Interactive model system for urban and rural land space planning based on double organization and development monitoring

Abstract

The application discloses an interactive model system for urban land space planning based on double organization and development monitoring, and has the characteristics that the system comprises a public participation platform, a construction land screening module, an urban space planning growth module and an urban development monitoring module. The urban growth model based on geographic information technology is introduced into urban planning and design, a growth model framework with better applicability is constructed, and the research and construction cost of the model needs to be controlled for the urban model of small towns. The model structure based on logistic regression and interactive design is in line with the double organization characteristics of urban growth and construction. Through urban development monitoring, changes that may be encountered in the long-term development of the city can be predicted, and corresponding passive feedback mechanisms can be developed to make corresponding adjustments to the land space planning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban planning, in particular to a town land space planning interactive model system based on double organization and development monitoring. BACKGROUND

[0002] In the past 20 years since 2000, the status of town in the overall strategy of urbanization development has become increasingly prominent. As an intermediate link in the urbanization chain, a part of the industry in large cities has begun to transfer to towns, and the migration destination of rural population has also shifted from large cities to towns. Towns have gradually shown important strategic role in the construction of town cluster system. In this process, the research on town growth in academia has gradually increased. Most scholars use different research methods to quantitatively analyze the influencing factors of town development, and form some valuable results. Hu Yingen et al. discussed the influence of six factors including nature, economy, system, science and technology information, humanity and environment on the extension of small town land. Dai Junliang et al. analyzed the internal driving force of town space expansion, and believed that the resource and environment condition was the prerequisite for town density, and the traffic mode, land development mode, residential development mode and road traffic tax system were the key factors affecting the development intensity of town. Cao Yinguo et al. analyzed the high correlation between the urbanization rate, the second industry employees, the fixed asset investment and the construction land. Wang Haijun researched the expansion process of the built-up area of Mianhu Town in Guangdong Province in recent years, analyzed the traffic network from the perspective of traffic condition improvement, and proposed that the traffic condition improvement was one of the driving forces of town expansion. Feng Yanyun et al. analyzed the suitability of town construction land space expansion in typical karst mountainous area, and showed that the topography had significant influence on urban growth.

[0003] However, the current research on town growth still has three limitations. Firstly, most quantitative research ignores the role of different policy environment on town growth, to some extent, excludes the objective influence of policy changes and regional cooperation on town development, and the research conclusion lacks adaptability. Secondly, the current research is still based on the top-down planning system, is limited to the rule simulation of complex town phenomenon, lacks the feedback and expression of public will, and there is lack of good interaction between researchers, planners and service objects. Thirdly, such research generally needs a large amount of data to support, and the research and design cost is high, which is difficult to be directly applied to planning practice. SUMMARY

[0004] The present application provides a town land space planning interactive model system based on double organization and development monitoring, introduces the town growth model based on geographic information technology into town planning and design, adopts the built environment quantitative research method in the new data environment, constructs the growth model framework with better applicability, and combines the double organization of interactive design and the town development monitoring, so as to provide more scientific technical support for the generation process and optimization strategy.

[0005] The technical scheme of the present application is:

[0006] An interactive model system for urban land space planning based on double organization and development monitoring, comprising a public participation platform, a construction land screening module, an urban space planning growth module, and an urban development monitoring module.

[0007] The public participation platform is a tool platform for various use subjects in the city to participate in urban decision-making, and collects and integrates the interests of multiple subjects. The formulation subject, management subject, and use subject of land space planning realize internal interaction through the public participation platform and jointly participate in the whole process of formulating and managing land space planning.

[0008] The construction land screening module is based on policy elements, environmental elements, and engineering elements to identify and exclude land that is not suitable for construction within the urban area. Policy elements refer to elements that affect construction, such as historical and cultural protection areas. Environmental elements refer to elements that affect construction, such as nature reserves. Engineering elements refer to elements that affect construction, such as water bodies and mountains.

[0009] The space planning growth module includes an urban land space planning statistical growth prediction submodule and an urban land space planning adaptive growth prediction submodule.

[0010] The urban development monitoring module realizes development monitoring element setting, expansion coefficient correction, and control weight correction, predicts possible changes in the long-term development process of the city, and formulates corresponding passive feedback mechanisms to make corresponding adjustments to land space planning.

