基于大数据的城市服务业空间关联分析

doi:10.13249/j.cnki.sgs.2017.09.003

Abstract

Abstract:

With the development of information technology, big data has become a research focus of all sectors. There is an increasing demand for big data in the urban planning management process. Big data acquisition and calculation is a key technology in the process of the smart city construction. This article covers the following major aspects: 1) Distance table linking to urban service physical store table is used to establish spatial association frequent rules model based on the concept of spatial neighbouring point and the property of spatial point entity; the article also introduces the method and procedure of how spatial frequent items and spatial association rules appear in urban service spatial association model; 2) “For xml path” technology is used in SQL Server to build spatial transaction database because transaction database is needed in association rules computing; 3) Python+sqlite3+lxml+BeautifulSoup technology is used to crawl the online data of the companies in Nanning which have all of their public information registered on “Baidu Nuomi” (https://nn.nuomi.com/); 4) Apriori algorithm is applied to analyze spatial frequent items and spatial association rules in urban service industry of 6 distance thresholds between 10 to 1 000 meters with the obtained data. In case study, the top six registered businesses in “Baidu Nuomi” are snacks and fast food, beauty, hotels, bakeries, sweets and drinks, budget hotels. The spatial association rule of {budget hotels, hotels} has a high degree of confidence and a high upgrading degree in the distance threshold of 10 m and 50 m, being a set of strong spatial association rules. This illustrates the Nanning hotel industry has the characteristics of a compact layout, with all kinds of hotels being together. The spatial association rule of {sweet drinks, snacks and fast food} is a set of strong spatial association rules in the distance threshold of 50 m, 500 m and 1 000 m. Snacks and fast food frequency is very high, especially in the succeeding rules with high support degree. In different distance thresholds, as a kind of mass consumer entity service, snacks and fast food restaurants are distributed around various industries. Because the lift degree of these rules is about 1, the snacks and fast food industry has the characteristics of no connection with other industries. This study is an attempt to use ubiquitous web data around us to analyze city management. Researchers can get a steady flow of big data so as to better carry out the studies on city big data in real time with this methods and thoughts.

Key words: big data, association rules, Apriori algorithm, service industry, Nanning City

CLC Number:

F290

Weihua Liao, Xin Nie. Spatial Association Analysis for Urban Service Based on Big Data[J].SCIENTIA GEOGRAPHICA SINICA, 2017, 37(9): 1310-1317.

Figures/Tables 6

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References 18

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输入点ID	临近点ID	距离（m）
1001	1003	100
1001	1004	80
1002	1003	85
1002	1005	70
1003	1001	95
…	…	…

距离（m）	规则	支持度	置信度	提升度
10	{经济型酒店} => {酒店}	0.1082	1.0000	7.1012
	{酒店} => {经济型酒店}	0.1082	0.7681	7.1012
	{川菜} => {全部中餐}	0.0445	0.8738	8.5759
	{个性写真} => {丽人}	0.0429	0.6963	3.5576
	{婚纱摄影} => {个性写真}	0.0389	0.7852	12.7380
50	{甜点饮品} => {小吃快餐}	0.2773	0.7772	1.4086
	{其他美食} => {小吃快餐}	0.2301	0.7989	1.4478
	{蛋糕} => {小吃快餐}	0.2293	0.7140	1.2940
	{经济型酒店} => {酒店}	0.1964	1.0000	3.9118
	{酒店} => {经济型酒店}	0.1964	0.7684	3.9118
100	{甜点饮品} => {小吃快餐}	0.4696	0.9012	1.2409
	{丽人} => {小吃快餐}	0.4450	0.8284	1.1406
	{蛋糕} => {小吃快餐}	0.4086	0.8631	1.1883
	{其他美食} => {小吃快餐}	0.4013	0.9001	1.2393
	{蛋糕} => {甜点饮品}	0.3500	0.7392	1.4186
500	{甜点饮品} => {小吃快餐}	0.9016	0.9944	1.0096
	{小吃快餐} => {甜点饮品}	0.9016	0.9154	1.0096
	{蛋糕} => {小吃快餐}	0.8982	0.9932	1.0084
	{小吃快餐} => {蛋糕}	0.8982	0.9119	1.0084
	{丽人} => {小吃快餐}	0.8897	0.9950	1.01024
1000	{蛋糕} => {小吃快餐}	0.9928	1.0000	1.0004
	{小吃快餐} => {蛋糕}	0.9928	0.9932	1.0004
	{甜点饮品} => {小吃快餐}	0.9924	1.0000	1.0004
	{小吃快餐} => {甜点饮品}	0.9924	0.9928	1.0004
	{甜点饮品} => {蛋糕}	0.9906	0.9982	1.0054

Spatial Association Analysis for Urban Service Based on Big Data

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