Urban and rural planning management method and system based on artificial intelligence

Abstract

The invention discloses an artificial intelligence-based urban and rural planning management method and system, and relates to the technical field of urban and rural planning management, and the method comprises the steps: dividing an urban and rural planning region based on land types, and carrying out the horizontal compatibility basic weight endowing of each construction land parcel and an adjacent construction land parcel, determining a first conflict constraint result of the construction land parcel; monitoring the average pedestrian flow and the average residence time of the construction land plot, calculating the final dynamic weight of the plot adjacent to the construction land plot, and determining a second conflict constraint result of the construction land plot; and obtaining a first conflict constraint result A, a second conflict constraint result B and a distance D from each construction land parcel to the downtown center, analyzing a comprehensive conflict result H of each construction land and surrounding construction lands, performing descending sorting according to H values, and outputting and forming an urban and rural planning region conflict thermodynamic diagram. A quantitative basis is provided for planning, core contradictions are solved preferentially, and high-conflict areas are helped to be quickly responded.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban and rural planning management, in particular to an urban and rural planning management method and system based on artificial intelligence. BACKGROUND

[0002] With the acceleration of global urbanization, urban and rural planning management is facing unprecedented complex challenges. Traditional planning methods highly rely on manual decision-making, which has the pain points of data lag, excessive model simplification, and inefficient multi-party interest coordination. In this context, the breakthrough of artificial intelligence technology provides a new technical paradigm for urban and rural planning, promoting the evolution of planning management towards data-driven, intelligent prediction, and dynamic optimization. The introduction of artificial intelligence reshapes the planning technology system. Machine learning algorithms can process multi-source heterogeneous data such as satellite remote sensing and Internet of Things sensors, achieving dynamic monitoring. For example, through convolutional neural network (CNN) analysis of satellite images, urban expansion boundaries can be identified in real time, with a precision of sub-millimeter, improving the efficiency compared with traditional manual mapping. Deep learning models (such as LSTM) integrate traffic flow and population migration data to predict urban growth hotspots in the next ten years, providing forward-looking basis for planning.

[0003] In the Chinese invention application with the application publication number CN119476870A, an urban and rural planning management method and system based on artificial intelligence is disclosed, including data collection, data preprocessing, urban expansion prediction, air quality prediction, and urban and rural planning management. The present application combines urban expansion prediction and air quality prediction to deepen the planning management dimension; uses convolutional long short-term parallel bidirectional gated recurrent network for urban expansion prediction to better extract spatial and temporal features of urban expansion, and improves the analysis ability of complex data through bidirectional gated recurrent, improving the overall accuracy of the method; uses time series convolutional bidirectional long short-term memory network combined with improved parameter optimization for air quality prediction, which can extract short-term air change features and analyze long-term trend air quality changes, and uses improved whale optimization search algorithm to optimize model performance, improving the efficiency and accuracy of urban and rural planning management.

[0004] In the above invention application, urban and rural planning management is combined with urban expansion prediction and air quality prediction, but urban and rural planning management cannot be optimized simultaneously in a large area, and the existing urban and rural planning management lacks scientificity, with inaccurate data, outdated methods, or insufficient prediction leading to disconnection between planning and actual demand. It is difficult to adapt to complex and changing urban and rural development needs.

[0005] Therefore, the present application provides an urban and rural planning management method and system based on artificial intelligence. SUMMARY

[0006] (I) Technical problems solved In view of the deficiencies of the prior art, the present application provides a kind of urban and rural planning management method and system based on artificial intelligence, the present application is divided into urban and rural planning area based on land use type, the horizontal compatibility basis weight of each construction land plot and its adjacent construction land plot is given, the horizontal compatibility basis weight is combined with adjustment item, the static weight of the adjacent plot of final construction land plot is calculated, the first conflict constraint result of construction land plot is determined, the average flow and average stay length of construction land plot are monitored, the dynamic weight of the adjacent plot of final construction land plot is calculated, the second conflict constraint result of construction land plot is determined, the first conflict constraint result A, the second conflict constraint result B and the distance D of city center of each construction land plot are obtained, the comprehensive conflict result H of each construction land and surrounding construction land is analyzed, is arranged in descending order according to H value, output forms urban and rural planning area conflict heat map, provides quantitative basis for planning, preferentially solves core contradiction, helps to respond to high conflict area quickly, thereby solve the technical problems recorded in the background art.

