposes a framework to categorize data into homogeneous or heterogeneous, static or dynamic schemes, and then collect data considering the homogeneous grid system. The implementation of the hierarchical grid system in the data collection process helps:

First, create a spatial index for each object and connect the objects to the grid system.

Second, use the homogeneous ground to define urban indices mainly anchored in the heterogeneous data.

The methodology uses the Python and the Numpy and Pandas libraries, as well as the Geopandas package in the Python environment and QGIS Tool. The approach helps to capture urban data from GIS resources, taking into account the location, general characteristics, other specifications, and spatial properties of urban elements.