Mining The Relationship Between Pollution Sources And Patients’ Addresses Based On Spatial Co-location Patterns

In recent years,tumors have gradually been one of the major refractory diseases that threaten global human health.Clinical data show that 80% of patients in cancer cases are related to environmental factors in life.At the same time,the acceleration of industrialization has made some pollution sources to emit large amounts of untreated pollutants into the environment.These pollution sources have significantly affected the surrounding environment.The increasing incidence of the tumor and the changes of the living environment make people pay more and more attention to the impact of environmental factors on tumor diseases.When residents choose the residential areas,they often focus more on whether there are pollution sources in the surrounding environment.The relationship between pollution sources and residential addresses has gradually become the focus of people’s life.How to use existing technology to analyze the relationship between pollution sources and the location of the patient’s addresses in a geographical location,which has a certain significance for the site selection of the pollution sources.Based on the above background,this thesis mainly studies the relationships between pollution sources and patients’ addresses in the space,then analyzes the minimum safe distances between the both.Based on this,provide a method for mining the relationships between the pollution sources and the patient’s addresses based on spatial co-location patterns.Firstly,according to the characteristics of the traditional co-location patterns mining,the larger distance thresholds,the easier get prevalent patterns and the mining method in this thesis,proposes a mining framework of this thesis.Secondly,the related definitions of the minimum distance(min_dist)and prevalent patterns with minimum distance are given.Using the plane scanning technology to solve the distance between the spatial instances,then provide the minimum safe distances mining method(MSDM)based on the maximum participation rate mining algorithm.Using the Baidu map Geocoding API to translate the patients’ structured detailed addresses to geographical coordinate points,using the POI search keywords to obtain relevant data of the pollution sources near the patients’ addresses,meanwhile collate the acquired data sets.Finally,construct an experimental framework for the relationship between pollution sources and patients’ addresses for the real data sets.Visualize display and analyze the mining results,and finally obtain the minimum safe distances of different pollution sources and patients’ addresses.Accordingly the feasibility and validity of the research framework were verified.