建成环境对星级酒店内被盗的影响——以ZG市中心城区为例

doi:10.18306/dlkxjz.2020.05.011

Abstract

Abstract:

As a ticklish social problem, crime committed in hotels has been concerned by both Chinese and Western scholars. Theft is one of the most frequent crime types occurred in hotels, especially in star hotels. Previous studies on influencing factors of hotel theft cases mainly focused on star hotel and personal attributes of victims at the micro level from the perspective of sociology, rather than considering the built environmental factors at the macro level from the perspective of geography. Using the data on the star hotels with theft cases in 2012-2014 in ZG central city obtained from Municipal Public Security Bureau, this study examined the spatial-temporal characteristics of these hotels. Then the environmental indicators within 500 m around the hotels were examined and the negative binomial regression method was used to make a systematic analysis on the factors affecting the theft of different types of star hotels in various time periods. The main results are as follows: 1) The spatial-temporal distribution of star hotels showed obvious agglomeration features. Generally, most of the main hotspots in the high incidence areas of hotel theft were time invariant, all of them are highlighted near the old city central business district, the eastern extension of the main road and the railway station. However, the spatial distribution of secondary hotspots was time-varying. 2) The overall model analysis indicates that the improvement of the service level was the most effective way to reduce the theft cases of all star hotels. The surrounding property and community points of interest (POIs) will significantly increase the opportunity for all star hotels to be stolen, while road intersections play a significant regulatory role in the theft of all star hotels. 3) The results of the sub models demonstrate that the effect of service level on the theft of three-star and five-star hotels is significant, and the effect of road intersection on the theft of four-star hotels often used by guests of business travel is stronger; the number of POIs has more obvious effect on three-star and four-star hotels. Large-scale retail business center can significantly increase the number of theft cases in hotels in the peak season and the weekend, and the number of road intersections is significant for the monitoring of stolen risk of the star hotels in the off-season period and the working days. These results have shown that the built environment played a significant role in affecting the opportunity and cost of hotel theft. The results verify the applicability of the daily activity theory in the study of hotel crimes in large cities in China, and expand the research results of crime geography in the direction of star hotel theft, which has a guiding effect for the prevention of hotel theft.

Key words: star hotels, theft, the built environment, negative binomial regression, ZG central city

ZHANG Chunxia, ZHOU Suhong, LIU Lin, XIAO Luzi. Relationship between the built environment and theft cases in star hotels in ZG central city[J].PROGRESS IN GEOGRAPHY, 2020, 39(5): 829-840.

Figures/Tables 7

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References 34

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变量	变量描述	最小值	最大值	均值	标准差	VIF
盗窃犯罪数(个)	星级酒店内被盗次数	1	42	4.66	6.26	—
星级标准	星级酒店自身的星级等级	2	5	3.62	0.82	2.04
服务水平	携程网客户对星级酒店的评分	3.4	4.9	4.22	0.40	1.38
房间数(个)	星级酒店客房的数量	10	850	181.47	141.88	1.75
地产小区	500 m缓冲区地产小区的数量	0	182	34.95	33.76	1.58
POI(个)	500 m缓冲区各类POI的数量	27	3272	905.22	687.77	1.92
星级酒店(个)	500 m缓冲区星级酒店的数量	1	12	3.08	2.36	1.54
大型零售商业(个)	500 m缓冲区商业大厦的数量	0	16	2.59	2.78	1.39
停车场(个)	500 m缓冲区停车场的数量	0	92	24.22	19.95	2.10
道路交叉口(个)	?500 m缓冲区内道路交叉口数量	0	98	14.60	17.03	2.25
公交车站(个)	500 m缓冲区内公交车站的数量	0	19	5.68	3.99	1.84

变量	模型1：总模型	模型2：三星级	模型3：四星级	模型4：五星级
常数	1.180(3.255)	0.113(1.120)	-1.564(0.209)	7.260(1422.87)
星级标准	-0.158(0.854)	—	—	—
服务水平	-1.101^***(0.333)	-0.939^**(0.391)	-0.662(0.516)	-2.335^**(0.097)
地产小区	0.002(1.002)	0.001(1.001)	0.002(1.002)	0.003(1.003)
POI数量	<0.001^***(1.000)	<0.001^**(1.000)	<0.001^***(1.000)	<-0.001(1.000)
星级酒店	-0.018(0.982)	-0.011(0.989)	-0.068(0.934)	0.080(1.084)
大型零售商业	0.033(1.033)	0.020(1.021)	0.037(1.037)	-0.026(0.974)
停车场	-0.002(0.998)	-0.006(0.994)	0.002(1.002)	0.017(1.017)
道路交叉口	-0.010^***(0.990)	-0.004(0.996)	-0.028^**(0.973)	-0.003(0.997)
公交车站	0.028(1.028)	0.041(1.042)	0.079(1.082)	-0.010(0.990)
对数似然函数	-390.861	-185.786	-111.835	-73.029
Alpha	0.379^***	0.328^**	0.268^***	0.376^**
被盗比	53.59%	59.85%	52.87%	71.43%
N	163	82	46	30

变量	模型1：旺季	模型2：淡季	模型3：周末	模型4：工作日
常数	-0.747(0.474)	0.939(2.558)	-0.787(0.455)	1.167(3.212)
星级标准	-0.152(0.856)	-0.174^*(0.840)	-0.200^*(0.819)	-0.140(0.869)
服务水平	-0.810^***(0.445)	-1.078^***(0.340)	-0.753^**(0.471)	-1.183^***(0.306)
地产小区	0.003(1.003)	0.002(1.002)	0.003(1.003)	0.002(1.002)
POI数量	<0.001^**(1.000)	<0.001^***(1.000)	<0.001^***(1.000)	<0.001^***(1.000)
星级酒店	-0.050(0.951)	0.004(1.004)	-0.048^*(0.953)	-0.013(0.987)
大型零售商业	0.083^***(1.086)	0.035(1.035)	0.079^***(1.083)	0.023(1.024)
停车场	-0.007(0.993)	0.002(1.002)	-0.003(0.997)	0.002(1.002)
道路交叉口	-0.006(0.994)	-0.009^**(0.991)	-0.007(0.993)	-0.011^***(0.989)
公交车站	0.003(1.003)	0.007(1.007)	-0.012(0.988)	0.026(1.026)
对数似然函数	-165.210	-315.380	-181.734	-313.656
Alpha	0.107^***	0.227^***	0.076^***	0.275^***
被盗比	32.03%	47.71%	34.64%	48.37%
N	97	146	106	147

Relationship between the built environment and theft cases in star hotels in ZG central city

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