[0007] (II) Technical solutions To achieve the above object, the present application is realized by the following technical solutions: an urban and rural planning management method based on artificial intelligence, comprising the following steps: The urban and rural planning area is divided based on land use type, the horizontal compatibility basis weight of each construction land plot and its adjacent construction land plot is given, the horizontal compatibility basis weight is combined with adjustment item, the static weight of the adjacent plot of final construction land plot is calculated, and the first conflict constraint result of construction land plot is determined. The average flow and average stay length of construction land plot are monitored, the dynamic weight of the adjacent plot of final construction land plot is calculated, and the second conflict constraint result of construction land plot is determined. The first conflict constraint result A, the second conflict constraint result B and the distance D of city center of each construction land plot are obtained, the comprehensive conflict result H of each construction land and surrounding construction land is analyzed, is arranged in descending order according to H value, output forms urban and rural planning area conflict heat map.

[0008] Further, the urban and rural planning area is divided based on land use type, divided into a plot, and the land use type is marked for plot, land use type includes construction land and non-construction land, construction land includes residential land, commercial land, industrial land, municipal land and green land, and non-construction land includes forestry land, water area and ecological preservation land.

[0009] Furthermore, a land use compatibility scoring table is constructed, and based on the land use compatibility scoring table, a horizontal compatibility basic weight is assigned to each construction land parcel and its adjacent construction land parcel. The corresponding horizontal compatibility basic weight is obtained after compatibility interpolation calculation of mixed land using fuzzy mathematics theory. The horizontal compatibility basic weight of the construction land parcel and all adjacent plots is output to construct a horizontal compatibility matrix.

[0010] Furthermore, a rule engine is deployed to load the horizontal compatibility matrix, calculate the weight adjustment item based on the difference between the floor area ratio of the construction land parcel and the distance d between the adjacent construction land, combine the horizontal compatibility basic weight with the adjustment item, and calculate the final static weight of the adjacent plots of the construction land parcel.

[0011] Static weight of adjacent plots = horizontal compatibility basic weight * ( )*(1 -0.1 ×d / ).

[0012] Among them, 0 means complete compatibility, 1 means complete conflict, and higher values ​​indicate worse compatibility. Floor area ratio = total building area / total land area. The distance d between adjacent construction lands refers to the shortest distance between adjacent construction lands. Indicates the standard distance between adjacent construction land.

[0013] Furthermore, the average static weight of the adjacent plots of the construction land plot is obtained and recorded as the first conflict constraint result of the construction land plot; the average static weight of the adjacent plots of the construction land plot is the average of the static weights of the same construction land plot and all its adjacent plots.

[0014] Furthermore, the base station signaling data is used to monitor the flow of people in the construction land area in real time, analyze the length of time users stay in the area, and collate the average flow of people and average length of time they stay in the construction land area; The mobile phone continuously interacts with nearby base stations to maintain a connection, generating signaling data. The base station records the mobile phone's unique ID, timestamp, base station location number, and event type. The timestamp span of continuous signaling data with the same mobile phone unique ID is the user's residence time. The number of mobile phone unique IDs in the same time area is the real-time pedestrian flow in the area. The average pedestrian flow is the average of all real-time pedestrian flow data in the current area, and the average residence time is the average of the residence time data of all users in the current area.

[0015] Furthermore, the average passenger flow R1 and average residence time Z1 of the construction land plot and the average passenger flow R2 and average residence time Z2 of its adjacent construction land plots are obtained, and combined with the average construction time J1 and J2 of the buildings in the construction land plot, the final dynamic weights of the adjacent plots of the construction land plot are calculated.

[0016] Dynamic weight of adjacent plots =

[0017] Wherein, J1 is the average construction time of buildings in the construction land plot, and J2 is the average construction time of buildings in the adjacent construction land plot.