[0011] Further, the system also influences the calculation of urban land space planning statistical growth prediction and urban land space planning adaptive growth prediction through the control system.

[0012] The double organization is based on top-down self-organization construction behavior based on regional macro space and industrial policy, and bottom-up self-organization construction behavior based on the location resources of the micro block itself.

[0013] The urban land space planning statistical growth prediction submodule is based on typical sample urban data, and constructs a typical growth model of urban land space planning through logistic regression analysis, reflecting the quantitative analysis of objective laws in the planning process.

[0014] The urban land space planning adaptive growth prediction submodule is based on target urban types, and constructs a control system based on public participation through an urban land use adaptability model and an analytic hierarchy process, reflecting the reasonable expression of subjective guidance.

[0015] As a preferred scheme, the urban land space planning typical growth model comprises an industrial urban model, a finance / service industry urban model and a residential urban model.

[0016] As a preferred scheme, the control system comprises an industrial sub-control system, a human settlement sub-control system and an environment sub-control system under the guidance of the overall urban development target; each sub-control system sorts the preset control system through analytic hierarchy process to obtain the proportion of the sub-control element in the final land use decision.

[0017] The stakeholders under the urban planning system in China comprise superior management agencies, urban management agencies and citizen groups.

[0018] The superior management agencies comprise urban regional comprehensive management departments, superior space planning departments and superior special planning departments; the participation mode of the superior management agencies is to translate the superior planning or guidance documents;

[0019] The urban management agencies comprise local comprehensive management departments, local space management departments and local industry departments; the participation mode of the urban management agencies is to participate directly through the public participation platform;

[0020] The participation mode of the citizen groups comprises conscious online participation and unconscious online participation, and the conscious online participation and the unconscious online participation are collected and integrated through the online public participation platform.

[0021] The public participation platform can collect the conscious online public participation and provide instant feedback, and can collect the conscious network public participation and the unconscious public participation data and provide the data to the land space planning formulating subject.

[0022] The development monitoring element is set in the model construction stage, according to the general law of urban development, based on the possible change types, the monitoring object parameters of each potential external change are preset, which are the trigger premise and basis of the expansion coefficient correction and the control weight correction; the expansion coefficient correction is applied to the statistical growth prediction module, the expansion coefficient is adjusted based on the change of the external environment to control the rigid bottom line of the land space planning;

[0023] The control weight correction is applied to the adaptive growth prediction module, the control system weight is adjusted based on the change of the external environment to control the elastic interval of the land space planning.

[0024] The present application has the following beneficial effects:

[0025] 1. The urban growth model based on geographic information technology is introduced into the urban planning design, a growth model framework with better applicability is constructed, and the research and construction cost of the need control model of the small town urban model is reduced.

[0026] 2. The model structure based on logistic regression and interaction design conforms to the dual organization characteristics of urban growth and construction.

[0027] 3. Through urban development monitoring, changes that may be encountered in the long-term development of the city can be predicted, and corresponding passive feedback mechanisms can be developed to make corresponding adjustments to the land space planning. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0029] Figure 1 The system basic framework is shown in Figure 1.

[0030] Figure 2 The urban growth interaction model is shown in Figure 2.

[0031] Figure 3 The expansion coefficient calculation process is shown in Figure 3.

[0032] Figure 4 The online interaction platform design is shown in Figure 4. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0034] A dual organization and development monitoring based urban land space planning interactive model system, comprising a public participation platform, a construction land screening module, an urban space planning growth module and an urban development monitoring module; through the control system, the calculation of urban land space planning statistical growth prediction and urban land space planning adaptability growth prediction is influenced, see Figure 1 .

[0035] Dual organization is a top-down organization construction behavior based on regional macro space and industrial policy and a bottom-up self-organization construction behavior based on the location resources of the micro plot itself. It can not only describe the objective external environment and subjective construction desire of small towns on a macro scale, but also simulate the spontaneous construction behavior of township residents on a micro scale.

