Urban open space accessibility evaluation method based on Ga2SFCA

Abstract

The invention discloses a city open space accessibility evaluation method based on Ga2SFCA. The method comprises the following steps: acquiring basic data from a city physical examination platform; preprocessing the image data by using remote sensing software; dividing a research area into grid units, and taking each unit as a demand point; determining a supply point position and calculating an attraction coefficient; obtaining the number of people of each demand unit; performing topology check and repair on the road network data; constructing a Gaussian two-step mobile search method model; introducing a Gaussian distance attenuation function; generating a table according to a model calculation result, and generating a spatial distribution diagram; and rendering the generated surface map layer to complete the visual analysis of the accessibility of the urban open space. The evaluation result can be visually presented through an ArcGIS visual tool, data support and decision basis are provided for urban planning, optimization of open space layout and traffic network design is facilitated, and scientificity and operability of overall urban planning are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning and space analysis, and in particular to an urban open space accessibility evaluation method based on Ga2SFCA. Background Art

[0002] Existing methods for evaluating urban open space accessibility typically focus on green spaces, plazas, and sports spaces as the primary data sources for public open space. However, this approach overlooks open spaces such as urban waterfronts, where citizens can participate in activities, and fails to fully reflect the diversity and actual utilization of open space. Urban waterfronts, as a key type of open space, possess high ecological, landscape, and social value. Their omission from accessibility assessments can lead to one-sided evaluation results.

[0003] Existing technologies typically use a fixed distance as the distance threshold for the two-step search method, failing to consider the differences in service radius between different levels of open spaces. When using a fixed distance threshold to evaluate open spaces of different levels, this method tends to exaggerate the service range of lower-level open spaces (such as pocket parks), thereby overestimating their accessibility. This can lead to inflated accessibility evaluation results for local areas, compromising the accuracy and scientific nature of the evaluation.

[0004] Existing methods for calculating population counts at demand points often rely on mobile phone signaling data. While this method can reflect population distribution over a specific period, it's not fully consistent with statistical caliber. Firstly, mobile phone signaling data struggles to distinguish the registered residence of permanent residents, making it difficult to accurately determine the ratio of migrant to permanent residents. Secondly, mobile phone signaling data struggles to capture populations that don't use mobile phones, such as the elderly and children, leading to discrepancies between statistical results and actual conditions. Furthermore, the acquisition and processing of mobile phone signaling data involves users' personal privacy, making it complex and posing significant security and legal risks.

[0005] To obtain population counts for grid cells, existing technologies often use point-level population distribution data and interpolate using the inverse distance weighted method (IDW). However, the accuracy of this calculation method depends on the quantity, quality, and distribution of sample data. If sample data is insufficient or unevenly distributed, the prediction results may be significantly biased. Furthermore, errors or outliers in the sample data may reduce the reliability and scientific nature of the prediction results.

[0006] Existing methods for calculating the supply capacity of supply points typically use only the area of ​​open space as a metric, failing to consider heterogeneous factors such as its quality and visitor appeal. This approach overlooks important factors such as internal facilities, landscape quality, and ease of use, making it difficult to accurately reflect the actual supply capacity of open spaces and resulting in a somewhat one-sided evaluation.

[0007] To determine the location of supply points, existing technologies typically use ArcGIS's Feature to Point tool, using the center point of the open space as the supply point location. This approach fails to fully consider the locational characteristics and main entrance and exit locations of open spaces, fails to reflect the actual service scope of large open spaces, and makes the evaluation results lack specificity and accuracy. In summary, existing technologies for evaluating the accessibility of public open spaces suffer from incomplete data sources, unreasonable model assumptions, unscientific demographic methods, one-sided supply capacity assessments, and a lack of precision in determining the location of supply points, limiting the scientific nature and practical application value of the evaluation results. Summary of the Invention

[0008] To overcome the shortcomings of the existing technology, the present invention proposes an urban open space accessibility evaluation method based on Ga2SFCA, which introduces four types of open space data: urban green space, urban square space, urban sports space and urban waterfront space. It comprehensively covers various types of public spaces that citizens participate in on a daily basis, making the evaluation results more in line with actual needs and the usage scenarios of open spaces.

[0009] To achieve the above objectives, the present invention provides an urban open space accessibility evaluation method based on Ga2SFCA, comprising the following steps: Step 1: Obtain basic data from the urban health check platform, including remote sensing image data, land use data, road network data, urban open space related data, annual average pedestrian flow data, POI data and administrative population data.

[0010] Step 2: Use remote sensing software to pre-process the image data, including geometric correction, radiometric calibration, and atmospheric correction. Combined with field surveys, the accuracy of the interpreted data is verified, and vectorized results of data such as urban green spaces, square spaces, sports spaces, and waterfront spaces are extracted.

[0011] Step 3: Use ArcGIS software to divide the study area into 500×500 meter grid cells, and each cell is used as a demand point.