[0018] Further, the average adjacent plot dynamic weight of the construction land plot is obtained, denoted as the second conflict constraint result of the construction land plot.

[0019] Further,

[0020] Wherein, the distance D of the construction land plot to the city center is the nearest distance from the construction land plot to the city center.

[0021] Further, the range standardization processing is performed on the comprehensive conflict value H of each construction land plot, the construction land plots are arranged in descending order of H value, the TOP 20% construction land plots are marked as red to-be-planned areas, the TOP 20%-40% construction land plots are marked as orange to-be-planned areas, the TOP 40%-60% construction land plots are marked as yellow to-be-planned areas, the TOP 60%-80% construction land plots are marked as cyan to-be-planned areas, and the TOP 80%-100% construction land plots are marked as blue to-be-planned areas.

[0022] An urban and rural planning management system based on artificial intelligence, comprising: A static conflict analysis module, which divides the urban and rural planning area based on land use types, gives horizontal compatibility basic weight to each construction land plot and its adjacent construction land plots, combines the horizontal compatibility basic weight with adjustment items, calculates the final static weight of the adjacent plots of the construction land plot, and determines the first conflict constraint result of the construction land plot. A dynamic conflict analysis module, which monitors the average traffic flow and average residence time of the construction land plot, calculates the final dynamic weight of the adjacent plots of the construction land plot, and determines the second conflict constraint result of the construction land plot. A conflict heat output module, which obtains the first conflict constraint result A, the second conflict constraint result B and the distance D to the city center of each construction land plot, analyzes the comprehensive conflict result H of each construction land plot and the surrounding construction land plots, arranges the construction land plots in descending order of H value, and outputs the conflict heat map of the urban and rural planning area.

[0023] (Three) beneficial effects The application provides an urban and rural planning management method and system based on artificial intelligence, which has the following beneficial effects: 1. Based on the division of urban and rural planning areas by land use type, the horizontal compatibility basis weight is given to each construction land plot and its adjacent construction land plot, the horizontal compatibility basis weight is combined with the adjustment item, the final static weight of the adjacent plot of the construction land plot is calculated, the first conflict constraint result of the construction land plot is determined, which reflects the compatibility between different land use types, helps to identify potential functional conflicts, more comprehensively reflects the actual conflicts between plots, and more accurately depicts the conflict intensity between plots.

[0024] 2. The average flow and average residence time of the construction land plot are monitored, the final dynamic weight of the adjacent plot of the construction land plot is calculated, the second conflict constraint result of the construction land plot is determined, the low-efficiency plot can be accurately positioned, a scientific basis is provided for urban planning and management, and the use status of the plot can be intuitively understood, so that more accurate and effective planning and management strategies are developed.

[0025] 3. The first conflict constraint result A, the second conflict constraint result B and the distance D from the city center of each construction land plot are obtained, the comprehensive conflict result H of each construction land and the surrounding construction land is analyzed, the H value is arranged in descending order, and a conflict heat map of the urban and rural planning area is output, which provides a quantitative basis for planning, preferentially solves core contradictions, and helps to quickly respond to high-conflict areas. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 A flowchart of a kind of urban and rural planning management method based on artificial intelligence according to the present application; Figure 2 Land property compatibility score according to the present application; Figure 3 A structure diagram of a kind of urban and rural planning management system based on artificial intelligence according to the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0028] Please refer to Figures 1-2 The present application provides a kind of urban and rural planning management method based on artificial intelligence, comprising the following steps: Step one, based on the land use type, the urban and rural planning area is divided, and the horizontal compatibility basis weight is given to each construction land block and its adjacent construction land block. The horizontal compatibility basis weight is combined with the adjustment item to calculate the final static weight of the adjacent land block of the construction land block, and the first conflict constraint result of the construction land block is determined.

[0029] The step one includes the following contents: Step 101, based on the land use type, the urban and rural planning area is divided into blocks, and the land use type is marked for the block, the land use type includes construction land and non-construction land, the construction land includes residential land, commercial land, industrial land, municipal land and green land. Non-construction land includes farmland, water area and ecological preservation land.