[0036] To adapt to the dual organization construction mode, the model system includes a town spatial planning growth module composed of small town statistical growth prediction and small town adaptive growth prediction, which respectively reflects the quantitative analysis of objective laws and the reasonable expression of subjective guidance in the planning process. Figure 2 The model system calculation process is also divided into two parts, corresponding to the planning research stage and the planning formulation stage in the traditional planning process.

[0037] The development monitoring module predicts possible changes in the long-term development process of the town and develops corresponding passive feedback mechanisms to make appropriate adjustments to the national spatial planning, including the implementation of development monitoring element setting, expansion coefficient correction, and control weight correction. Among them, the development monitoring element setting works in the model construction stage, according to the general law of town development, based on the type of possible changes, the monitoring object parameters preset for each potential external change, which is the premise and basis for expansion coefficient correction and control weight correction; the expansion coefficient correction works in the statistical growth prediction module, based on the changes in the external environment to adjust the expansion coefficient and thus control the rigid bottom line of national spatial planning; the control weight correction works in the adaptive growth prediction module, based on the changes in the external environment to adjust the control system weight and thus control the elastic interval of national spatial planning.

[0038] The overall construction method of the model is highly consistent with the traditional planning customization process, corresponding to the planning research stage, the planning customization stage, and the planning implementation stage.

[0039] I. Planning Research Stage

[0040] Collect various data and convert them into CAD files for input into the model system. This includes various types of spatial planning, current land use, facility layout, etc., as rigid control elements for model calculation. Build a public participation platform to collect residents' opinions, including online and offline platforms. The online platform includes web pages, QR codes, and independent apps for different access modes to improve information collection efficiency; the offline platform includes questionnaires and forums, based on the collected data as elastic control elements.

[0041] Perform construction land screening and output non-construction land. This includes identifying the rigid bottom line of town national spatial planning, such as ecological protection red line, permanent basic farmland, and urban development boundary. Build a multi-plan integration platform to integrate economic development planning, national spatial planning, and other regulations. Based on the above steps, finally, through the analytic hierarchy process, all stakeholders' opinions are synthesized to sort the preset control system, obtaining the weight of each sub-control system in the final land use decision. The default sub-control system for special construction is as follows:

[0042] Control Element 1: Select land suitable for residential land allocation

[0043] Sub-control element 1.1: Selecting plots suitable for construction development

[0044] Sub-control element 1.1.1: Selecting plots with suitable slope;

[0045] Sub-control element 1.1.2: Selecting plots already developed as a community;

[0046] Sub-control element 1.1.3: Selecting plots not affected by noise;

[0047] Sub-control element 1.1.4: Selecting plots not affected by exhaust fumes;

[0048] Sub-control element 1.1.5: Selecting plots not affected by water pollution.

[0049] Sub-control element 1.2: Selecting plots suitable for residential land use according to residential environment

[0050] Sub-control element 1.2.1: Selecting plots close to educational services;

[0051] Sub-control element 1.2.2: Selecting plots close to medical services;

[0052] Sub-control element 1.2.3: Selecting plots close to water bodies or green spaces;

[0053] Sub-control element 1.2.4: Selecting plots close to cultural facilities;

[0054] Sub-control element 1.2.5: Selecting plots close to retail.

[0055] Sub-control element 1.3: Selecting plots suitable for residential land use from the perspective of convenient travel

[0056] Sub-control element 1.3.1: Selecting plots appropriately close to roads;

[0057] Sub-control element 1.3.2: Selecting plots appropriately close to bus stops;

[0058] Sub-control element 1.3.3: Selecting plots with bicycle lanes in the surrounding area;

[0059] Sub-control element 1.3.4: Selecting plots close to already built communities.

[0060] Control element 2: Selecting plots suitable for industrial land use

[0061] Sub-control element 2.1: Selecting plots based on existing regulations

[0062] Sub-control element 2.1.1: Selecting plots that do not affect already built communities;

[0063] Sub-control element 2.1.2: Select plots that do not affect ecologically highly sensitive areas;

[0064] Sub-control element 2.1.3: Select plots that have been developed for industry;

[0065] Sub-control element 2.1.4: Select plots that are not contaminated by noise;

[0066] Sub-control element 2.1.5: Select plots that are not contaminated by exhaust fumes;

[0067] Sub-control element 2.1.6: Select plots that have been developed for service / financial use.