[0012] Step 4: Determine the location of supply points in open spaces, using the center of mass for small spaces and the entrances and exits for large spaces, and calculate the attractiveness coefficient for each supply point. The attractiveness coefficient takes into account the area, average annual traffic volume, and infrastructure score.

[0013] Step 5: Based on POI data and administrative population data, the kernel density calculation method is combined with principal component analysis to obtain the population of each demand unit.

[0014] Step 6: Check and repair the topology of the road network data, set the travel speed according to different levels, generate a network dataset in ArcGIS software, and use the OD cost matrix to calculate the time cost from the demand point to the supply point.

[0015] Step 7: Construct a Gaussian two-step moving search model, calculate the supply-demand ratio within the service range with the supply point as the center, then summarize the supply-demand ratio within the service range with the demand point as the center, and calculate the accessibility of each demand point.

[0016] Step 8: Set time cost thresholds based on different open space levels and introduce a Gaussian distance decay function to optimize the accuracy of supply-demand ratio calculation.

[0017] Step 9: Generate a table with the model calculation results. Use ArcGIS's spatial connection tool to map the accessibility results to demand points to generate a spatial distribution map. Render the generated surface layer to complete a visual analysis of the accessibility of urban open spaces, and make improvement suggestions for low-accessibility areas based on actual conditions.

[0018] Furthermore, step 1 is as follows: Step 1.1: Collect remote sensing image data, land use data, and road network data of the study area from the urban health examination platform; Step 1.2: Obtain a list and classification standards for open spaces such as urban green spaces, plaza spaces, sports spaces, and waterfront spaces; Step 1.3: Collect internal facility data for open spaces, including average annual foot traffic, sidewalks, public seating, restrooms, parking lots, and other facility information; Step 1.4: Obtain various POI data (residential facilities, commercial facilities, cultural facilities, educational facilities, public service facilities, transportation facilities) and administrative population data within the study area; Step 1.5: Ensure the integrity and consistency of data sources and that the data format is suitable for subsequent analysis.

[0019] Furthermore, step 2 is as follows: Step 2.1: Use remote sensing software to perform geometric correction on remote sensing image data to ensure that the geographic coordinates match the actual location; Step 2.2: Perform radiometric calibration and atmospheric correction to remove light and atmospheric interference from the image; Step 2.3: Use supervised classification methods to extract land use information from the image and identify urban green spaces, square spaces, sports spaces, and waterfront spaces; Step 2.4: Check and revise the classification results through field surveys to ensure that the classification accuracy reaches above 85%; Step 2.5: Fuse the image data with other geographic data to provide a basis for subsequent vectorization processing.

[0020] Furthermore, step 3 is as follows: Step 3.1: Use the Fishnet tool in ArcGIS software to generate a 500 × 500 m grid cell within the study area. Step 3.2: Define each grid cell as a demand point to carry population and demand attributes; Step 3.3: Ensure that the gridding covers the entire study area without leaving any gaps; Step 3.4: Assign a unique number to each grid cell to facilitate subsequent attribute association and indexing operations.

[0021] Furthermore, step 4 is as follows: Step 4.1: Based on the open space inventory and vectorized data, determine the location of each open space and its hierarchical attributes (city level, residential area level, neighborhood level); Step 4.2: For small open spaces (such as community green spaces), take their centroid as the supply point location; for large open spaces (such as comprehensive parks), take their main entrances and exits as the supply point location; Step 4.3: Collect open space infrastructure data, including annual pedestrian traffic, sidewalks, public seating, restrooms, parking lots, and other facilities; Step 4.4: Calculate the attractiveness coefficient of the open space, combining the normalized area value with the quality score as the supply capacity of the supply point; Step 4.5: Store the calculation results in the supply point attribute table to provide basic data for subsequent model analysis.

[0022] Furthermore, step 5 is as follows: Step 5.1: Use the Kernel Density tool in ArcGIS software to perform kernel density calculation on various POI data and extract the density values ​​of various POIs for each demand point; Step 5.2: Use principal component analysis to calculate the weights of different POI types, perform weighted summation on the kernel density data of each demand point, and obtain the comprehensive weight value of each demand point; Step 5.3: Use the comprehensive weight value and administrative population data to perform proportional allocation and calculate the population within each demand point; Step 5.4: Check whether the calculated results are consistent with the total population of administrative demographic data to ensure the accuracy of the allocation results; Step 5.5: Store the population of each demand point in the demand point attribute table to provide data support for subsequent supply-demand ratio calculations.

