Dynamic Monitoring Method for the Supporting Level of Community Living Circle Facilities Based on Self-Service Index

Abstract

The present invention proposes a dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index, including the following steps: Step 1: Divide the regions within the research scope into plot units and assign attributes to each plot unit; Step 2: Identify the residential and workplace of permanent residents, then screen the daily travel data of permanent residents within the research scope that does not include commuting travel data, and finally construct a directed connection network N between plot units based on the daily travel data; Step 3: Calculate the self-service index S of each community living circle within the research scope based on the directed connection network N, the plot unit code UID and the affiliated community living circle code LCID in Step 1 i , then classify the community living circles according to the level of the self-service index from high to low, and finally draw a spatial distribution map. The present invention can realize the dynamic monitoring of the "supply-use" situation between the community living circle facilities and residents within the research scope, so as to formulate long-term and effective classification governance strategies

Description

Technical Field

[0001] The present invention belongs to the technical field of community living circles, and particularly relates to a dynamic monitoring method for the supporting level of community living circle facilities based on a self-service index. Background Art

[0002] Since the "15-minute community living circle" was proposed in the Shanghai Urban Master Plan (2017-2035), cities such as Beijing, Guangzhou, Hangzhou, Chengdu, Wuhan, Changsha, Jinan, Xiamen, and Haikou have successively launched community living circle planning. In 2018 and 2021, the Ministry of Housing and Urban-Rural Development and the Ministry of Natural Resources successively issued the "Urban Residential Area Planning and Design Standard (GB50180-2018)" and the "Technical Guide for Community Living Circle Planning (TD / T 1062-2021)" (hereinafter referred to as the "Standard" and the "Guide"), marking the transformation of China's community planning from the residential area mode to the living circle mode.

[0003] Currently, there are mainly three evaluation methods for the service level of community living circle supporting facilities:

[0004] One is to evaluate the construction level of supporting facilities within the living circle based on a questionnaire survey of residents' satisfaction, reflecting the advantages and disadvantages of public service facility support from the perspective of users. Literature [1] conducted a field survey in Zhiyin Dongyuan Community in Wuhan, collected the satisfaction evaluations of community residents on 14 types of facilities, and proposed an update strategy for community public service facilities based on the results of 194 valid questionnaires obtained.

[0005] The second is to determine whether it meets the standards based on the configuration requirements in the "Standard" and the "Guide". Literature [2] determined the presence or absence of 18 types of facilities in each community living circle in the central urban area of Wuhan. If it exists, it is recorded as meeting the standard; if not, it is not up to the standard, and the community living circles are classified according to the number of types of facilities that meet the standards.

[0006] The third is to evaluate the spatial accessibility of supporting facilities based on geography, deriving indicators such as coverage rate, accessibility, and walkability. Literature [3] used the Urban Network Analysis Toolbox to evaluate the coverage of 13 types of facilities in the community living circles of Nansha Urban Area in Guangzhou. The coverage rate is defined as the ratio of the number of residential buildings that can reach the facilities by walking to the total number of residential buildings in the living circle; Literature [4] considered the impact of distance on residents' walking to supporting facilities based on the distance decay function, measured the current situation and planned walkability of the community living circles in Huangpu District, Shanghai, and evaluated the rationality of facility configuration.

[0007] The first method has a high cost for collecting residents' subjective evaluations, a limited number of samples collected, and it is difficult to form a comprehensive understanding of the evaluation of the city-wide living circles. Moreover, it is easily affected by residents' subjective preferences, and its representativeness is difficult to guarantee.

[0008] The second method is the direct application of the two normative documents of the "Standard" and the "Guide", ignoring the differences in urban characteristics and residents' behaviors. Judging compliance only by the presence or absence of facilities cannot reflect the rationality of facility layout either.