[0068] Sub-control element 2.2: Select plots according to construction and transport costs

[0069] Sub-control element 2.1.1: Select plots with suitable slopes;

[0070] Sub-control element 2.2.2: Select plots close to roads;

[0071] Sub-control element 2.2.3: Select plots close to freight stations;

[0072] Sub-control element 2.2.4: Select plots close to warehousing facilities;

[0073] Sub-control element 2.2.5: Select plots close to central business districts;

[0074] Sub-control element 2.2.6: Select plots close to integrated management departments;

[0075] Sub-control element 2.2.7: Select plots close to airports and high-speed rail stations;

[0076] Sub-control element 2.2.8: Select plots close to relevant functional departments.

[0077] Sub-control element 2.3: Select plots based on commuting cost control considerations

[0078] Sub-control element 2.3.1: Select plots close to bus stops;

[0079] Sub-control element 2.3.2: Select plots close to roads;

[0080] Sub-control element 2.3.3: Select plots with surrounding bicycle lanes;

[0081] Sub-control element 2.3.4: Select plots close to existing communities.

[0082] Control element 3: Select plots suitable for allocation as environmental land

[0083] Sub-control element 3.1: Selecting plots based on landscape quality considerations

[0084] Sub-control element 3.1.1: Selecting plots not affected by noise

[0085] Sub-control element 3.1.2: Selecting plots not affected by exhaust gas

[0086] Sub-control element 3.1.3: Selecting plots not affected by water pollution

[0087] Sub-control element 3.1.4: Selecting plots with appropriate slope

[0088] Sub-control element 3.1.5: Selecting plots already developed as environmental land

[0089] Sub-control element 3.2: Selecting plots based on accessibility

[0090] Sub-control element 3.2.1: Selecting plots close to existing communities

[0091] Sub-control element 3.2.2: Selecting plots close to bus stops

[0092] Sub-control element 3.2.3: Selecting plots with surrounding bicycle lanes

[0093] Sub-control element 3.2.4: Selecting plots close to roads

[0094] Sub-control element 3.3: Selecting plots based on regional needs and local characteristics

[0095] Sub-control element 3.3.1: Selecting plots close to water bodies or green spaces

[0096] Sub-control element 3.3.2: Selecting plots selected as green spaces in higher-level spatial planning

[0097] Sub-control element 3.3.3: Selecting plots close to characteristic cultural resources

[0098] Sub-control element 3.3.4: Selecting other plots with corresponding development potential

[0099] Data is collected through an online interactive platform, as shown in the design diagram Figure 4 The functional modules include town introduction, town forum, planning publicity, public bidding, development suggestions, implementation supervision, and offline activities. The main body of land space planning can allocate weights to each sub-control element in proportion according to the collected data using the analytic hierarchy process, and output the basic control system.

[0100] After obtaining the basic control system, according to the overall control target of urban development, combined with the positioning of urban development, the possible external environmental changes are predicted and divided into four categories: industrial pattern change, population structure change, ecological environment change and policy management change.

[0101] The industrial pattern change is specifically manifested as "service industry response" when the service industry employment rate of the city is higher than 0.6; "industrialization response" when the industrial employment rate of the city is higher than 0.6. For every increase of 0.1 in the employment multiplier, the weight of "service industry response" or "industrialization response" will increase by 11.11%.

[0102] The population structure change is specifically manifested as "aging society response" when the proportion of aging population in the total population exceeds 7%; "young society response" when the proportion of children in the total population exceeds 7%. For every increase of 1% in the proportion of the elderly or children in the total population, the weight of "aging society response" or "young society response" will increase by 5%.

[0103] The ecological environment change is specifically manifested as "air pollution response" when the number of days with good air quality is less than 80%. For every increase of 1% in the number of pollution days, the weight of "air pollution response" will increase by 1.25%. When the water quality of urban water function area is less than 70% or V-class water body appears, "water pollution response" occurs. Due to the diffusion and sustainability of water pollution, before the pollution source is treated, the weight of "water pollution response" in all downstream areas will be increased to 100%.