[0023] Furthermore, step 6 is as follows: Step 6.1: Collect road network data and use ArcGIS topology tools to check data quality and fix dangling points, pseudo nodes, and line segment overlap issues. Step 6.2: Use the Extend Line tool to extend the broken road segments to ensure the integrity of the road network; Step 6.3: Use the Feature to Line tool to break the line segments at the intersection and generate new line features with the intersection as the endpoints; Step 6.4: Set the traffic speed according to the road class (highway, expressway, trunk road, branch road), taking into account the average driving speed during local peak hours; Step 6.5: Use ArcGIS's network analysis tools to create a network dataset with the demand point as the starting point and the supply point as the end point. Step 6.6: Use the OD cost matrix function to calculate the time cost from each demand point to the supply point, including the starting point, end point, and time cost attributes of the path; Step 6.7: Associate the time cost data with the demand points and supply points to form the OD time cost matrix.

[0024] Furthermore, step 7 is as follows: Step 7.1: Taking each supply point as the center, set the time cost thresholds of different levels of open space and determine the service scope; Step 7.2: Calculate the total population of all demand points within the service area and calculate the supply-demand ratio based on the attraction coefficient; Step 7.3: Taking each demand point as the center, set the corresponding time cost threshold and calculate the supply-demand ratio of all supply points within the service range; Step 7.4: Take the weighted sum of the supply-demand ratios within the range of the demand points to obtain the accessibility value of each demand point; Step 7.5: Use the Gaussian distance decay function to modify the supply-demand ratio, simulate the nonlinear effect of increasing distance on the service level, and improve calculation accuracy; Step 7.6: Output the final accessibility calculation results for each demand point.

[0025] Furthermore, step 8 is as follows: Step 8.1: Generate a data table based on the calculation results of the Gaussian two-step moving search method model and append the spatial index of the demand points; Step 8.2: Use the ArcGIS spatial join tool to link the accessibility calculation results of each demand point to the corresponding demand point; Step 8.3: Display the calculation results in the form of a surface layer, with each grid cell showing the corresponding accessibility value; Step 8.4: Check whether the generated surface layer matches the location and index of the demand point to ensure the correctness of the data.

[0026] Furthermore, step 9 is as follows: Step 9.1: Use ArcGIS software to render the generated surface layer in different levels, select appropriate color gradients to highlight accessibility differences, and set grading standards based on different accessibility levels, such as high, medium, and low. Step 9.2: Ensure that the rendering clearly shows the distribution of accessibility, including areas of high and low accessibility. Step 9.3: Analyze the causes of low accessibility, such as poor transportation, insufficient open space facilities, or high population density; Step 9.4: Output a complete visual analysis result diagram and provide a reference for subsequent urban planning scheme design; Step 9.5: Based on the visualization results, identify low-accessibility areas and analyze their causes based on the actual situation; Step 9.6: Propose improvement measures for low-access areas, such as increasing open space provision or improving infrastructure; Step 9.7: Integrate urban policy planning optimization plans to adjust the existing open space layout or increase transportation connectivity; Step 9.8: Incorporate the improvement plan into the urban planning proposal and validate the model to simulate the accessibility improvement effect of the improvement plan; Step 9.9: Output the optimized accessibility evaluation results to ensure the scientific nature and operability of the planning and design.

[0027] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention provides an urban open space accessibility evaluation method based on Ga2SFCA, which adopts the POI comprehensive density ratio method to calculate the permanent population of the basic unit, avoiding the problems of mobile phone signaling data in statistical caliber, privacy protection and acquisition difficulty, and improving the reliability and consistency of demographic data.

[0028] 2. This paper provides an urban open space accessibility evaluation method based on Ga2SFCA. It uses the attraction coefficient instead of the open space area to measure the supply capacity of the supply point, and incorporates quality factors such as the average annual passenger flow and infrastructure conditions into the evaluation. It overcomes the one-sidedness of using only area as an indicator and more comprehensively reflects the actual service capacity of the open space.

[0029] 3. The present invention provides an urban open space accessibility evaluation method based on Ga2SFCA. When determining the location of the supply point, the centroid of small open spaces and the main entrance and exit of large open spaces are taken, which more scientifically reflects the service scope of different open spaces and improves the accuracy of the evaluation results.

[0030] 4. The present invention provides an urban open space accessibility evaluation method based on Ga2SFCA. It uses the Gaussian two-step moving search model to evaluate open space accessibility and introduces a distance decay function to more accurately describe the accessibility differences within the service range. This avoids the problem of exaggerating the impact range of low-level open spaces that may be caused by traditional fixed distance thresholds. Different time cost thresholds are set for open spaces of different levels, comprehensively considering the scale and service range of the open space, avoiding the one-sidedness of subjective assumptions, and improving the scientific nature of the model and the credibility of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 This is a schematic diagram of the steps of the present invention Figure 2 This is a schematic diagram of the supply point Figure 3 This is a unit population distribution map calculated based on POI data. Figure 4 OD cost matrix line diagram Figure 5 This is the supply-demand ratio diagram of the supply point using the Gaussian two-step moving search method. Figure 6 Urban open space accessibility evaluation map DETAILED DESCRIPTION

