Spatial Optimization Site Selection of Beijing Cainiao Station Based on Multi-Source Geospatial Data

As express delivery self-pickup becomes a part of people's daily lives, the location optimization of express delivery self-pickup points has become important research content. This study is based on the POI data of Beijing Cainiao Station and postal stations, combined with Point Of Interest (POI) data of other facilities and Beijing Digital Elevation Model (DEM), population distribution raster data, and road network data, to conduct spatial analysis, spatial statistics, and geographical detector analysis, and uses the Gurobi solver for optimization Site selection. The study found that: The spatial distribution of Cainiao Stations in Beijing is affected by factors such as land prices, administrative functional areas, and population density. The number shows a less-more-less spatial distribution from the central city to the outskirts of Beijing; The Cainiao Stations in Beijing are in a multi-core aggregation model. Mainly concentrated in the administrative districts with large residential populations on the periphery of East and Xicheng Districts, there is almost no distribution in the peripheral administrative districts with small residential populations. The gathering center of postal stations is the central urban area of Beijing, which is evenly distributed in the outermost areas; Express delivery pick-up point to the nearest residential area shows an increasing-decreasing-increasing trend. The spatial distribution of Cainiao Station is greatly affected by residential areas, vegetable stores, universities, population density, office buildings, etc.


INTRODUCTION
With the continuous advancement of urbanization, major logistics companies have opened logistics self-pickup site.To minimize the construction and operation costs of self-pickup sites and ensure that the service scope can cover as many customers as possible, the delivery site should be in a place with good accessibility, low transportation costs, low land costs, and many communities within the service range.
Alfred Weber raised the site selection problem in 1909.The site selection process should adhere to the principles of adaptability, coordination, economy, and strategy at the same time.Fire stations and community gardens should be located strategy and economy [1].Self-pickup site optimization is essentially a site selection issue, many researchers are optimizing all kinds of algorithms [3].Many studies at home and abroad have analysed the problems of selfpickup sites.Morganti [5] described how logistics companies choose the location of express delivery pick-up points and analyzed the French express pick-up point network.Moussaoui [6] conducted a survey on online shopping users in Morocco and found that the location, density, safety, and business hours of express delivery self-pickup points influence users' use of self-pickup points.Since carbon emissions are already having a huge impact on Earth's climate [7], many scholars are also exploring the analysis of carbon emission levels of traditional delivery models and selfpickup models [8].Based on the self-pickup points that have been built, some scholars use specific cities as cases to explore the delivery efficiency of self-pickup points [11].This study selects Beijing as the research area to study the spatial distribution model of Beijing Cainiao Station and explore the factors that affect the distribution of Beijing Cainiao Station, to optimize the distribution of Beijing Cainiao Station.

Distribution statistics of distance from pickup points to residential areas
To explore the distribution of walking distance between Cainiao Station and postal station and the nearest residential area, the Generate Neighbor List tool was used to find the three residential areas with the closest Euclidean distance to each Cainiao Station and Postal Station, and the Baidu Map API was used to The magnitude route planning is used to obtain the community with the shortest walking distance, and the distance distribution from the express pick-up point to the nearest residential area is calculated.In this study, the coordinates of the residential area POI point are selected as the location of the residential area for calculation.

Figure 3: Distribution map of distances from express delivery pick-up points to the nearest residential area
The number of distances between express pickup points and the nearest residential area shows an increasing-decreasing-increasing trend.When the distance reaches 200 meters, the number of express pickup points is the largest.About 87.19% of Cainiao Stations are located within 1 kilometre of the nearest residential area.Within the area, 87.97% of post offices are located within 1 km of the nearest residential area.The average distance from Cainiao Station to the nearest residential area is 477.53 meters, while the average distance from the postal station to the nearest residential area is 1101.50meters.

Factors influencing the site selection of Cainiao Station
The distribution of land features in various blocks in Beijing is different, and the distribution of Cainiao Station is also affected by it.To explore the influence of other factors on the distribution of Cainiao Stations, and input the statistical results into the geographical detector [14], using the number of Cainiao Stations as the dependent variable and the levels of the values of each influencing factor as the independent variables, to find out the impact and credibility of each influencing factor on the spatial distribution of Cainiao Stations.The results are shown in Table 1.

