Spatiotemporal Variability of Urban Management Events based on the Bayesian Spatiotemporal Model

Wenqian DONG; Liang DONG; Lin XIANG; Haijun TAO; Chuanhu ZHAO; Hanbing QU

doi:10.12082/dqxxkx.2020.190413

Journals >Journal of Geo-information Science >Volume 22 >Issue 5 >Page 1073 > Article

Journal of Geo-information Science
Vol. 22, Issue 5, 1073 (2020)

Spatiotemporal Variability of Urban Management Events based on the Bayesian Spatiotemporal Model

Wenqian DONG^1、1, Liang DONG^{2、2、3、3}, Lin XIANG^1、1、*, Haijun TAO^1、1, Chuanhu ZHAO^4、4, and Hanbing QU^{2、2、3、3}

Author Affiliations

¹.中国计量大学信息工程学院,杭州 310018

¹College of Information Engineering, China Jiliang University, Hangzhou 310018, China

².北京市科学技术研究院,北京 100089

²Beijing Academy of Science and Technology, Beijing 100089, China

³.北京市新技术应用研究所,北京 100094

³Beijing Institute of New Technology Applications, Beijing 100094, China

⁴.河北工业大学人工智能与数据科学学院,天津 300401

⁴School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China

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DOI: 10.12082/dqxxkx.2020.190413 Cite this Article

Wenqian DONG, Liang DONG, Lin XIANG, Haijun TAO, Chuanhu ZHAO, Hanbing QU. Spatiotemporal Variability of Urban Management Events based on the Bayesian Spatiotemporal Model[J]. Journal of Geo-information Science, 2020, 22(5): 1073 Copy Citation Text

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Fig. 1. Spatial distribution of urban management event number in the study area

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Fig. 2. Temporal trend of urban management event number in a week

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Fig. 3. Spatial distribution of the POI data

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Fig. 4. Spatial distribution of urban management events' relative risk

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Fig. 5. Spatial hot/cold spots of urban management events

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Fig. 6. Temporal relative risk trend of urban management events during a week with the 95% CI

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Fig. 7. Temporal relative risk trend of urban management events during a day with the 95% CI

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POI类别	POI内容	均值(标准差)
餐饮服务类	中餐厅、快餐厅、外国餐厅、休闲餐饮场所等	0.77(1.20)
交通设施类	公交车站、物流站等	0.20(0.37)
居民住宅类	住宅区	0.35(0.58)
购物服务类	便利店、超市、烟酒专卖店、购物相关场所等	0.71(1.07)
生活服务类	美容美发店、洗浴推拿场所、生活服务场所等	0.71(1.00)
住宿服务类	酒店、旅馆、招待所等	0.13(0.38)
医疗保健类	医院、社区诊所、卫生院、药店、药房等	0.37(0.69)
公司企业类	公司、企业、工厂等	0.68(0.78)

Table 1. Summary of the POI data

模型	模型结构
M1	$\log (λ_{ijk}) = b_{0} + \sum_{p} β_{p} X_{pijk} + \log (E_{ij})$
M2	$\log (λ_{ijk}) = b_{0} + \sum_{p} β_{p} X_{pijk} + μ_{i} + ν_{i} + \log (E_{ij})$
M3	$\log (λ_{ijk}) = b_{0} + \sum_{p} β_{p} X_{pijk} + μ_{i} + ν_{i} + γ_{j} + φ_{j} + τ_{k} + \log (E_{ij})$

Table 2. Candidate models with different components

协变量(POI密度)	VIF	协变量(POI密度)	VIF
餐饮服务类	4.53	生活服务类	5.17
交通设施类	1.37	住宿服务类	2.52
居民住宅类	2.26	医疗保健类	3.21
购物服务类	3.88	公司企业类	1.41

Table 3. Results of multicollinearity evaluation

事件类别	候选模型	DIC
街面秩序	M1	36 873.15
	M2	26 182.07
	M3	24 851.99
市容环境	M1	27 093.02
	M2	21 791.38
	M3	19 099.85
宣传广告	M1	29 935.99
	M2	23 364.75
	M3	20 375.71

Table 4. DIC values for the various candidate models

协变量	街面秩序类	市容环境类	宣传广告类
截距项	0.586(0.549, 0.624)	0.654 (0.614, 0.697)	0.611 (0.573, 0.651)
餐饮服务类	1.083^*(1.032, 1.136)	1.065^*(1.017, 1.116)	1.086^* (1.036, 1.138)
交通设施类	1.105^*(1.047, 1.166)	1.056^* (1.001, 1.113)	1.091^* (1.035, 1.151)
居民住宅类	1.156^*(1.017, 1.315)	1.073 (0.949, 1.213)	1.118 (0.990, 1.263)
购物服务类	1.055(0.963, 1.157)	1.039 (0.950, 1.136)	1.052 (0.962, 1.150)
生活服务类	1.084^*(1.026, 1.145)	1.065^* (1.009, 1.125)	1.081^* (1.024, 1.141)
住宿服务类	1.158^*(1.019, 1.315)	1.201^* (1.070, 1.363)	1.205^* (1.069, 1.359)
医疗保健类	1.070(0.956, 1.162)	1.001 (0.931, 1.093)	1.081 (0.997, 1.171)
公司企业类	0.984(0.928, 1.043)	0.995 (0.941, 1.052)	0.944 (0.921, 1.031)

Table 5. Posterior mean and confidence intervals of potential covariates

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