[0030] Step 102, construct a land property compatibility score table, and give a horizontal compatibility basis weight to each construction land block and its adjacent construction land block based on the land property compatibility score table. The corresponding horizontal compatibility basis weight is obtained after the compatibility interpolation calculation of mixed land by using fuzzy mathematics theory. The horizontal compatibility basis weight of the construction land block and all adjacent land blocks is output, and the horizontal compatibility matrix is constructed.

[0031] If the commercial-residential ratio of mixed land is 6:4, the corresponding horizontal basis weight is 0.8*0.6+1*0.4=0.88.

[0032] Step 103, deploy a rule engine to load the horizontal compatibility matrix, calculate the weight adjustment item according to the construction land block volume rate and the adjacent construction land distance d difference, combine the horizontal compatibility basis weight with the adjustment item, and calculate the final static weight of the adjacent land block of the construction land block.

[0033] The static weight of the adjacent land block = the horizontal compatibility basis weight *( )*(1-0.1×d / ).

[0034] Wherein, 0 represents complete compatibility, 1 represents complete conflict, the higher the value, the worse the compatibility, the volume rate = total building area / total land area, the adjacent construction land distance d refers to the nearest distance of the adjacent construction land, The standard distance of the adjacent construction land.

[0035] If the block area is 10000㎡, a 5-story building with each floor of 2000㎡, the total building area is 10000㎡, and the volume rate is 10000 / 10000=1.

[0036] Step 104, obtaining the average adjacent plot static weight of the construction land plot, denoted as the first conflict constraint result of the construction land plot. The average adjacent plot static weight of the construction land plot is the average of the same construction land plot and all adjacent plot static weights.

[0037] In use, in combination with the contents in steps 101 to 104: Based on the division of the urban and rural planning area according to the land use type, the horizontal compatibility basic weight is given to each construction land plot and its adjacent construction land plot, the horizontal compatibility basic weight is combined with the adjustment term, the final static weight of the adjacent plot of the construction land plot is calculated, the first conflict constraint result of the construction land plot is determined, which reflects the compatibility between different land use types, helps to identify potential functional conflicts, more comprehensively reflects the actual conflicts between plots, and more accurately depicts the conflict strength between plots.

[0038] Step two, monitoring the average traffic flow and average residence time of the construction land plot, calculating the final dynamic weight of the adjacent plot of the construction land plot, and determining the second conflict constraint result of the construction land plot.

[0039] The step two includes the following contents: Step 201, monitoring the construction land plot area traffic flow in real time through base station signaling data, and analyzing user residence time to arrange the average traffic flow and average residence time of the construction land plot.

[0040] The mobile phone continuously interacts with the nearby base station to maintain the connection, generates signaling data such as location update and handover request, and the base station records the mobile phone unique identifier (IMSI / TMSI), timestamp, base station location number and event type such as call and Internet access. The timestamp span of the same mobile phone unique identifier continuous signaling data is the user residence time. The number of mobile phone unique identifiers in the same time area is the real-time traffic flow in the area. The average traffic flow is the average of all real-time traffic flow data in the current area, and the average residence time is the average of all user residence time data in the current area.

[0041] Step 202, obtaining the average traffic flow R1 and average residence time Z1 of the construction land plot and the average traffic flow R2 and average residence time Z2 of the adjacent construction land plot, combining the average construction time J1 and J2 of the buildings in the construction land plot, and calculating the final dynamic weight of the adjacent plot of the construction land plot.

[0042] The dynamic weight of the adjacent plot =

[0043] Wherein, J1 is the average construction time of the buildings in the construction land plot, and J2 is the average construction time of the buildings in the adjacent construction land plot.

[0044] Step 203, obtaining the average adjacent plot dynamic weight of the construction land plot, denoted as the second conflict constraint result of the construction land plot. The average adjacent plot dynamic weight of the construction land plot is the average of the dynamic weights of all adjacent plots of the same construction land plot.

[0045] In use, in combination with the contents in steps 201 to 203: Monitoring the average traffic flow and average residence time of the construction land plot, calculating the final dynamic weight of the adjacent plot of the construction land plot, determining the second conflict constraint result of the construction land plot, accurately positioning the low-efficiency plot, providing a scientific basis for urban planning and management, and intuitively understanding the use of the plot, so as to formulate more accurate and effective planning and management strategies.