[0104] The policy management change is specifically manifested as "regional strengthening response" when the land planning and management department issues policies such as strengthening urban economic development or environmental protection cooperation, increasing commodity circulation between nodes, adjusting regional industrial chain layout, etc.; "regional weakening response" when the land planning and management department issues policies such as developing independent industrial types and constructing independent environmental protection system, etc.; "smart growth response" when the land planning and management department issues policies such as optimizing urban industrial structure and improving land use efficiency, etc.; "rapid growth response" when the land planning and management department issues policies such as increasing urban population and industrial capacity, improving service capacity, etc.

[0105] (1) Service industry response:

[0106] Secondary control element 2.1 (16.3%), secondary control element 2.2 (54.0%), secondary control element 2.3 (29.7%);

[0107] In secondary control element 2.1, the priority order of each sub-goal is as follows:

[0108] Sub-control element 2.1.5: Select land not polluted by exhaust gas (8.8%).

[0109] Sub-control element 2.1.4: Select plots that are not contaminated by noise (4.8%);

[0110] Sub-control element 2.1.6: Select plots that have been developed as service / financial land (2.7%).

[0111] In the sub-control element 2.2, the priority order of each sub-control element is as follows:

[0112] Sub-control element 2.2.5: Select plots close to central business districts (25.2%);

[0113] Sub-control element 2.2.6: Select plots that are appropriately close to integrated management departments (15.0%);

[0114] Sub-control element 2.2.7: Select plots that are appropriately close to airports and high-speed rail stations (8.6%);

[0115] Sub-control element 2.2.8: Select plots close to relevant functional departments (5.1%).

[0116] In the sub-control element 2.3, the priority order of each sub-control element is as follows:

[0117] Sub-control element 2.3.4: Select plots close to existing communities (13.9%);

[0118] Sub-control element 2.3.3: Select plots with surrounding bicycle lanes (8.2%);

[0119] Sub-control element 2.3.1: Select plots close to bus stops (4.8%);

[0120] Sub-control element 2.3.2: Select plots close to roads (2.8%).

[0121] (2) Industrialization response:

[0122] Sub-control element 2.1 (54.0%), sub-control element 2.2 (29.7%), sub-control element 2.3 (16.3%);

[0123] In the sub-control element 2.1, the priority order of each sub-control element is as follows:

[0124] Sub-control element 2.1.1: Select plots that do not affect built communities (29.2%);

[0125] Sub-control element 2.1.2: Select plots that do not affect ecologically high-sensitive areas (16.0%);

[0126] Sub-control element 2.1.3: Selecting a site that has been developed for industry (8.8%).

[0127] In sub-control element 2.2, the priority order of each sub-control element is as follows:

[0128] Sub-control element 2.1.1: Selecting a site with a suitable slope (13.9%);

[0129] Sub-control element 2.2.2: Selecting a site close to a road (8.2%);

[0130] Sub-control element 2.2.3: Selecting a site close to a freight terminal (4.8%);

[0131] Sub-control element 2.2.4: Selecting a site close to a warehouse facility (2.8%).

[0132] In sub-control element 2.3, the priority order of each sub-control element is as follows:

[0133] Sub-control element 2.3.3 Selecting a site with a bicycle lane in the vicinity (8.8%);

[0134] Sub-control element 2.3.1: Selecting a site close to a bus stop (4.8%);

[0135] Sub-control element 2.3.2: Selecting a site close to a road (2.7%).

[0136] (3) Response to an aging society:

[0137] Sub-control element 1.1 (54.0%), sub-control element 1.2 (29.7%), sub-control element 1.3 (16.3%);

[0138] In sub-control element 1.1, the priority order of each sub-control element is as follows:

[0139] Sub-control element 1.1.1: Selecting a site with a suitable slope (10.8%);

[0140] Sub-control element 1.1.2: Selecting a site that has been developed as a community (10.8%);

[0141] Sub-control element 1.1.3: Selecting a site that is not affected by noise (10.8%);

[0142] Sub-control element 1.1.4: Selecting a site that is not affected by exhaust gas (10.8%);

[0143] Sub-control element 1.1.5: Selecting a site that is not affected by water pollution (10.8%).

