AU2020100703A4 - A method of spatial accessibility evaluation of urban facility services based on GIS - Google Patents
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Abstract
The invention utilizes the network analysis function of ArcGIS software and combines with JAVA language to analyze the spatial accessibility of urban facility services. In order to achieve better spatial accessibility evaluation, the general research method is as follows : First, process the original data accordingly. Then, calculate the value of the transportation impedance, that is, the distance. The third step is to obtain the spatial accessibility by calculating the shortest distance matrix from the facility to the point of demand. Next, count the accessibility results. Finally, transform the data results into visualized images. According to the above steps, the accessibility analysis of urban facility services can make the results more accurate and avoid problems such as excessive error, insufficient reality or too subjective, so as to evaluate the rationality of urban facility layout and optimize it, which is conducive to the development of the city. Data Processing __I_ Transportation impedance calculation Accessibility calculation __I_ Statistics about accessibility results Visualization about accessibility results Figure 1
Description
CM TITLE
A method of spatial accessibility evaluation of urban facility services based on GIS
FIELD OF THE INVENTION
The invention utilizes the network analysis function of ArcGIS software and combines with JAVA language to analyze the spatial accessibility of urban facility services.
BACKGROUND OF THE INVENTION
The 21st century has witnessed the rapid development of cities and the expansion of urban scale. However, there also come a series of urban problems such as uneven distribution of public service facilities, which is closely related to the utilization efficiency of resources and environment and social benefits. Reasonable distribution of public service facilities can reduce the temperature of the city to a certain extent as well as reduce the emission of pollution. What is more, it can relieve residents' travel pressure, promote neighborhood relations and improve the happiness of social life.
Spatial accessibility is a comprehensive index to evaluate the layout of urban public facilities, which refers to the degree of accessibility from a
2020100703 05 May cq given location to the location of activities. We already have some ways to evaluate spatial accessibility. Firstly, the nearest distance method can directly reflect residents' behavior of choosing the nearest service area, but it abstracts both source and destination as points in the evaluation process, resulting in a large error in the evaluation result. Another method is buffer analysis. The buffer zone means the area covered within a certain radius. It is generally believed that people within the buffer zone can easily reach the destination, while citizens outside the buffer zone cannot enjoy the service. The buffer analysis method is easy to show the accessibility of a service facility visually and is also easy to use in planning. But there are some shortcomings: it can only distinguish accessible region and inaccessible region instead of reflecting the reality of the users’ situation. Therefore, it is impossible to accurately reflect the actual traffic situation and accessibility differences in the service area. There is also a method called the cost resistance, which can better reflect the resistance that the individual overcomes in the process of reaching a certain destination. But the assignment of resistance value is subjective and there is no uniform standard.
Avoiding the disadvantages of the above methods, the network analysis method based on ArcGIS can help us get better analysis. The network analysis method is based on the road network to calculate the service
2020100703 05 May cq scope of urban public facilities. In this method, we extract the central point of the plot where there is human activity as a demand point set, in combination with facilities point set, and using the service area analysis method and OD cost matrix method in network analysis method to analyze the accessibility of urban public facilities, compare the better as a result and finally visualize the results. The advantage of network data analysis method is that it is based on the road network and takes the center point of the plot of human activities as the set of demand points. And it avoids the disadvantage of buffer analysis method, that means it can identify obstacles. Moreover, the result is the most accurate of these methods.
To sum up, network analysis method can provide ideas and technical support for spatial accessibility evaluation, evaluate spatial accessibility more scientifically and systematically and provide guidance for regional planning and spatial layout.
SUMMARY OF THE INVENTION
The spatial accessibilty evaluation can be applied in multiple fields. In order to achieve corresponding spatial accessibility, the invention defines accessibility as the convenience from a given location to an activity location by using a specific transport system and performs a method of
2020100703 05 May rq calculation by using ArcGIS and Java.
rq
As Fig.l shows, spatial accessibility evaluation can be divided into the following five steps:
Step one, data proccessing. Raw data generally includes demand point data, facility data and road data. Based on these given data file, further calculation and processing are performed to obtain the data required for subsequent calculations.