[0009] The third method is an optimization of the second method, which can more carefully and accurately reflect the distribution level of supporting facilities in the living circle. However, affected by the construction area and service quality of the facilities, it cannot reflect the actual usage of the living circle facilities by residents. In particular, it does not consider the general situation of residents using public service facilities across living circles under e-commerce and different modes of transportation. Summary of the Invention

[0010] The purpose of the present invention is to provide a dynamic monitoring method for the supporting facility level of community living circles based on the self-service index, which can dynamically monitor the "supply - use" situation of community living circle facilities and residents within the research scope, so as to formulate long-term and effective classification governance strategies. To achieve the above purpose, the following technical solutions are adopted:

[0011] A dynamic monitoring method for the supporting facility level of community living circles based on the self-service index, comprising the following steps:

[0012] Step 1: Divide the region within the research scope into plot units, and assign attributes to each plot unit. The attribute types include plot unit code UID and the code LCID of the affiliated community living circle;

[0013] Among them, there are several community living circles within the research scope, and each community living circle contains multiple plot units;

[0014] Step 2: Take all plot units as objects, obtain the mobile location service data of all users in the plot units within the set time period, identify the place of residence and workplace of the permanent residents based on the mobile location service data, then screen the daily travel data of the permanent residents within the research scope that does not include commuting travel data, and finally construct a directed connection network N between plot units based on the daily travel data;

[0015] Among them, the directed connection network N contains several directed connections;

[0016] The starting point of the directed connection is the plot unit where the place of residence of the permanent resident is located;

[0017] The end point of the directed connection is the plot unit where the non-workplace where the permanent resident's daily non-commuting activities occur is located;

[0018] Step 3: Based on the directed connection network N, the plot unit code UID and the code LCID of the affiliated community living circle in Step 1, calculate the self-service index S of each community living circle within the research scope i, and then classify the community living circles according to the self-service index from high to low, and finally draw a spatial distribution map;

[0019] Among them, the self-service index S of each community living circle i is the ratio of the number of directed connections within the circle to the total number of directed connections.

[0020] Preferably, step 2 specifically includes the following steps:

[0021] Step 201: Identify permanent residents;

[0022] Step 202: Identify the place of residence of permanent residents and the place of work of permanent residents;

[0023] Step 203: Select a number of consecutive working days, and establish a directed connection network N, which specifically includes the following steps:

[0024] Step 203A: Compare the place of residence and the place of work of permanent residents identified in step 202, and screen the daily travel data of permanent residents within the research scope;

[0025] Among them, the daily travel data is the data between the plot unit where the place of residence of permanent residents is located and the plot unit where other activity places where non-commuting activities occur are located;

[0026] Step 203B: Define directed connections;

[0027] Step 203C: Take the daily travel data obtained in step 203A as the traversal object, and extract all the directed connections in the daily travel data;

[0028] Step 203D: Summarize the directed connections that appear in step 203C to obtain the directed connection network N from the place of residence to other activity places between plot units.

[0029] Preferably, step 201 specifically includes the following steps:

[0030] Based on the mobile phone location service data within a set time period, count the location information of each user for multiple days. If the proportion of its location within the research scope exceeds the set proportion, then it is identified as a permanent resident within the research scope.

[0031] Preferably, step 202 specifically includes the following steps:

[0032] For the mobile phone location service data of permanent residents within a set time period, judge the residence frequency of permanent residents during the working hours on weekdays in each plot unit, and extract the unit where the residence frequency dc in a single unit exceeds the set value as the place of work of permanent residents;

[0033] For the mobile location service data of permanent residents within a set time period, determine the residence frequency of permanent residents in each plot unit during the night time period, and extract the units where the residence frequency nc in a single unit exceeds the set value as the residence places of permanent residents.

[0034] Preferably, the set time period in step 202 is 91 days; the selected consecutive 5 working days in step 203.

