Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing

Disheng Yi · Jing Feng Yang · Jingjing Liu · Yusi Liu · Jing Zhang

30 citations0/0 files storedISPRS International Journal of Geo-Information

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Human Mobility and Location-Based Analysis Geographic Information Systems Studies Urban Design and Spatial Analysis

Abstract

Urban areas involve different functions that attract individuals and fit personal needs. Understanding the distribution and combination of these functions in a specific district is significant for urban development in cities. Many researchers have already studied the methods of identifying the dominant functions in a district. However, the degree of collection and the representativeness of a function in a district are controlled not only by its number in the district but also by the number outside this district and a number of other functions. Thus, this study proposed a quantitative method to identify urban functions, using Fisher’s exact test and point of interest (POI) data, applied in determining the urban districts within the Sixth Ring Road in Beijing. To begin with, we defined a functional score based on three statistical features: the p-value, odds-ratio, and the frequency of each POI tag. The p-value and odds-ratio resulted from a statistical significance test, the Fisher’s exact test. Next, we ran a k-modes clustering algorithm to classify all urban districts in accordance with the score of each function and their combination in one district, and then we detected four different groups, namely, Work and Tourism Mixed-developed district, Mixed-developed Residential district, Developing Greenland district, and Mixed Recreation district. Compared with the other identifying methods, our method had good performance in identifying functions, except for transportation. In addition, the Coincidence Degree was used to evaluate the accuracy of classification. In our study, the total accuracy of identifying urban districts was 83.7%. Overall, the proposed identifying method provides an additional method to the various methods used to identify functions. Additionally, analyzing urban spatial structure can be simpler, which has certain theoretical and practical value for urban geospatial planning.

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References

2017Extracting urban functional regions from points of interest and human activities on location‐based social networks 2015Mining point-of-interest data from social networks for urban land use classification and disaggregation 2018Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs 2018Using Spatial Semantics and Interactions to Identify Urban Functional Regions 2018Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data 2011Extracting hierarchical landmarks from urban POI data 2011Extracting hierarchical landmarks from urban POI data 2018CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery

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