Step two, transportation impedance calculation. Considering that the dataset of spatial distances between facilities and demand points is used as a numerical indicator for assessing accessibility, it is reasonale to get the road network distances from nodes to nodes by building the road network dataset. As a result, the value of the distance is the transportation impedance used in this invention.
Step three, accessibility calculation. Based on the prepared and processed data, the shortest distance matrix from the facilities to the demand points is calculated. The shortest distance obtained is just the accessibility evaluation index. Moreover, the smaller the distance values, the easier the corresponding facility is to reach.
2020100703 05 May
Step four, statistics about accessibility results. Set different thresholds to classify the demand points to different levels. Then output the result as a data file.
Step five, visualization about accessibility results. Map the demand areas hierarchically according to the catographic document calculated in the previous step, which makes the result intuitive and easy to understand.
Following all the steps above, visual results such as thematic maps and statistical charts are produced. It is convenient to analyze specific problems through visual expressions, and useful information can be drawn from the final result to provide services for further research.
In most cases, the spatial accessibilty evaluation can be used to evaluate the rationality of urban facility layout and plays a part in optimization. Additionally, it also works in analyzing their future development potential and competitiveness.
DESCRIPTION OF THE DRAWINGS
The appended drawings are only for the purpose of description and explanation but not for limitation, wherein:
2020100703 05 May
Fig.l is a schematic diagram of spatial accessibilty evaluation.
Fig.2 is the basic data to be prepared before the experiment.
Fig.3 shows the four data to be processed in the data processing phase.
Fig.4 shows the two steps of calculating road impedance.
Fig.5 is a flowchart of calculating the OD cost matrix between the facilities and the demand points based by using Java.
Fig.6 is a detailed explanation of the “Computing” steps in Fig.5.
Fig.7 is a flowchart of the process of grading the demand areas based on the distances.
Fig.8 is a detailed explanation of the “Data Analysis” steps in Fig.7.
Fig.9 is a map made according to the levels of the demand areas.
Fig. 10 is a flowchart of the steps to use ECharts to express the
2020100703 05 May
C\| correspondence between facilities and demand points.
rd
Fig. 11 is the final result of using ECharts to express the correspondence between facilities and demand points.
DESCRIPTION OF PREFERRED EMBODIMENT
Purpose
The accessibility of catering facility services is an important indicator to reflect the service level of catering facilities, and it is also an important criterion for judging the rationality of catering facility layout. In order to optimize layout of the catering facilities, the GIS network analysis method is used to analyze the catering facility service area under different distances by walking.
Principle 1
Distance index is one of the evaluation indexes of spatial accessibility, which is divided into space straight distance and shortest distance through road network. The shortest distance can be space distance, time distance or economic distance. Because the experiment mentioned in this paper involves different density of road network on different spatial units in the urban area, the experiment is based on the urban road network, taking the shortest distance between the demand point and the facility point as the
2020100703 05 May
ΓΜ traffic impedance to evaluate the spatial accessibility.
rq
For the basic road network, the travel mode is automobile, non motor vehicle, walking, etc. The traffic impedance between the starting point and the target point is composed of three parts: the distance between the starting point O and the nearest point OB in the road network is Di , the distance between the target point D and the nearest point DB in the road network is D3 , and the shortest distance between OB and DB through the road network is D2 . The calculation formula of traffic impedance is:
D= Di + D2 + D3 (In the formula, Di and D3 are usually replaced by straight-line distance. Di is the shortest path distance among all possible paths between OB and DB.)
With the application of the shortest path algorithm in the network analysis of GIS, the calculation of the traffic impedance in the road network can be realized quickly in the relevant software platform of GIS. The traffic impedance between the nodes of the road network, that is, between OB and DB, can be obtained by using the network analysis, OD cost matrix and other operations in ArcGIS.
