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

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.

Identification of urban functions enhancement and weakening based on urban land use conversion: A case study of Changchun, China

“…With the advancement of technology, tax OD trips data [1], POI data [2], mobile phone data [7], social media data [7] and vehicle trajectory data [32] and other big data [33][34][35][36][37] are used in the identification of urban functions. Although scholars can quickly and accurately identify the types of urban functions, it is difficult to identify the enhancement and weakening of urban functions.…”

“…Moreover, several scholars performed semantic analyses on the basis of POI data to identify different urban functional divisions [33][34][35][36]. In order to avoid subjective errors in the semantic analysis of POI data, Yi et al used the Fisher's exact test to identify urban functions and districts [37]. In addition, some scholars have explored the classification of urban functions from a micro-level perspective.…”

Research on the Coupling Coordination Relationships between Urban Function Mixing Degree and Urbanization Development Level Based on Information Entropy

“…Since the reform and development of China, Beijing’s urbanization has developed rapidly, the scale of the city has continued to expand, and its urban functions have been expanded and improved. However, the urbanization development of Beijing is very unbalanced, mainly manifesting in relatively lagged development of suburban areas, a single economic structure and incomplete functions [ 28 ]. In the process of its modern urban development, Beijing, being representative of a typical city in China, had reached an 86.5% urbanization level by the end of 2018.…”

Complexity of Functional Urban Spaces Evolution in Different Aspects: Based on Urban Land Use Conversion

“…However, most of the existing literature on functional urban spaces focuses on identifying the types of these spaces using new data or methods. New data from taxi OD trips, POI, human activity, mobile phones, social media, and vehicle trajectories are used to identify urban functions [35][36][37][38][39][40][41][42]. Previous studies have focused on the identification and classification of functional urban spaces using a variety of data and methods; however, little attention has been paid to the complexity of how the use of urban space has evolved.…”