[0033] The technical solutions of the present invention will be more clearly and completely explained below through description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0034] The present invention discloses an improved urban open space accessibility evaluation method based on Ga2SFCA, which evaluates and visualizes the accessibility of urban open spaces by introducing a Gaussian two-step moving search method model. Based on a list of urban parks and green spaces, a list of squares, a list of sports parks, a list of sports venues, and a list of water areas, vectorized data on urban green spaces, urban square spaces, urban sports spaces, and urban waterfront spaces and their grade attributes are obtained; based on open space data, remote sensing images, POI data, etc., the attractiveness coefficient of urban open spaces is calculated to obtain supply point attributes; the unit population is calculated from multiple types of POI data and the number of permanent residents to obtain demand point attributes; the OD time cost is calculated as the travel time distance cost by introducing a network analysis method; a corresponding time cost threshold is set according to the open space grade; the open space accessibility result of each unit is calculated using a Gaussian two-step moving search method model, and the accessibility of urban open spaces is visualized using ArcGIS software.

[0035] The present invention introduces the improved Ga2SFCA model into the urban planning process, improves the accessibility analysis level of open space, takes into account the connotation and concept of open space, and uses urban green space, urban square space, urban sports space and urban waterfront space as urban open space data, so as to make the evaluation more realistic; uses POI comprehensive density ratio calculation instead of mobile phone signaling data to calculate the permanent population of the basic unit, avoiding the problem of inconsistent statistical caliber; uses attraction coefficient instead of urban open space area to express the supply capacity of supply points, and uses small open space centroid and large open space entrance and exit as the supply point location, so as to make the evaluation results more scientific and objective. Figure 2 As shown in the figure, the Gaussian two-step moving search method model is used to evaluate the accessibility of open space. Taking into account that urban open spaces of different levels have different influence ranges, different time cost thresholds are used as search thresholds, which avoids the problems of human subjectivity and one-sidedness and improves the scientific nature of planning scheme design.

[0036] like Figure 1 As shown: (1) Based on the local urban health check platform of the study area, basic geographic information data and data preprocessing were obtained, including remote sensing image data, land use data, road network data, urban green space data, urban square space data, urban sports space data and urban waterfront space data, annual average pedestrian flow data, urban open space internal facility data, urban open space grade standards, administrative population data, various types of (residential facilities, commercial facilities, cultural facilities, educational facilities, public service facilities, transportation facilities) POI (Point of Interest) data, etc.; POI data were crawled from the AutoNavi map API and preprocessed using ArcGIS software, including deleting duplicate entries, points with low quality or missing information, and points with unclear functional positioning, and were divided into residential facilities, commercial facilities, cultural facilities, educational facilities, public service facilities, and transportation facilities according to their functions; Remote sensing image data comes from the National Geographic Information Public Service Platform; remote sensing software ENVI 5.3 was used to preprocess remote sensing images, including geometric correction, radiometric calibration, atmospheric correction, image fusion, and mosaic cropping. Information was extracted through supervised classification, and field surveys were conducted to check and correct the interpreted data. The interpretation accuracy was above 85%, and land use data for the study area was obtained. Road data came from the OSM open source map platform. The original OSM road data contained many topological errors. The Extend Line tool in ArcGIS software was used to process the original road network data, setting an appropriate threshold to extend the line segments a certain distance. Then, the Feature to Line tool was used to break the lines at the intersections, and new line features were generated with the intersections as endpoints. Finally, after manual verification, relatively accurate road network data was obtained. Data preprocessing includes data vectorization. Check the quality of image data, including clarity, resolution, and accuracy; use the Georeferencing tool in ArcGIS software to align the remote sensing data with the geographic coordinate system of the third national land survey vector data; find specific locations based on the list of urban parks and green spaces, squares, sports parks, sports venues, and water areas; and vectorize urban green spaces, urban squares, urban sports spaces, and urban waterfront data in turn according to the boundary contours in the remote sensing image data and the patch properties in the current land use data; determine the hierarchical attributes of each city's open space, including city level, residential level, and neighborhood level, based on the "Urban Green Space Classification Standard (CJJ / T 85-2017)" and the differences in the scale, radiation range, and main service objects of open spaces within urban construction areas; the city level includes comprehensive parks, transportation hubs, scenic spots, etc., with a scale of more than 10 hectares; the regional level includes parks, squares, transportation nodes, etc., with a scale of 5-10 hectares; and the community level includes community green spaces, community squares, etc., with a scale of 1-5 hectares; Based on the list of urban parks and green spaces, the list of squares, the list of sports parks, the list of sports venues, and the list of water areas, combined with remote sensing image data and the internal facility data of urban open spaces, we determine whether each open space has facilities such as sidewalks, public seats, toilets, parking lots, convenience stores, etc. and conduct statistics.

