Territory analysis with spatial datasets

Abstract

This research seeks to comprehend and analyze the dynamic interplay between residents and urban spaces, emphasizing the use of geolocation data as an indicator of area quality, which, in turn, may influence property prices. Leveraging OpenStreetMap as a valuable repository of spatial data, the study aims to assist planners and decision-makers in shaping territorial development. The exploration extends to the relatively new theme of self-organizing phenomena in territorial planning and development, a concept subject to varying interpretations and applications in contemporary planning debates. The agglomeration benefits of cities, resulting from spatial concentration of activities, are also investigated, with an emphasis on understanding how cities evolve beyond their primary functions. OpenStreetMap is employed to scrutinize global geospatial data, offering empirical insights into street networks across different urban scales.

Materials and methods

Each urban structure (typology) in the city has its own characteristics determined by the character of public spaces, the scale of buildings, interrelationships and atmosphere. The data source is the open platform data.bno.cz, which accesses relevant data sets in various formats, including CSV, JSON and XML. For the purpose of comparison of the selected statistical data, I have divided the urban structure of Brno into several basic types: Compact, Block, Mixed, Modern Residential Complexes, Villas, and Family Houses — the study utilizes the Multiple-sample comparison method, specifically Analysis of Variance (ANOVA), to discern significant differences in spatial characteristics among these structures. The analysis also incorporates Fisher's Least Significant Difference (LSD) method to identify specific mean differences between different urban structures. Visual representation through Box-and-Whisker Plots enhances the interpretation of results.

Results

The actual analysis involves an in-depth examination of spatial characteristics through various indicators extracted from OpenStreetMap data. These indicators include a spectrum of factors such as road network length, serviceability index, dust pollutant concentration, population density, park area, floor area index, number of dwellings, number of houses, occupancy index, crime incidence, sales area, development area as per the master plan, resident satisfaction index, resident presence index as per mobile operator data, and number of housing offers. The results show that urban structures can generally be divided into two groups: compact or block structures and modern residential complexes, single-family houses and villas. Mixed structures tend to fit more closely with the characteristics of the second group.

Conclusions

The paper concludes that this innovative approach to utilizing open spatial data platforms contributes to transparency and innovation in municipal governance. Statistical tests reveal significant differences between structures for specific indicators, highlighting their diverse impact on the quality of life of residents. This paper sheds light on the distinctive characteristics and influences of different urban structures, offering valuable insights for urban planning and decision-making at the municipal and community levels. The detailed analysis of the interaction between residents and urban spaces contributes to informed development strategies, demonstrating the potential of innovative approaches to data collection and utilization through open spatial data platforms.