A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data

Xucai Zhang,Yeran Sun,Anyao Zheng,Yu Wang

doi:10.3390/ijgi9020124

Xucai Zhang, Yeran Sun + Show 2 more

Open Access

https://doi.org/10.3390/ijgi9020124

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Abstract

The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shopping, eating, etc. The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing technology. To address this issue, we used a new social sensing approach to classify land use according to human mobility and activity patterns. Previous studies used other social sensing approaches to predict land use types at the parcel or the area level, whilst fine-grained point-of-interest (POI)-level land use data are likely to more useful in urban planning. To abridge this research gap, we proposed a new social sensing approach dedicated to classifying land use at a finer scale (i.e., POI-level or building level) according to human mobility and activity patterns reflected by location-based social network (LBSN) data. Specifically, we firstly investigated spatial and temporal patterns of human mobility and activity behavior using check-in data from a popular Chinese LBSN named Sina Weibo and subsequently applied those patterns to predicting the category of POI to refine urban land use classification in Guangzhou, China. In this study, we applied three classification methods (i.e., naive Bayes, support vector machines, and random forest) to recognize category of a certain POI by spatial and temporal features of human mobility and activity behavior as well as POIs’ locational characteristics. Random forest outperformed the other two methods and obtained an overall accuracy of 72.21%. Apart from that, we compared the results of the different rules in filtering check-in samples. The comparison results show that a reasonable rule to select samples is essential for predicting the category of POI. Moreover, the approach proposed in this study can be potentially applied to identifying functions of buildings according to visitors’ mobility and activity behavior and buildings’ locational characteristics.

Highlights

In order to develop a reasonable and desirable policy for improving the city structure and the city resource allocation to support the city sustainable development, urban planners and policy makers must improve their understanding of land use distribution that influence people’s real-time activity pattern
The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing
We mapped four Points of Interest (POIs) categories in four main urban districts of Guangzhou (Liwan, Haizhu, Yuexiu, Tianhe) by using the no-further-process dataset, wherein the POI set only represents POI of storing check-in records, the result of visualization shown as Figure 7

Summary

Introduction

In order to develop a reasonable and desirable policy for improving the city structure and the city resource allocation to support the city sustainable development, urban planners and policy makers must improve their understanding of land use distribution that influence people’s real-time activity pattern. Within different POIs, people may demonstrate different movements (e.g., in residential POI, people may check-in when they get up or leave home in the morning and come back home or watch TV in the evening, whereas, in shopping center POI, people may check-in more when they undertake shopping or entertainment activities in the evening or weekend) This may allow us to investigate and refine the internal human activity structure of certain big functional areas, where a number of similar POIs are highly mixed in, by location-based social network (LBSN) data. The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing (e.g., commercial land consists of different functional areas such as eating, hotel, entertainment, etc.) In this case, conventional parcel- or area-level land use classifications cannot satisfy the demand of modern urban planning [17]. Once the Gini(Xi) was calculated for each candidate explanatory variable, the variable with the lowest Gini impurity index was selected to split the samples

Random Forest

Input space feature space

Result and Discussion

Findings

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