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 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.本数据集通过地理处理（geoprocessing）、编程与分析流程获取。本次研究所采用的升阶分层格网系统（ascending hierarchical grid system）以动态城市异质性（dynamic urban heterogeneity）理论为基础，同时兼顾数据模式（data schema）、特征要素与空间位置三大维度。数据处理工作依托Python编程库（Python programming packages）及用于地理处理与分析的QGIS工具（QGIS Tool）完成。本多学科大规模数据集采集自塔林（Tallinn）市全部8个行政区，共收录34001份建筑样本，涵盖空间位置、建筑特征、城市特征、城市热岛（Urban Heat Island, UHI）数据及气候数据。本研究提出的工作方法构建了一套数据分类框架，可将数据划分为同质/异质、静态/动态模式，并基于同质格网系统开展数据采集。在数据采集流程中应用该分层格网系统可实现两大核心功能：其一，为每个地物对象构建空间索引（spatial index），并将其关联至格网系统；其二，依托同质格网定义主要锚定异质数据的城市指数。