[0144] In sub-control element 1.2, the priority order of each sub-control element is as follows:

[0145] Sub-control element 1.2.5: Select plots close to retail (12.4%);

[0146] Sub-control element 1.2.3: Select plots close to water bodies or green spaces (7.8%);

[0147] Sub-control element 1.2.4: Select plots close to cultural facilities (4.8%);

[0148] Sub-control element 1.2.2: Select plots close to medical services (2.9%);

[0149] Sub-control element 1.2.1: Select plots close to educational services (1.8%);

[0150] In sub-control element 1.3, the priority order of each sub-control element is as follows:

[0151] Sub-control element 1.3.4: Select plots close to established communities (7.6%);

[0152] Sub-control element 1.3.2: Select plots appropriately close to bus stops (4.5%);

[0153] Sub-control element 1.3.3: Select plots with bicycle lanes in the surrounding area (2.6%);

[0154] Sub-control element 1.3.1: Select plots appropriately close to roads (1.5%).

[0155] (4) Youth-oriented social response:

[0156] Sub-control element 1.1 (54.0%), sub-control element 1.3 (29.7%), sub-control element 1.2 (16.3%);

[0157] In sub-control element 1.1, the priority order of each sub-control element is as follows:

[0158] Sub-control element 1.1.1: Select plots with appropriate slope (10.8%);

[0159] Sub-control element 1.1.2: Select plots that have been developed as communities (10.8%);

[0160] Sub-control element 1.1.3: Select plots not affected by noise (10.8%);

[0161] Sub-control element 1.1.4: Select plots not affected by exhaust fumes (10.8%);

[0162] Sub-control element 1.1.5: Select plots not affected by water pollution (10.8%).

[0163] In sub-control element 1.3, the priority order of each sub-control element is as follows:

[0164] Sub-control element 1.3.3: Select plots with a bicycle lane in the vicinity (13.9%);

[0165] Sub-control element 1.3.2: Select plots in close proximity to a bus stop (8.2%);

[0166] Sub-control element 1.3.1: Select plots in close proximity to a road (4.8%);

[0167] Sub-control element 1.3.4: Select plots in close proximity to an already built community (2.8%).

[0168] In sub-control element 1.2, the priority order of each sub-control element is as follows:

[0169] Sub-control element 1.2.1: Select plots in close proximity to educational services (6.8%);

[0170] Sub-control element 1.2.4: Select plots in close proximity to cultural facilities (4.3%);

[0171] Sub-control element 1.2.2: Select plots in close proximity to medical services (2.6%);

[0172] Sub-control element 1.2.5: Select plots in close proximity to retail (1.6%);

[0173] Sub-control element 1.2.3: Select plots in close proximity to water bodies or green areas (1.0%);

[0174] (5) Air pollution response:

[0175] Sub-control element 3.1 (40.0%), sub-control element 3.3 (40.0%), sub-control element 3.2 (20.0%);

[0176] In sub-control element 3.1, the priority order of each sub-control element is as follows:

[0177] Sub-control element 3.1.2: Select plots not affected by exhaust fumes (16.8%);

[0178] Sub-control element 3.1.4: Select plots with an appropriate slope (10.5%);

[0179] Sub-control element 3.1.3: Select plots not affected by water pollution (6.4%);

[0180] Sub-control element 3.1.1: Select plots not affected by noise (3.9%);

[0181] Sub-control element 3.1.5: Select plots that have been developed as environmental land (2.5%).

[0182] In sub-control element 3.3, the priority order of each sub-control element is as follows:

[0183] Sub-control element 3.3.2: Select plots selected as green space in the higher-level spatial plan (18.7%);

[0184] Sub-control element 3.3.4: Select other plots with corresponding development potential (11.0%);

[0185] Sub-control element 3.3.3: Select plots close to distinctive cultural resources (6.4%);

[0186] Sub-control element 3.3.1: Select plots close to water bodies or green spaces (3.8%).

[0187] In sub-control element 3.3, the priority order of each sub-control element is as follows:

[0188] Sub-control element 3.2.1: Select plots close to existing communities (9.4%);

[0189] Sub-control element 3.2.2: Select plots close to bus stops (5.5%);

[0190] Sub-control element 3.2.3: Select plots with surrounding bicycle lanes (3.2%);

[0191] Sub-control element 3.2.4: Select plots close to roads (1.9%).