[0037] (2) Use ArcGIS software to divide the grid into 500×500m units, and use each unit as a demand point. According to previous studies, smaller research units can more effectively reflect the differences in facility accessibility within the region. At the same time, considering the suitable accessible range for residents' travel, the 500×500m grid unit commonly used in current accessibility research was used. The specific steps are: use the Fishnet tool of ArcGIS software to divide the units with a grid length of 500m and the study area as the spatial range, and use each unit as a demand point for subsequent calculations. (3) Preprocess the urban open space data to determine the location of the supply point, calculate the attraction coefficient of the urban open space, and obtain the attributes of the supply point; specifically, introduce the attraction coefficient S that takes into account the quality of the urban open space. j , to reveal the heterogeneous supply of open space, the attraction coefficient is calculated as follows:

[0038] Where, is the capacity of urban open space j considering only the area, k n is the normalized value of the total quality score of urban open space j; The quality score calculation of urban open spaces includes the annual average pedestrian flow score and the existence score, as shown in the following table:

[0039] The annual average flow of people in each city’s open space is normalized; the existence of the scoring type evaluation content is judged by whether it exists or not, that is, existence is scored as 1 point, and non-existence is scored as 0 point. The scores of all scoring factors are summed to obtain the quality score of the city’s open space, and the normalized score is obtained as k n .

[0040] (4) Calculate the permanent population data of each unit based on administrative population data and POI data; including residential facilities, commercial facilities, cultural facilities, educational facilities, and public service facilities, which are most relevant to the population, so use this type of POI data to calculate the unit population (such as Figure 3The kernel density of each POI data was calculated using the kernel density tool in ArcGIS software, and the kernel density values ​​of each type of POI at each demand point were obtained using the extract values ​​to points tool. The weight values ​​of each type of POI were calculated and normalized using principal component analysis. After obtaining the weights of each indicator, the comprehensive weight value of the population distribution in the study area was calculated using the following formula:

[0041] Where, F j is the comprehensive weight value of the jth unit, w i Refers to the weight of the i-th type of POI, P ij Refers to the POI value of the i-th category in the j-th unit; Use the following formula to get the population of each unit:

[0042] Where, POP j represents the population in the jth unit, and POP represents the administrative population data of the study area; Compared with mobile phone signaling data, POI data has the following advantages: Accessibility: POI data is usually publicly available from large map websites or geographic information systems (GIS). This data has often been organized and optimized for direct use. However, obtaining mobile phone signaling data may require cooperation with telecom operators, and the data format is complex, making data processing more difficult. Ease of processing: POI data mainly consists of geographic location and attribute information, and the data structure is relatively simple, making it easier to process. Mobile phone signaling data, on the other hand, contains a large amount of user behavior information and personal privacy information. During data processing and use, it is necessary to strictly comply with relevant laws and regulations and privacy protection policies, and complex algorithms and models are required for parsing and extraction.

[0043] Accuracy: POI data is generated based on human activities and is closely related to population size. It can accurately describe geographic location information, and attribute information such as name and type is also highly accurate, providing a reliable basis for calculating unit population. Mobile phone signaling data, while able to reflect user behavior, can be affected by various factors such as user activity habits and phone usage frequency when calculating population, resulting in reduced accuracy. (5) After topological inspection and repair of the road network data, the elevation attributes were retained. The city's roads were divided into four levels: expressways, expressways, trunk roads, and branch roads. The lines were interrupted at the intersections. For roads of different levels, the traffic speeds were set according to the local average driving characteristics during peak hours to facilitate network analysis in ArcGIS software. The OD cost matrix function of the network analysis in ArcGIS software was used to obtain the OD travel time cost from each demand point to multiple supply points as the time distance cost of the model. To perform topological inspection and repair on road network data, the New Topology tool in ArcGIS software is used to set topological rules for the road network data, including "Must Not Have Dangles" (line features cannot have dangle points), "Must Not Have Pseudos" (line features cannot have pseudo nodes), and "Not Overlap" (lines within the same feature class cannot overlap), and then generate a topology. The Error Inspector window of the topology is opened, and the topological error location is located by double-clicking. The road network data is then edited and repaired using the Editor toolbar. The main repair steps include: using the editing function or the Extend Line tool in ArcGIS software, setting an appropriate threshold to extend the line segment a certain distance, and then using the Feature to Line tool to break the line at the intersection and generate a new line feature with the intersection as the endpoint. Finally, after manual verification, relatively accurate road network data is obtained. Based on the road network, ArcGIS software is used to place the processed road network data into a newly created feature dataset and create a corresponding network dataset; a network is constructed based on the road network data, with the demand point as the starting point and the supply point as the end point; the Network Analyst tool is launched, and in the toolbar, select Generate Origin Destination Cost Matrix to load the corresponding origin and destination point data into the OD cost matrix to obtain the OD line and its attribute table, which includes the origin, destination, and OD time cost of each path; the OD time cost is used as the travel time distance cost. This calculation method can take into account factors such as natural obstacles and road conditions, reducing errors (such as Figure 4 shown); (6) Construct a Gaussian two-step moving search method model to evaluate the accessibility of urban open space, and generate a table of the accessibility calculation results. Use the spatial connection tool of ArcGIS to attach the accessibility calculation results of each demand point to the demand point according to the demand point index, generate a surface layer and render it, and obtain the visualization analysis results of the urban open space accessibility evaluation based on Ga2SFCA; The principle of the Gaussian two-step moving search model is as follows: The two-step floating catchment area method (2SFCA) is an accessibility calculation method based on spatial interactions. It is easy to understand, highly operational, and accurately reflects the relationship between supply and demand. Therefore, it is often used to calculate the accessibility of various public service facilities. The calculation process is as follows: The first step is to use facility j as the search center, find all the demands k within the service radius d0 of j, and calculate the supply-demand ratio R of each facility. j :

