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| 基于LGCP的城市管理事件空间点模式分析 | |
| 引用本文： | 董文钱,董良,向琳,陶海军,赵传虎,曲寒冰.基于LGCP的城市管理事件空间点模式分析[J].地理科学进展,2020,39(8):1356-1366. |
| 作者姓名： | 董文钱 董良 向琳 陶海军 赵传虎 曲寒冰 |
| 作者单位： | 1. 中国计量大学信息工程学院,杭州 310018 2. 北京市科学技术研究院,北京 100089 3. 北京市新技术应用研究所,北京 100094 4. 河北工业大学人工智能与数据科学学院,天津 300401 |
| 基金项目： | 国家重点研发计划项目(2018YFC0809700);国家重点研发计划项目(2018YFF0301000);国家重点研发计划项目(2018YFC0704800);国家自然科学基金重大研究计划重点支持项目(91746207);北京市科学技术研究院创新团队项目(IG201801N) |
| 摘 要： | 数字化城管系统累积了大量城管事件历史数据,充分挖掘事件背后的空间分布模式和事件成因机制能够为城管部门的管控工作提供决策支持。论文利用Log Gaussian Cox Processes(LGCP)模型分析了西北某地H市P区的街面秩序类、市容环境类和宣传广告类城管事件之间的空间分布差异和事件成因影响差异。研究发现：① 3类城管事件都呈现出明显的空间聚集,其空间聚集尺度最远不超过924 m;② 各类事件聚集的特征各不相同,街面秩序类贴近城区主要干道,呈路网状。市容环境类表现出在城区中心块状聚集,周边地区零星分散的特征。宣传广告类靠近交通干线呈长条状,靠近商业中区域呈块状分布;③ 城区内不同类别的POI对城管事件的影响大小不同。购物服务类、医疗保健类和居民住宅类表现出最显著的影响,说明特定区域内人群的流量和密度是影响城管事件分布的重要因素,人群的流动和聚集会加剧城管事件数量的增加。研究结果能够满足城管部门的城管事件空间分布热点识别以及事件成因分析的需求。 |
| 关 键 词： | 城管事件 Log Gaussian Cox Processes 空间点模式 积分嵌套拉普拉斯逼近 随机偏微分方程 |
| 收稿时间： | 2019-07-09 |
| 修稿时间： | 2019-11-04 |
Spatial pattern of urban management cases based on Log Gaussian Cox Processes |
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| DONG Wenqian,DONG Liang,XIANG Lin,TAO Haijun,ZHAO Chuanhu,QU Hanbing.Spatial pattern of urban management cases based on Log Gaussian Cox Processes[J].Progress in Geography,2020,39(8):1356-1366. | |
| Authors: | DONG Wenqian DONG Liang XIANG Lin TAO Haijun ZHAO Chuanhu QU Hanbing |
| Institution: | 1. College of Information Engineering, China Jiliang University, Hangzhou 310018, China 2. Beijing Academy of Science and Technology, Beijing 100089, China 3. Beijing Institute of New Technology Applications, Beijing 100094, China 4. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China |
| Abstract: | Digital urban management systems have accumulated a large amount of historical data of urban management cases, but there is a general lack of research on the overall spatial pattern and cause of urban management cases. Therefore, it is necessary to fully explore the spatial distribution pattern and cause mechanism behind the incidents of urban management, which could provide decision support for the urban management departments to prevent and control the cases. Taking street order, urban environment, and publicity advertising urban management cases as an example and considering the points of interest (POI) data, this study used the Log Gaussian Cox Processes (LGCP) model to analyze the differences of spatial distribution and influencing factors between street order cases, urban environment cases, and publicity advertising cases in P district of H city, Northwest China. The study found that: 1) All the three types of urban management cases present obvious spatially aggregated distribution, and no spatial correlation is believed to exist beyond 924 meters. 2) The spatial features of the agglomeration space are different. The street order cases are close to the main trunk roads in the urban area, resembling a road network. The urban environment cases tend to cluster as blocks around the center of the district, while more scattered and dispersed in the peripheral areas of the district. The publicity advertising cases are in elongated distribution near the main traffic lines, but clustered as blocks in the commercial areas of the city. 3) Different types of POI in the study area have different impacts. Shopping services, health care, and residential areas show the most significant attractiveness, indicating that the flow and density of people in specific areas are the most important factors that affect the distribution of urban management cases, and increased flow and concentration of the crowd will sharply increase the number of urban management incidents. The results of this study include spatial hotspot identification and cause analysis, which can meet the urban management departments' needs. |
| Keywords: | urban management cases Log Gaussian Cox Processes spatial point pattern integrated nested Laplace approximation (INLA) stochastic partial differential equations (SPDE) |
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