Research On Local Spatial Association Pattern Mining Algorithm For POI Of Urban Public Service Facilities

With the development of the economy,the demand for both the number and types of public service facilities of urban residents is constantly increasing.The differentiation of residents’ living standards in cities also leads to different demands for public service facilities in different regions.Therefore,in order to rationally plan the supporting public service facilities of residential districts,it is crucial to identify the spatial associations of residential districts and urban public service facilities in different areas.Spatial association is the spatial interdependence and interaction of spatial objects and phenomena in essence.The existing spatial association pattern mining algorithms are mostly oriented to the spatial association between two types of elements,which cannot fully reflect the complex spatial relationship between residential districts and various of public service facilities.In addition,the interaction between different types of elements is often asymmetric,but the existing spatial association pattern researches usually ignore the one-way dependence between different elements.The spatial association between residential districts and public service facilities has one-way dependence.Therefore,the existing algorithms are not fully applicable to mining local spatial association patterns between residential districts and public service facilities.Aiming at the above problems,a local spatial association pattern mining algorithm for POI of urban public service facilities is proposed and a new index MLCLQ to measure the strength of local region association patterns is constructed in this thesis.Firstly,the spatial proximity between residential districts POI and urban public service facility POIs is judged by a fixed filter and the near table is generated.The geospatial weight is given in the near table by Gaussian kernel function.Then,the ratio of the probability of finding spatial association patterns in local areas to the expected probability is calculated by drawing on the idea of location quotient,and the MLCLQ of each residential district is calculated by combining with the weighting coefficient.Finally,the POI data are processed with restricted random labeling,and the simulated sample distribution of MLCLQ is recalculated.The significance of MLCLQ is tested by the simulated sample distribution.Based on the proposed local spatial association pattern mining algorithm,the script tool of the algorithm is implemented on Arc Map platform.When the script tool is running,generating a single nearest table can mine multiple spatial association patterns at the same time,which improves the mining efficiency.In the thesis,the algorithm is verified by manual simulation data.The experimental results show that the algorithm can accurately identify the local spatial association patterns and test the significance of MLCLQ,which proves the effectiveness of the algorithm.Moreover,we applied the algorithm by real data example and selected five districts within Chengdu as the study area to mine the local spatial association patterns of residential districts and urban public service facilities.The experimental results show that the algorithm proposed in the thesis can identify the one-to-many spatial association patterns between residential districts and public service facilities in different regions and quantitatively measure the strength of local regional spatial association patterns,which provides a new method for spatial association pattern mining.The research results can not only provide a reference for the rational planning of public service facilities in residential districts,but also be applied to the spatial association pattern mining of all classification point data.