Mapping Urban Spatial Structure Based on POI (Point of Interest) Data: A Case Study of the Central City of Lanzhou, China The study of urban spatial structure is currently one of the most popular research fields in urban geography. This study uses Lanzhou, one of the major cities in Northwest China, as a case area. Using the industry classification of POI data, the nearestneighbor index, kernel density estimation, and location entropy are adopted to analyze the spatial clusteringdiscrete distribution characteristics of the overall economic geographical elements of the city center, the spatial distribution characteristics of the various industry elements, and the overall spatial structure characteristics of the city. All of these can provide a scientific reference for the sustainable optimization of urban space. The urban economic geographical elements generally present the distribution trend of center agglomeration. In respect of spatial distribution, the economic geographical elements in the central urban area of Lanzhou have obvious characteristics of central agglomeration. Many industrial elements have largescale agglomeration centers, which have formed specialized functional areas. There is a clear xe2x80x9ccentralxe2x80x93peripheralxe2x80x9d difference distribution in space, with an obvious circular structure. Generally, tertiary industry is distributed in the central area, and secondary industry is distributed in the peripheral areas. In general, a stripshaped urban spatial structure with a strong main center, weak subcenter and multiple groups is present. Improving the complexity of urban functional space is an important goal of spatial structure optimization.

Discovering Functional Regions in Modern Cities by Using User Checkin Records and POIs The rapid development of the modern city necessitates the division of different functional regions, such as educational regions and business regions. In this paper, we propose an LDA based topic model to discover regions with different functions in a city using user checkin records and PointofInterests (POIs). We infer the functions of each region using the proposed model, which regards a region as a document, a function as a topic, categories of POIs and checkin frequency as words. As a result, a region is represented by a distribution of functions, and a function is represented by the distribution of POI categories. We further identify the intensity to illustrate that our method considers both the spatial distance and checkin frequency. We evaluate our method by using a large scale dataset, consisting of two user checkin records that occurred in Los Angeles and Lower Manhattan. The experiment results justify the advantages of our proposed method over the baseline method solely using POIs or checkin records.