[0046] Step three, obtaining the first conflict constraint result A, the second conflict constraint result B and the distance D from the city center of each construction land plot, analyzing the comprehensive conflict result H of each construction land and the surrounding construction land, arranging in descending order according to the H value, and outputting to form a conflict heat map of the urban and rural planning area.

[0047] The step three includes the following contents: Step 301, obtaining the first conflict constraint result A, the second conflict constraint result B and the distance D from the city center of each construction land plot, analyzing the comprehensive conflict result H of each construction land and the surrounding construction land:

[0048] Among them, the distance D from the construction land plot to the city center is the nearest distance from the construction land plot to the city center.

[0049] Step 302, performing range standardization processing on the comprehensive conflict value H of each construction land plot, arranging in descending order according to the H value, marking the TOP 20% construction land plot as a red planning area, the TOP 20%-40% construction land plot as an orange planning area, the TOP 40%-60% construction land plot as a yellow planning area, the TOP 60%-80% construction land plot as a green planning area, and the TOP 80%-100% construction land plot as a blue planning area, and outputting to form a conflict heat map of the urban and rural planning area.

[0050] The range standardization processing is specifically:

[0051] In use, in combination with the contents in steps 301 and 302: The first conflict constraint result A, the second conflict constraint result B and the distance D from the city center of each construction land plot are acquired, the comprehensive conflict result H of each construction land and surrounding construction land is analyzed, the H values are arranged in descending order, and a conflict heat map of the urban and rural planning area is outputted.

[0052] Please refer to Figure 3 The application provides an urban and rural planning management system based on artificial intelligence, which comprises: The static conflict analysis module divides the urban and rural planning area based on land use types, gives horizontal compatibility basic weights to each construction land plot and its adjacent construction land plots, combines the horizontal compatibility basic weights with adjustment items, calculates the final static weights of the adjacent plots of the construction land plot, and determines the first conflict constraint result of the construction land plot.

[0053] The dynamic conflict analysis module monitors the average traffic flow and average residence duration of the construction land plot, calculates the final dynamic weights of the adjacent plots of the construction land plot, and determines the second conflict constraint result of the construction land plot.

[0054] The conflict heat output module acquires the first conflict constraint result A, the second conflict constraint result B and the distance D from the city center of each construction land plot, analyzes the comprehensive conflict result H of each construction land and surrounding construction land, arranges the H values in descending order, and outputs a conflict heat map of the urban and rural planning area.

[0055] The above embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part. Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solutions.

[0056] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiment according to actual needs.

[0057] The above is only a specific embodiment of the application, but the protection scope of the application is not limited thereto, and any skilled person in the art can easily think of changes or replacements within the technical range disclosed in the application, which should be covered within the protection scope of the application.

Claims

1. An urban and rural planning management method based on artificial intelligence, characterized by: The steps include: Based on land use type, the urban and rural planning areas are divided, and a horizontal compatibility basic weight is assigned to each construction land parcel and its adjacent construction land parcels. The horizontal compatibility basic weight is combined with the adjustment item to calculate the final static weight of the adjacent plots of the construction land parcel, and the first conflict constraint result of the construction land parcel is determined; Monitor the average flow of people and the average length of stay on the construction land parcel, calculate the final dynamic weights of the adjacent parcels of the construction land parcel, and determine the second conflict constraint result of the construction land parcel; Obtain the first conflict constraint result A, the second conflict constraint result B, and the distance D from the city center for each construction land parcel, analyze the comprehensive conflict result H between each construction land and the surrounding construction land, arrange them in descending order by H value, and output a heat map of urban and rural planning regional conflicts.

2. The method for urban and rural planning management based on artificial intelligence according to claim 1, characterized in that: The urban and rural planning areas are divided into plots based on land use types, and the plots are marked with land use types. Land use types include construction land and non-construction land. Construction land includes residential land, commercial land, industrial land, municipal land and green land. Non-construction land includes agricultural and forestry land, water areas and ecological reserves.