[0035] Preferably, step 3 specifically includes the following steps:

[0036] Step 301: Classify the directed connections in the directed connection network N into in-circle directed connections and out-of-circle directed connections, which specifically includes the following steps:

[0037] For any directed connection, determine whether the community life circle code of the residence place of the permanent resident is the same as the community life circle code of the other activity place;

[0038] If they are the same, classify it as an in-circle directed connection; otherwise, classify it as an out-of-circle directed connection;

[0039] Step 302: Take the community life circle where the residence place of the permanent resident is located as the statistical unit, and count the number a of in-circle directed connections i and the number b of out-of-circle directed connections i ;

[0040] where i is the community life circle code LCID;

[0041] Step 303: Calculate the self-service index S of each community life circle i ;

[0042] S i = a i / (a i + b i )

[0043] Step 304: Classify the community life circles according to the level of the self-service index;

[0044] Step 305: Draw the spatial distribution map of the self-service index S i :

[0045] Use different marks to distinguish different types of community life circles in the map obtained in step 1 to form a spatial distribution map;

[0046] Step 306: Execute steps 2 to 305 to realize the regular observation of the change of the self-service index of each community life circle.

[0047] Compared with the prior art, the advantages of the present invention are:

[0048] (1) The present invention selects mobile location service data to replace the subjective evaluation survey of residents, which can not only truly reflect the daily activities of residents and the usage of facilities, but also evaluate the living circles of the whole city under the same standard.

[0049] (2) The dynamic monitoring method proposed by the present invention emphasizes examining the construction level of the community living circle from the longitudinal time dimension, avoiding the irrationality of horizontal comparison between cities caused by urban characteristics and differences in residents' behaviors.

[0050] (3) The self-service index selected by the present invention evaluates the construction level of the supporting facilities of the community living circle from the perspective of residents' subjective use. The quantity and layout of facilities, construction area, service quality, and transportation methods are all reflected in the travel preference choices of residents.

[0051] At the method level, the self-service index provides an evaluation from the perspective of residents' subjective use with the help of big data of mobile location services. The self-service index can not only measure the self-sufficiency ability of the living circle, but also identify the needs of residents to use external resources across circles. Based on the self-service index of the community living circle, the "supply - use" relationship between the construction of community living circle facilities and residents' living needs can be further explored, revealing the internal formation mechanism and external multi-scale spatial structure of the community living circle.

[0052] At the practical level, the self-service index helps to dynamically monitor the "supply - use" situation of community living circle facilities and residents within the research scope, transforming from a static ideal model to a dynamic usage model, so as to formulate long-term and effective classification governance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a schematic flow chart of the dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index;

[0054] Figure 2 is a spatial distribution map of the community living circles in Shanghai based on the self-service index. DETAILED DESCRIPTION OF THE INVENTION

[0055] The following will describe in more detail the dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index of the present invention with reference to the schematic diagrams, in which the preferred embodiments of the present invention are shown. It should be understood that those skilled in the art can modify the present invention described herein while still achieving the beneficial effects of the present invention. Therefore, the following description should be understood as broad guidance for those skilled in the art and not as a limitation to the present invention.

[0056] As Figure 1 shown, a dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index includes the following steps:

[0057] Step 1: Divide the areas within the research scope into plot units, and assign attributes to each plot unit. The attribute types include plot unit code UID and the code LCID of the community living circle to which it belongs.

[0058] Among them, there are several community living circles within the research scope, and each community living circle contains multiple plot units, that is, one code LCID of the community living circle corresponds to multiple plot unit codes UID.

[0059] The number of community living circles is the actual existing social living circles, which can be actually counted by existing technologies.

[0060] The number of plot units within each community living circle is restored according to the actual quantity.

[0061] In addition, before dividing the plot units, it is necessary to restore the map of the research scope according to the drawing scale.

[0062] It is known from existing technologies that: based on ArcGIS, plot units can be manually divided with the actual road centerlines and actual water system centerlines as boundaries, or plot units can be automatically divided with a grid network.

[0063] Step 2: Take all plot units as objects, obtain the mobile location service data of all users in the plot units within the set time period, identify the residence and workplace of the permanent residents based on the mobile location service data, then filter the daily travel data (excluding commuting travel data) of the permanent residents within the research scope, and finally construct a directed connection network N between plot units based on the daily travel data. Specifically, it includes the following steps:

[0064] Step 201: Identify permanent residents.

