Study On Spatial Distribution Pattern Of Urban Management Cases

Currently,city is growing fast,however,the chaotic phenomena of advertisements on the roads and light poles,roadside stall business,illegal constructions and disorderly buildings,accumulation of wastes on the roads still exists.The complex environmental characteristics of the city and the surge of urban population have different influences on the occurrence trend and distribution pattern of such urban management cases(referred to as urban management cases for short).At the same time,residents also put forward higher requirements for urban development,which is in urgent need of improving intelligent analysis technology and scientific management ability.The current research on spatial distribution pattern is fruitful,but it is less applied to the management of cities,more qualitative description of municipal facilities,traffic congestion and public leisure space,and so on.The existing problems are put forward,but are not visually interpreted.Much of them are interpreted from the management level of process technology,administrative standardization and evaluation standardization,lacking sufficient scientific basis and data analysis.This paper proposes a spatial distribution pattern analysis process and method for the typical cases of urban management(publicity advertisement,city environment and street order),and probes into the high-incidence area of the case,and preliminarily determines the factors that may affect the spatial distribution of urban management cases according to the population density and social and economic activities,and the correlation between the deep excavation and the influencing factors in the frequent cases.In this paper,the spatial distribution pattern recognition of typical cases based on the combination of global and local,distance and density is carried out and the influencing factors are detected.(1)In the analysis of global spatial distribution pattern based on distance,the density center of the case is firstly determined by the centralized trend statistical index,and the development trend of the case is calculated by the standard deviation ellipse in the discrete trend index.The standard distance determines the close degree of the case in the spatial distribution of the study area.Then,the average nearest neighbor ratio is calculated by means of average nearest neighbor to calculate the global clustering situation of the case.Finally,multi-distance spatial clustering analysis method is used to study the cluster distribution of cases under different spatial research scales,and the maximum aggregation scale of the case is determined by the Monte Carlo test method,and the significance test is carried out.(2)In the analysis of the global spatial distribution pattern based on the density,by using the relatively mature and relatively mature global space autocorrelation analysis,according to the comprehensive calculation of the position information of the case and the related attribute information,it is judged by the Moran index that the result is that the case is distributed in the space,and whether the spatial self-correlation exists.Finally,the high/low clustering analysis method is used to distinguish the cases belonging to high value clustering or low-value clustering,and further explore the possibility of masking other spatial clustering distribution patterns.(3)In the analysis of the local spatial distribution pattern of urban management cases,this paper selects a kernel density estimation method based on the first law of geography,and then makes an in-depth exploration of the spatial distribution of data from the experimental data itself.In this method,the distance threshold setting has a big impact on the heat detection results,and in the course of the calculation of the optimal distance threshold in the present concentration,it is determined that the distance threshold of the hot spot is applied in this paper.In the end,the hot spot analysis is used to evaluate the distribution pattern of the significant clusters.(4)Refer to the relevant literature preliminary identification factors which may affect the distribution characteristics of the urban tube cases herein,and quantify the correlation between investigated cases and impact factors,which provides scientific basis for the deployment of urban management staff and the deployment of materials,so as to make urban governance work in a direction that can improve the level,quality and efficiency of urban management.This paper makes a comparison between the existing spatial distribution pattern analysis methods,and determines the research methods applicable to the data of urban management cases in this paper,and the research scope is based on the Xicheng district of Beijing.For various eyesores cases(advertising,the environment of the city,street order)of two years,respectively from the viewpoints of both global and local,and based on density level with a combination of distance level,to analyze the spatial distribution pattern.The research results show that:(1)the development trend of three kinds of cases is northeast – southwest through Spatial Distribution Description Analysis,and the aggregation degree of street order cases is obviously higher than other two cases;(2)by means of Average Nearest Neighbor Analysis and Multi-distance Spatial Clustering Analysis,the three types of urban management cases have statistically significant spatial clustering distribution patterns;(3)all three kinds of cases are spatial positive self-related and belong to high value clustering,the hot spot areas of the cases provides the city management staff with the direction of inspection and the distribution of materials;(4)using spatial analysis techniques like Buffer Analysis and combining simple statistical knowledge,spatial distribution pattern of the cases is closely related to the location distribution of municipal public transportation,commercial cents,tourism resources and education medical institutions in the city,which is related to the strong flow,high agglomeration,high density and the plurality of the people in the above field.