When Di , Di , D3 can be obtained by processing the basic data, the
2020100703 05 May 2020 shortest distance between the demand point and the facility point can be obtained after inputting starting point O and the target point D in the compiled Java program, which is the experimental principle.
Principle 2
The experiment is designed on the basis of spatial barrier model, which is based on graph theory to analyze the accessibility of nodes in the network. The model defines the accessibility as the difficulty of overcoming the barrier in space. The model is as follows:
Ai=Zj Lij (In the formula, Ai represents the accessibility of node i; Lij represents the transportation impedance from node i to node j, usually the distance between node i and J (straight-line distance, shortest network distance), travel time, travel cost, etc.)
In the experiment, we take the shortest distance of the nodes in the road network as one of the transportation impedances.
Step 1
Based on the strong advantages of GIS technology in spatial data management and spatial analysis, a spatial accessibility analysis method based on GIS is established. The technical process includes data
2020100703 05 May
CM processing, transportation impedance calculation, accessibility calculation, statistics about accessibility results and visualization about accessibility results, etc. The experiment is mainly carried out on ArcGIS .Before the experiment, three basic data should be ready, “road” document, “facility” document and “land parcel” document. The layout of road network, the distribution of facility and the land use map are transformed into the data format recognized by Arc GIS and then input into the GIS platform.
In the first stage, as listed in Fig.2, the processing of the above three data is required. The first step is to create a network dataset from “road” document. By using ArcGIS, a powerful road network database can be established, which can effectively extract the urban road data needed for road evaluation and provide sufficient data base for the next road evaluation. And then directly extract the center point of the “land” document with human activities as the set of demand points. After the previous operation, the X and Y coordinates of the demand points are obtained. The X and Y coordinates of the facility points can be obtained directly from “facility” document . Through the spatial connection tool provided by ArcGIS, the land parcel number of demand points and facility points can be known. All the above data listed in Fig.3 will support the accessibility analysis experiment of urban facilities.
2020100703 05 May cj Step 2
The second stage of the experiment is to calculate the transportation impedance, which consists of two parts shown in Fig.4. Calculating OD cost matrix between road nodes is one of them. Creating OD cost matrix is a process of result calculation according to the analysis parameters set for experiment purpose after adding demand points and facility points. The other part is to calculate the shortest distance between points and lines, which can be realized by the “near” tool provided by ArcGIS. The results obtained in this stage will provide the basis for the calculation of the road length between the demand point and the facility point.
After the two steps above, the data files including the facility-demand distance matrix, the properties of all facilities and demands, road network properties and OD matrix are obtained. The format of the files are listed in Form 1.
| Data sheet name | Data sheet description | ||||
| Data sheet field names | |||||
| Facilitiyland | Facilities’ position | ||||
| FID | LX | LandFID | Type | X | Y |
| Facilityroad | Vertical distance from facilities’ position to nearest road | ||||
| FID | LX | Near_FID | Near_distance | ||
| F acility_j unctions | Distance along road from vertical pedal point to both vertexs of a road segment | ||||
| FID | LX | Near_FID | Near_distance | ||
| Centerland | Demands’ position (except for water and green land fields) | ||||
| FID | Type | LandFID | Type | X | Y |
| Centerroad | Vertical distance from demands’ position to nearest road |
2020100703 05 May 2020
| (except for water and green land fields) | |||
| FID | Type | Near_FID | Near_distance |
| Center junctions | Distance along road from vertical pedal point to both vertexs of a road segment (except for water and green land fields) | ||
| FID | Type | Near_FID | Near_distance |
| Road | Road network | ||
| FID | Junction 1 ID | Junction2_ID | Length |
| ODMatrix | Origination-Destination distance matrix of all nodes on road network | ||
| Origination ID | Destination ID | Totallength |
Form 1
Step 3
Then, our program calculate the distance from facilities to demands as Fig.5 shows. We extract a facility position and a demand position by their FID in ascending order and check out whether they belong to one field. If so, we calculate the length straightway. If not, we calculate the distance in four steps. Firstly, inquire the vertical distance from facility to its nearest road. Secondly, inquire the distance from foot of perpendicular to its nearest road nodes on one road segment, normally two nodes and two lengths come out with a position calculated. Thus, we got four nodes and four lengths after this step, a facility corresponding two and a demand corresponding two. Then, we inquire the distance from one facility-nearest node to one demand-nearest node in OD matrix of road network. Normally this step we get four lengths. Finally, add all distance and choose the shortest one in these four results. The shortest way is a
2020100703 05 May cq new value in new OD matrix. Output these calculating results as a new Facility-Demand OD matrix and save as TXT file. This file is essential basis of our further work and analysis.