[0065] Based on the mobile location service data within the set time period, count the location information of each user over multiple days (such as 91 consecutive days). If the proportion of its location within the research scope exceeds a certain proportion (such as 60%), then it is identified as a permanent resident within the research scope.

[0066] Step 202: Identify the residence of the permanent residents and the workplace of the permanent residents.

[0067] For the mobile location service data of the permanent residents within the set time period, judge the residence frequency of the permanent residents during the working hours on weekdays (such as 9:00 - 17:00) in each plot unit, and extract the unit where the residence frequency dc in a single unit exceeds a certain proportion (such as 60%) of the set time period as the workplace of the permanent residents.

[0068] Among them, the residence frequency dc in a single unit is the number of residence times of the permanent residents during the working hours on weekdays in a single unit.

[0069] For the mobile location service data of permanent residents within a set time period, determine the residence frequency of permanent residents in each plot unit during the night time period (e.g., 21:00 - 6:00 the next day), and extract the units where the residence frequency nc in a single unit exceeds a certain proportion (e.g., 60%) of the set time period as the residence places of permanent residents.

[0070] Among them, the residence frequency ns of a single unit is the number of times a permanent resident stays in a single unit during the night time period.

[0071] Step 203: Based on a selected number of consecutive working days, establish a directed connection network N. Specifically, it includes the following steps:

[0072] Step 203A: Compare with the residence places of permanent residents identified in step 202 and the workplaces of permanent residents, and screen the daily travel data (excluding commuting travel) of permanent residents within the research scope.

[0073] The daily travel data is the directed connection data between plots, starting from the plot unit where the residence place of the permanent resident is located and ending at the plot unit where other activities (excluding commuting activities) occur.

[0074] For example, if a permanent resident has three non-commuting activity places A, B, and C during a selected number of consecutive working days, and goes to place A twice, place B once, and place C once, it is recorded as generating 4 pieces of daily travel data.

[0075] Specifically, it includes the following steps:

[0076] First, compare with the residence places of permanent residents identified in step 202, and identify the plot unit where the residence place of each permanent resident is located in the mobile location service data of permanent residents within the selected number of working days;

[0077] Compare with the residence places of permanent residents identified in step 202 and the workplaces of permanent residents, and identify the plot unit where other activity places (non-workplaces) of each permanent resident are located in the mobile location service data of permanent residents within the selected working days.

[0078] That is, if a plot unit is neither the residence place of a permanent resident nor the workplace of a permanent resident, it is classified as the plot unit where other activity places of the permanent resident are located.

[0079] Step 203B: Define the directed connection.

[0080] Take the plot unit where the residence place of the permanent resident is located as the starting point of the directed connection;

[0081] Take the plot unit where other activity places generated by the daily non-commuting activities of the permanent resident are located as the end point of the directed connection.

[0082] For a certain regular user, within a certain working day, their mobile phone location service data appears in four plot units A, B, C, and D. Among them, A is the plot unit where the residence is located, B is the plot unit where the workplace is located, and C and D are the plot units where other activity locations are located. Then, the connections from A→C and A→D are respectively recorded as a directed connection.

[0083] For the same regular user, the directed connections corresponding to different working days may be different. That is, on different working days, the locations of other activities may also be different.

[0084] Step 203C: Using the daily travel data obtained in Step 203A as the traversal object, extract all the directed connections in the daily travel data. Specifically, it includes the following steps:

[0085] First, traverse the daily travel data obtained in Step 203A, and record the plot units where the mobile phone location service data of each regular user appears on each working day;

[0086] After that, search the daily travel data obtained in Step 203A to establish the directed connections that appear on each working day.

[0087] Step 203D: Summarize the directed connections that appear in Step 203C to obtain a directed connection network N from the residence to other activity locations between plot units.