[0044] Where S j is the supply scale of facility j; P k is the demand level of demand point k within the service range; is the distance decay function, where d kj The distance between the demand point and the supply point.

[0045] Step 2: Taking demand k as the search center, find all facilities j within the reachable radius d0 of k. The supply-demand ratio of the summed facilities is the reachability of demand k. :

[0046] Where, P k is the supply-demand ratio of facilities within the accessible radius; d kj is the distance between the demand point and the supply point.

[0047] The traditional two-step moving search method assumes that resources are evenly distributed within the service radius, which is clearly inconsistent with reality. In reality, demand points closer to facilities receive better service. However, as the distance between supply and demand points increases, the service received by the demand point decreases, and this attenuation trend is nonlinear. Therefore, mathematical functions are introduced to simulate this attenuation trend, such as power-exponential pair functions, kernel density functions, and Gaussian functions. Among all 2SFCA modifications, the Gaussian two-step moving search method (Ga2SFCA) is the most widely used accessibility calculation model. It utilizes a Gaussian function to describe the distance decay effect, accurately reflecting accessibility differences within the service area. It avoids the monotonically increasing or decreasing attenuation rate with increasing distance that can occur with other distance decay functions, thereby improving the accuracy of accessibility evaluation.

[0048] The Gaussian function calculation formula is as follows:

[0049] The meaning of each variable in the formula is the same as that of the variables in the above formula.

[0050] The level of open space is determined based on the city's relevant policies and plans. The walking time for city-level open space is within 15 minutes, the walking time for residential-level open space is within 10 minutes, and the walking time for neighborhood-level open space is within 5 minutes. Different time cost thresholds are used for different levels of urban open space. The Gaussian two-step moving search model is used to calculate the accessibility of open space: Calculate the supply-demand ratio:

[0051] Where R j is the supply-demand ratio (e.g. Figure 5 ), represents the potential per capita open space area (square meters / person); i is the demand point; j is the supply point; S j The service capacity of the supply point is represented by the urban open space attraction coefficient in this paper; i is the scale of the demand point, expressed as the population in the unit (people); d ij is the travel time cost between demand point i and supply point j, which is expressed in meters in this paper; d0 is the predefined time cost threshold; G(d ij ) is the Gaussian distance decay function, which indicates that the park service capacity decreases with the increase of travel distance.

[0052] Calculate reachability:

[0053] Where A i The accessibility of the unit is represented by the supply-demand ratio R of the urban open space within the spatial scope with the time threshold d0, with each demand point i as the core. j Perform weighting and sum. Thus, we can get the accessibility A of each demand point. i , and use ArcGIS software to perform hierarchical visualization rendering (such as Figure 6 Overall, the accessibility evaluation results of demand points decrease outward from the supply points and are affected by road conditions, showing a non-uniform distribution. Some supply points have large areas but inconvenient transportation, lack corresponding supporting facilities, and have a monotonous landscape environment. Therefore, they are not very attractive to residents and the accessibility of surrounding demand points is low, which is consistent with the actual situation. On the other hand, open spaces located in population centers, with convenient transportation, large capacity, rich landscapes, and high-level supporting facilities, are highly attractive and have high accessibility to surrounding demand points.

[0054] As a specific implementation method, basic data is first obtained from the urban health check platform, including remote sensing image data, land use data, road network data, lists and classification standards of urban green spaces, plazas, sports spaces, and waterfront spaces, as well as annual average pedestrian flow data, various point of interest (POI) data, and administrative population data. The remote sensing image data is preprocessed, including geometric correction, radiometric calibration, and atmospheric correction. Urban open space information is extracted through supervised classification. The interpretation results are corrected based on field surveys and integrated with other geographic data to generate vectorized data of green spaces, plazas, sports spaces, and waterfront spaces.

[0055] Using ArcGIS software, the study area was divided into 500×500-meter grid cells. Each grid cell served as a demand point, providing a basis for subsequent supply and demand calculations. Supply point locations were determined based on the open space inventory and vectorized data: the centroid for small open spaces and the main entrance and exit for large open spaces. The average annual foot traffic and infrastructure (such as sidewalks, public seating, restrooms, and parking) of the open spaces were also analyzed to calculate the attraction coefficient, which served as the supply capacity of the supply point.