Workflow
According to the influencing factors of the spatial distribution of Cainiao Station, ten typical factors were selected to solve the optimal location selection of Cainiao Station in Beijing.The overall experimental process is shown in Figure 4.

Model
The purpose of Cainiao Post is to maximize revenue, so it needs to cover as many users as possible under the conditions of a limited number of Cainiao Post.Its mathematical model is expressed as follows: '  !=  "∈% " and  ! are decision variables, defined as follows: " = H 1 Establish a Cainiao station at point j 0 Do not establish a Cainiao station at point j ( 6)

Model Solved based on Gurobi solver
Gurobi Solver is a powerful commercial mathematics solver with high-performance solving algorithms and advanced mathematical optimization technology.Limited by equipment conditions, it is impossible to solve the complete Beijing Cainiao Station location optimization problem.Here, only part of the data is selected as a sample to solve.The following 25 facilities are selected based on 28,140 demand points and 100 candidate facilities.The source code for this study is available at https://github.com/HIGISX/hispot.

CONCLUSION AND FUTURE WORKS
The location of the express pickup point needs to cover as many users as possible and be as close as possible to the users.
Perform hierarchical color representation according to the population density, and then conduct separate statistics on Cainiao Station and China Post Station in each district, and use a bar chart to obtain the population density distribution of each district in Beijing.As can be seen from the figure, the population density of various districts in Beijing gradually decreases from Dongcheng District and Xicheng District to the outer administrative districts.The number of Cainiao Stations overall shows a low-high-low distribution.The reasons for this distribution of express delivery pick-up points are Beijing's land price factors, population density factors and regional administrative function factors.Judging from the number of Cainiao Stations and China Post stations in each district, Cainiao Station has an obvious market orientation.

Figure 1 :
Figure 1: Stratified colouring of population density in each district of Beijing and histogram of Cainiao Station and postal stations2.2Spatial agglomeration characteristicsUse the hotspot analysis tool to conduct hotspot analysis within a fixed distance range between Beijing Cainiao Station and postal

Figure 2 :
Figure 2: Distribution and hotspot analysis map of express delivery pick-up points

Figure 4 :
Figure 4: The process for solving the spatial optimization problems First, count the number of these ten influencing factors in the fishing net in Beijing.The number of Cainiao stations and influencing factors are input into the geographical detector, and the output q-value is used as the weight.Then the number of influencing factors in each grid is weighted and summed.Arrange the Cainiao feasibility in descending order by value, select the geometric centres of the 3000 grids with the largest values as candidate points for Beijing Cainiao Station, and use the 1kilometre grid identification point to obtain the value of the Beijing population distribution DEM as Demand point demand intensity.Enter the Cainiao Station alternative points and demand points into the p-Median Problem and select the Cainiao Station set point through the utility function value.The larger the utility value, the greater the impact of the Cainiao Station on the demand points.

Table 1 . The influence and credibility of various factors count on the distribution of Cainiao Station.
Among them,  represents the set of demand points ;  represents the set of Cainiao Station alternative points  ;  is the number of Cainiao Stations planned to be built;  !" is the distance from demand point  to Cainiao Station alternative point  ;  ! is the demand point  population weight;  !" represents the utility value of Cainiao Station alternative point  to demand point ;  is the service radius of Cainiao Station; () is the set of Cainiao Station sites that can cover demand point .
This study takes Beijing as the research area, explores the spatial distribution rules of Beijing Cainiao Station, analyzes the influencing factors affecting the distribution of Beijing Cainiao Station, and optimizes the distribution of Cainiao Station.The conclusions drawn from the analysis are as follows:The number of Cainiao Stations in Beijing shows a low-high-low spatial distribution from the central city to the outer administrative areas.Both Beijing Cainiao Station and postal stations have obvious spatial aggregation, and Cainiao Station is in a multi-core aggregation mode.The distribution of the distance from the express pickup point to the nearest residential area shows an increasedecrease-increase trend, with the largest number in the range of 100 meters to 200 meters.Cainiao Station uses geographical detectors to analyze the influencing factors of its distribution.Residential areas, vegetable shops, universities, population density, office buildings, etc. have a greater impact on its distribution.