[0088] Step 3: Based on the directed connection network N and the plot unit code UID and the affiliated community living circle code LCID in Step 1, calculate the self-service index S of each community living circle within the research scope i , and then classify the community living circles according to the level of the self-service index. Finally, draw a spatial distribution map and conduct dynamic monitoring. Specifically, it includes the following steps:

[0089] Step 301: Classify the directed connections in the directed connection network N into in-circle directed connections and out-of-circle directed connections. Specifically, it includes the following steps:

[0090] For any directed connection, judge whether the affiliated community living circle code of the residence of the regular user is the same as the affiliated community living circle code of the other activity location.

[0091] If they are the same, it is classified as an in-circle directed connection; otherwise, it is classified as an out-of-circle directed connection.

[0092] Step 302: Taking the community living circle where the residence of the regular user is located as the statistical unit, count the number a of in-circle directed connections i and the number b of out-of-circle directed connections i .

[0093] Step 303: Calculate the self-service index S of each community living circlei 。

[0094] S i = a i / (a i + b i )

[0095] Where i is the community living circle code LCID.

[0096] That is, the self-service index of each community living circle is the ratio of the number of directed connections within the circle to the total number of directed connections.

[0097] Step 304: Classify the community living circles according to the level of the self-service index.

[0098] Draw a histogram of the self-service index of the community living circles within the research scope, and select three benchmark values of "high", "medium", and "low" using the natural break point method according to the numerical distribution characteristics shown in the histogram. There are differences in the performance of different regions.

[0099] Step 305: Draw a spatial distribution map of the self-service index S i .

[0100] Use different marks to distinguish different types of community living circles in the map obtained in Step 1 to form a spatial distribution map.

[0101] Step 306: Execute Step 2 to Step 305, that is, regularly collect the mobile phone location service data in Step 2 to realize the regular observation of the change of the self-service index of each community living circle, and dynamically monitor the "supply - use" situation of the supporting facilities and residents in the whole city's living circles.

[0102] In this case, the research scope is the Shanghai urban area, which contains 669 community living circles, and the plot unit is a 100m * 100m grid generated by ArcGIS.

[0103] Select the mobile phone location service data that appeared in Shanghai from April 1 to June 30, 2018, for a total of 91 days, involving about 56 million users, and the average daily number of users is about 7.3 million.

[0104] According to the number of days of appearance in Shanghai exceeding 60 days as the basis for judging permanent residents, about 3.068 million permanent residents in Shanghai are identified.

[0105] Based on the data records of permanent residents, respectively judge the residence frequency of each plot unit during the working hours (9:00 - 17:00) and night hours (21:00 - 6:00 the next day) of the users, and extract the units with a residence frequency exceeding 60% of the total number of days (91 days) in a single plot unit as the residence and work place of the permanent resident.

[0106] Select the mobile location service data for the five working days from May 14th to 18th, 2018 in Shanghai. Delete the data records generated at the workplace and its vicinity of permanent residents. Take the plot unit where the residence of permanent residents is located as the starting point, and the plot unit where other activity locations generated by their daily non-commuting activities are located as the ending point to form a directed connection between plot units.

[0107] Summarize the directed connections of all permanent residents within five days to construct a directed connection network between plot units.

[0108] Calculate the self-service index of 669 community living circles in Shanghai, and classify the community living circles into three categories according to the self-service index (Table 1), draw a spatial distribution map ( Figure 2 ) and conduct dynamic monitoring.

[0109] Table 1

[0110]

[0111] The above are only the preferred embodiments of the present invention and do not impose any limitation on the present invention. Any person skilled in the art within the technical field, without departing from the technical solution of the present invention, makes any form of equivalent replacement or modification and other changes to the technical solution and technical content disclosed by the present invention, which are all within the content of the technical solution of the present invention and still fall within the protection scope of the present invention.