[0056] Based on various POI data, we used kernel density calculations to determine the POI density for each demand point. Principal component analysis was then used to calculate the weights for different POI types. We then used these weights and administrative population data to perform a proportional allocation, calculating the population within each demand point and verifying its consistency with the administrative population to ensure the accuracy of the allocation results.

[0057] Topology checks and repairs were performed on the road network data, addressing issues with dangling points, pseudo-nodes, and overlapping segments. Speed ​​limits were set based on road class, generating a complete network dataset. ArcGIS's network analysis tools were used to calculate the OD time cost from demand points to supply points, generating an OD cost matrix. The results were then linked to the demand and supply point data to provide input for model calculations.

[0058] In the Gaussian two-step moving search model, with the supply point as the center, a time cost threshold is set based on the open space level to calculate the supply-demand ratio within the service area. With the demand point as the center, a similar time cost threshold is set and the supply-demand ratio within the service area is summarized to obtain the accessibility value for each demand point. A Gaussian distance decay function is introduced to modify the supply-demand ratio to simulate the nonlinear effect of increasing distance on service level, improving the accuracy of the calculation results.

[0059] The model calculation results were generated into a data table, and accessibility values ​​were linked to demand points using the Spatial Join tool to generate a surface layer. ArcGIS software was used to render the surface layer in a graded manner, visually displaying the accessibility level of each area using a color gradient. A high, medium, and low grading system was established to highlight accessibility differences. The causes of low accessibility in these areas were also analyzed, such as poor transportation conditions, insufficient open space, or inadequate infrastructure, and targeted optimization recommendations were provided.

[0060] Based on the visualization results and analysis, the open space layout or transportation network is adjusted, the effect of the optimization plan is simulated, and the optimized accessibility evaluation results are incorporated into the urban planning recommendations to ensure the scientificity and rationality of the planning and design.

[0061] The above-described specific embodiments merely describe preferred embodiments of the present invention and do not limit the scope of protection of the present invention. Any modifications, substitutions, and improvements made to the technical solution of the present invention by a person skilled in the art based on the textual description and drawings provided herein, without departing from the design concept and spirit of the present invention, shall fall within the scope of protection of the present invention. The scope of protection of the present invention is determined by the claims.

Claims

1. The urban open space accessibility evaluation method based on Ga2SFCA is characterized by: The following steps are involved: Step 1: Obtain basic data from the urban health check platform, including remote sensing image data, land use data, road network data, urban open space data, annual average pedestrian flow data, POI data, and administrative population data; Step 2: Use remote sensing software to pre-process the image data, conduct accuracy verification of the interpreted data based on field surveys, and extract the vectorized results of the urban data; Step 3: Use ArcGIS software to divide the study area into 500 × 500 m grid cells, with each cell serving as a demand point; Step 4: Determine the location of the supply points in the open space, taking the center of mass for small spaces and the entrances and exits for large spaces, and calculate the attraction coefficient of each supply point; Step 5: Based on POI data and administrative population data, the kernel density calculation method is combined with principal component analysis to obtain the population of each demand unit; Step 6: Check and repair the topology of the road network data, set the speed according to different levels, generate a network dataset in ArcGIS software, and use the OD cost matrix to calculate the time cost from the demand point to the supply point; Step 7: Construct a Gaussian two-step moving search model to calculate the supply-demand ratio within the service range with the supply point as the center. Then, summarize the supply-demand ratio within the service range with the demand point as the center and calculate the accessibility of each demand point. Step 8: Set time cost thresholds based on different open space levels and introduce a Gaussian distance decay function to optimize the accuracy of supply-demand ratio calculation; Step 9: Generate a table of the model calculation results and use ArcGIS's spatial connection tool to map the accessibility results to the demand points to generate a spatial distribution map; The generated surface layer is rendered to complete the visual analysis of the accessibility of urban open spaces, and improvement suggestions are made for low-accessibility areas based on actual conditions.

2. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 1 is as follows: Step 1.1: Collect remote sensing image data, land use data, and road network data of the study area from the urban health examination platform; Step 1.2: Obtain the list and classification standards of urban open spaces; Step 1.3: Collect internal facility data of the open space; Step 1.4: Obtain various POI data and administrative population data within the study area; Step 1.5: Ensure the integrity and consistency of data sources and that the data format is suitable for subsequent analysis.

3. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 2 is as follows: Step 2.1: Use remote sensing software to perform geometric correction on remote sensing image data to ensure that the geographic coordinates match the actual location; Step 2.2: Perform radiometric calibration and atmospheric correction to remove light and atmospheric interference from the image; Step 2.3: Use supervised classification methods to extract land use information from the image and identify urban green spaces, square spaces, sports spaces, and waterfront spaces; Step 2.4: Check and revise the classification results through field surveys to ensure that the classification accuracy reaches above 85%; Step 2.5: Fuse the image data with other geographic data to provide a basis for subsequent vectorization processing.

4. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 3 is as follows: Step 3.1: Use the Fishnet tool in ArcGIS software to generate a 500 × 500 m grid cell within the study area. Step 3.2: Define each grid cell as a demand point to carry population and demand attributes; Step 3.3: Ensure that the gridding covers the entire study area without leaving any gaps; Step 3.4: Assign a unique number to each grid cell to facilitate subsequent attribute association and indexing operations.

5. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 4 is as follows: Step 4.1: Determine the location and hierarchical attributes of each open space based on the open space inventory and vectorized data; Step 4.2: For small open spaces, the centroid of the small open space is taken as the supply point location; for large open spaces, the main entrance and exit of the large open space is taken as the supply point location; Step 4.3: Collect open space infrastructure data; Step 4.4: Calculate the attractiveness coefficient of the open space, combining the normalized area value with the quality score as the supply capacity of the supply point; Step 4.5: Store the calculation results in the supply point attribute table to provide basic data for subsequent model analysis.

6. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 5 is as follows: Step 5.1: Use the Kernel Density tool in ArcGIS software to perform kernel density calculation on various POI data and extract the density values ​​of various POIs for each demand point; Step 5.2: Use principal component analysis to calculate the weights of different POI types, perform weighted summation on the kernel density data of each demand point, and obtain the comprehensive weight value of each demand point; Step 5.3: Use the comprehensive weight value and administrative population data to perform proportional allocation and calculate the population within each demand point; Step 5.4: Check whether the calculated results are consistent with the total population of administrative demographic data to ensure the accuracy of the allocation results; Step 5.5: Store the population of each demand point in the demand point attribute table to provide data support for subsequent supply-demand ratio calculations.

7. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 6 is as follows: Step 6.1: Collect road network data and use ArcGIS topology tools to check data quality and fix dangling points, pseudo nodes, and line segment overlap issues. Step 6.2: Use the Extend Line tool to extend the broken road segments to ensure the integrity of the road network; Step 6.3: Use the Feature to Line tool to break the line segments at the intersection and generate new line features with the intersection as the endpoints; Step 6.4: Set the speed limit based on the road grade, taking into account the average speed during peak hours. Step 6.5: Use ArcGIS's network analysis tools to create a network dataset with the demand point as the starting point and the supply point as the end point. Step 6.6: Use the OD cost matrix function to calculate the time cost from each demand point to the supply point, including the starting point, end point, and time cost attributes of the path; Step 6.7: Associate the time cost data with the demand points and supply points to form the OD time cost matrix.

8. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 7 is as follows: Step 7.1: Taking each supply point as the center, set the time cost thresholds of different levels of open space and determine the service scope; Step 7.2: Calculate the total population of all demand points within the service area and calculate the supply-demand ratio based on the attraction coefficient; Step 7.3: Taking each demand point as the center, set the corresponding time cost threshold and calculate the supply-demand ratio of all supply points within the service range; Step 7.4: Take the weighted sum of the supply-demand ratios within the range of the demand points to obtain the accessibility value of each demand point; Step 7.5: Use the Gaussian distance decay function to modify the supply-demand ratio to simulate the nonlinear effect of increasing distance on the service level; Step 7.6: Output the final accessibility calculation results for each demand point.

9. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 8 is as follows: Step 8.1: Generate a data table based on the calculation results of the Gaussian two-step moving search method model and append the spatial index of the demand points; Step 8.2: Use the ArcGIS spatial join tool to link the accessibility calculation results of each demand point to the corresponding demand point; Step 8.3: Display the calculation results in the form of a surface layer, with each grid cell showing the corresponding accessibility value; Step 8.4: Check whether the generated surface layer matches the location and index of the demand point to ensure the correctness of the data.

10. The urban open space accessibility evaluation method based on Ga2SFCA according to claim 1 is characterized in that: Step 9 is as follows: Step 9.1: Use ArcGIS software to render the generated surface layer in different levels, select appropriate color gradients to highlight accessibility differences, and set grading standards according to different accessibility levels; Step 9.2: Ensure that the rendering clearly shows the distribution of accessibility, including areas of high and low accessibility. Step 9.3: Analyze the causes of low accessibility areas; Step 9.4: Output a complete visual analysis result diagram and provide a reference for subsequent urban planning scheme design; Step 9.5: Based on the visualization results, identify low-accessibility areas and analyze their causes based on the actual situation; Step 9.6: Propose improvement measures for low accessibility areas; Step 9.7: Integrate urban policy planning optimization plans to adjust the existing open space layout or increase transportation connectivity; Step 9.8: Incorporate the improvement plan into the urban planning proposal and validate the model to simulate the accessibility improvement effect of the improvement plan; Step 9.9: Output the optimized accessibility evaluation results to ensure the scientific nature and operability of the planning and design.

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