3. The method for urban and rural planning management based on artificial intelligence according to claim 1, characterized in that: Construct a land use compatibility scoring table, and assign horizontal compatibility basic weights to each construction land parcel and its adjacent construction land parcels based on the land use compatibility scoring table. Use fuzzy mathematics theory to perform compatibility interpolation calculations on mixed land to obtain the corresponding horizontal compatibility basic weights. Output the horizontal compatibility basic weights of the construction land parcel and all adjacent plots to construct a horizontal compatibility matrix.

4. The method for urban and rural planning management based on artificial intelligence according to claim 1, characterized in that: Deploy a rule engine to load the horizontal compatibility matrix, calculate the weight adjustment item based on the floor area ratio of the construction land parcel and the difference in the distance d between the adjacent construction land, combine the horizontal compatibility basic weight with the adjustment item, and calculate the final static weight of the adjacent plots of the construction land parcel.

5. The method for urban and rural planning management based on artificial intelligence according to claim 1, characterized in that: The average static weight of the adjacent plots of the construction land parcel is obtained and recorded as the first conflict constraint result of the construction land parcel; the average static weight of the adjacent plots of the construction land parcel is the average of the static weights of the same construction land parcel and all its adjacent plots.

6. The urban and rural planning management method based on artificial intelligence according to claim 1, characterized in that: Through base station signaling data, the flow of people in the construction land area is monitored in real time, and the length of time users stay in the area is analyzed to sort out the average flow of people and the average length of time they stay in the construction land area; Mobile phones continuously interact with nearby base stations to maintain connections, generating signaling data. The base stations record the mobile phone's unique ID, timestamp, base station location number, and event type. The timestamp span of continuous signaling data with the same mobile phone's unique ID is the user's residence time. The number of mobile phone unique IDs in the same time area is the real-time pedestrian flow in the area. The average pedestrian flow is the average of all real-time pedestrian flow data in the current area, and the average residence time is the average of the residence time data of all users in the current area.

7. The method for urban and rural planning management based on artificial intelligence according to claim 1, characterized in that: The average pedestrian flow R1 and average residence time Z1 of the construction land plot and the average pedestrian flow R2 and average residence time Z2 of its adjacent construction land plot are obtained, and combined with the average construction time J1 and J2 of the buildings in the construction land plot, the dynamic weights of the final adjacent plots of the construction land plot are calculated.

8. The urban and rural planning management method based on artificial intelligence according to claim 1, characterized in that: The average dynamic weight of the adjacent plots of the construction land parcel is obtained and recorded as the second conflict constraint result of the construction land parcel; the average dynamic weight of the adjacent plots of the construction land parcel is the average of the dynamic weights of the same construction land parcel and all its adjacent plots.

9. The method for urban and rural planning management based on artificial intelligence according to claim 1, characterized in that: The TOP 20% construction land plots are marked as red to-be-planned areas, the TOP 20%-40% construction land plots are marked as orange to-be-planned areas, the TOP 40%-60% construction land plots are marked as yellow to-be-planned areas, the TOP 60%-80% construction land plots are marked as cyan to-be-planned areas, and the TOP 80%-100% construction land plots are marked as blue to-be-planned areas.

10. An artificial intelligence-based urban and rural planning management system, used to implement the method according to any one of claims 1 to 9, characterized in that: include: The static conflict analysis module divides urban and rural planning areas based on land use types, assigns horizontal compatibility basic weights to each construction land parcel and its adjacent construction land parcels, combines the horizontal compatibility basic weights with the adjustment items, calculates the final static weights of the adjacent plots of construction land parcels, and determines the first conflict constraint result of the construction land parcels; The dynamic conflict analysis module monitors the average flow of people and the average length of stay on the construction land parcel, calculates the dynamic weights of the final adjacent parcels of the construction land parcel, and determines the second conflict constraint result of the construction land parcel; The conflict thermal output module obtains the first conflict constraint result A, the second conflict constraint result B and the distance D from the city center of each construction land parcel, analyzes the comprehensive conflict result H between each construction land and the surrounding construction land, arranges them in descending order by H value, and outputs a conflict thermal map of the urban and rural planning area.

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