Claims

1. A dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index, characterized in that It includes the following steps: Step 1: Divide the area within the research scope into plot units, and assign attributes to each plot unit. The attribute types include plot unit code UID and the code LCID of the affiliated community living circle; Among them, there are several community living circles within the research scope, and each community living circle contains multiple plot units; Step 2: Take all plot units as objects, obtain the mobile location service data of all users in the plot units within a set time period, identify the residence and workplace of the permanent residents based on the mobile location service data, then screen the daily travel data of the permanent residents within the research scope that does not include commuting travel data, and finally construct a directed connection network N between plot units based on the daily travel data; Among them, the directed connection network N contains several directed connections; The starting point of the directed connection is the plot unit where the residence of the permanent resident is located; The end point of the directed connection is the plot unit where the non-workplace of the non-commuting activities of the permanent resident is located every day; Step 3: Based on the directed connection network N, the plot unit code UID and the affiliated community living circle code LCID in Step 1, calculate the self-service index S of each community living circle within the research scope i , then classify the community living circles according to the level of the self-service index, and finally draw a spatial distribution map; Among them, the self-service index S of each community living circle i is the ratio of the number of directed connections within the circle to the total number of directed connections.

2. The dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index according to claim 1, wherein, Step 2 specifically includes the following steps: Step 201: Identify permanent residents; Step 202: Identify the residence of the permanent resident and the workplace of the permanent resident; Step 203: Select several consecutive working days to establish a directed connection network N, which specifically includes the following steps: Step 203A: Compare the residence and workplace of the permanent residents identified in Step 202, and screen the daily travel data of the permanent residents within the research scope; Among them, the daily travel data is the data between the plot unit where the residence of the permanent resident is located and the plot unit where other activity places generated by non-commuting activities are located; Step 203B: Define directed connections; Step 203C: Take the daily travel data obtained in Step 203A as the traversal object, and extract all the directed connections in the daily travel data; Step 203D: Summarize the directed connections that appear in Step 203C to obtain the directed connection network N from the residence to other activity places between plot units.

3. The dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index according to claim 2, wherein Step 201 specifically includes the following steps: Based on the mobile location service data within a set time period, count the location information of each user for multiple days. If the proportion of its location within the research scope exceeds the set proportion, then it is identified as a permanent resident within the research scope.

4. The dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index according to claim 2, wherein, Step 202 specifically includes the following steps: For the mobile location service data of permanent residents within a set time period, judge the residence frequency of permanent residents during the working hours of working days in each plot unit, and extract the unit where the residence frequency dc in a single unit exceeds the set value as the workplace of the permanent resident; For the mobile location service data of permanent residents within a set time period, judge the residence frequency of permanent residents during the night time in each plot unit, and extract the unit where the residence frequency nc in a single unit exceeds the set value as the residence of the permanent resident.

5. The dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index according to claim 4, characterized in that The set time period in Step 202 is 91 days; five consecutive working days are selected in Step 203.

6. The dynamic monitoring method for the supporting level of community living circle facilities based on the self-service index according to claim 1, wherein Step 3 specifically includes the following steps: Step 301: Classify the directed connections in the directed connection network N into in-circle directed connections and out-of-circle directed connections, which specifically includes the following steps: For any directed connection, determine whether the community living circle code of the place of residence of the permanent user is the same as the community living circle code of the other activity place; If they are the same, it is classified as a directed connection within the circle; otherwise, it is classified as a directed connection outside the circle; Step 302: Taking the community living circle where the residence of the regular user is located as the statistical unit, count the number of directed connections a within the circle i and the number of directed connections b outside the circle i ; where i is the community living circle code LCID; Step 303: Calculate the self-service index S of each community living circle i ; S i = a i / (a i + b i ) Step 304: Classify the community living circles according to the self-service index from high to low; Step 305, draw the spatial distribution map of the self-service index S i : Use different marks to distinguish different types of community living circles in the map obtained in Step 1 to form a spatial distribution map; Step 306: Execute Step 2 to Step 305 to regularly observe the changes in the self-service index of each community living circle.

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