Step 4
In the fourth step, rank the level of demand position as Fig.7 shows. Firstly, input the grading scale and grading source, namely threshold values string and facility-demand distance matrix. What’s more, formative Geo-database data is also needed for union query. Secondly, our program split the threshold values string into floating point figures for latter estimation. The estimation is a loop nesting comparison. Concretely, we extract a facility-demand distance in matrix’s storage sequence. Then we compare the length with the threshold values from bottom to top to ensure the class of this demand position. When covered all elements in matrix, the estimation of every demand position is completed. Finally the program will output the level of a demand position, which facility will serve this demand and the length between facility and demand. In addition, these results with union query of Geo-database data provides us with more information of this relationship, like numbers, names and positions of facilities near the position.
Step 5
2020100703 05 May cq In the last step, two different visual representations of the accessibiity evalution results are made.
The first expression is hierarchical mapping by using ArcGIS.Connect the IDs of the graded demand points in the catographic data with the IDs of the demand points in the original data. In this way, each demand point can be associated with its corresponding level in spatial location. According to the different levels of the demand area, express the demand areas in different colors on the map, through which we can get Fig.9.
The second way of expression is to express the relationship between the demand points and facilities by using ECharts after the steps in Fig. 10. First, convert land_parcel, facility and road files to json format. Second, we input “land_parcel.json” to visualize the base map and get the outline of the whole map. Then input file 2 and display all facility points on the map. The size of facility points is positively correlated with the number of demand points that can be reached within 600 meters, highlighting the service capacity of facility points within a certain range. Third, select a number of demand points and display them on the map. If the demand points are within 600 meters of the facility points, the service accessibility line from the facility points to the demand points is established, which emphasizes the corresponding relationship between
2020100703 05 May
ΓΜ the facility points and the demand points. Finally, Fig. 11 is obtained.
CM
Results
Fig.9 can intuitively reflect the result of grading the demand areas according to their distances to the facilities. From the figure, we can get the breadth of the demand areas that the catering facilities can serve , and can also evaluate the convenience of the demand areas to the catering facilities, so as to optimize the layout of the catering facilities in order to make the catering facilities serve more demand areas and be more convenient.
As shown in Fig. 11, we visualize facility points and part of the demand points on the basis of the map, which expressed the facilities, service capacity, the size of the points and demand points corresponding relation and facilities. This shows that the facilities are different because the geographical location and the surrounding demand points are different, and the distribution of the size of the facilities, service capacity are also different, therefore, service capacity of facilities is determined by the common facilities and demand.
Claims (2)
1. A method of spatial accessibility evaluation of urban facility services based on GIS, characterized in that: in the implementation of data visualization, ECharts is applied, it is a pure JavaScript icon library and a very good visualization of the front frame, Echarts can provide the front-end development with intuitive, vivid, interactive, highly personalized custom data visualization, and make our results more intuitive image.
2. According to method of claim 1, wherein in the analysis of accessibility of urban facilities, OD distance cost matrix is used, which is a table of connected costs between multiple demand points and multiple facility points, during analysis, data can be imported and the shortest path can be processed in batches, the operation is relatively accurate and simple.
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