[0192] (6) Water pollution response:

[0193] Sub-control element 3.1 (40.0%), sub-control element 3.3 (40.0%), sub-control element 3.2 (20.0%);

[0194] In sub-control element 3.1, the priority order of each sub-control element is as follows:

[0195] Sub-control element 3.1.3: Select plots not affected by water body pollution (16.8%);

[0196] Sub-control element 3.1.4: Select plots with suitable slope (10.5%);

[0197] Sub-control element 3.1.2: Select plots not affected by exhaust gas (6.4%);

[0198] Sub-control element 3.1.1: Select land plots not affected by noise (3.9%);

[0199] Sub-control element 3.1.5: Select land plots developed as environmental land (2.5%).

[0200] In the secondary control element 3.3, the priority order of each sub-control element is as follows:

[0201] Sub-control element 3.3.2: Select land plots selected as green space in the upper space planning (18.7%);

[0202] Sub-control element 3.3.3: Select land plots close to distinctive cultural resources (11.0%);

[0203] Sub-control element 3.3.4: Select other land plots with corresponding development potential (6.4%);

[0204] Sub-control element 3.3.1: Select land plots close to water bodies or green spaces (3.8%).

[0205] In the secondary control element 3.2, the priority order of each sub-control element is as follows:

[0206] Sub-control element 3.2.1: Select land plots close to existing communities (9.4%);

[0207] Sub-control element 3.2.2: Select land plots close to bus stops (5.5%);

[0208] Sub-control element 3.2.3: Select land plots with surrounding bicycle lanes (3.2%);

[0209] Sub-control element 3.2.4: Select land plots close to roads (1.9%).

[0210] (7) Regional reinforcement response:

[0211] In the suitability analysis of residential land, the ranking of sub-control element 1.1.3 increases, and the ranking of sub-control element 1.3.1 decreases;

[0212] In the suitability analysis of industrial land, the ranking of sub-control element 2.2.2 increases, the ranking of sub-control element 2.2.3 decreases, the ranking of sub-control element 2.2.4 decreases, and the ranking of sub-control element 2.2.6 decreases;

[0213] In the suitability analysis of environmental land, the ranking of sub-control element 3.3.2 increases, and the ranking of sub-control element 3.2.4 decreases.

[0214] (8) Regional weakening response:

[0215] In the suitability analysis of residential land, the ranking of sub-control element 1.1.3 rises;

[0216] In the suitability analysis of industrial land, the ranking of sub-control element 2.2.3 rises, the ranking of sub-control element 2.2.4 rises, the ranking of sub-control element 2.2.7 rises, and the ranking of sub-control element 2.2.6 falls;

[0217] In the suitability analysis of environmental land, the ranking of sub-control element 3.3.1 rises, and the ranking of sub-control element 3.3.2 falls.

[0218] II. Planning stage

[0219] Based on the unsuitable construction land result output by the construction land screening, according to the classification of the target town, the rigid growth bottom line of the town is obtained based on the preset growth influencing factors. On this basis, adaptive analysis is carried out on all land blocks within the range of the town's developable land, and the basic elastic interval of the town's land space planning growth prediction is obtained combined with the population growth prediction. The preset growth influencing factors include:

[0220] Geographical factors: slope factor and coastal factor;

[0221] Ecological factors: forest factor and wetland factor;

[0222] Population factor: population factor;

[0223] Economic factors: community factor, commercial factor, industrial factor and service factor;

[0224] Policy factors: protection factor and urban agglomeration attraction factor;

[0225] Cultural factors: historical and cultural factors;

[0226] Infrastructure factors: highway factor, main road factor, traffic node factor, road density factor, water body factor.

[0227] According to the overall goal of the town, the town is classified, and each type of city corresponds to different driving forces. According to the different main industries of the town, the town is divided into three types: industrial town, financial / service industry town and residential town. According to the results of logistic regression analysis, the driving force factors of each type of town are as follows:

[0228] The driving force factors of industrial town in order of influence are: industrial factor (31.2%), community factor (22.2%), highway factor (15.5%), traffic node factor (10.8%), water body factor (7.4%), urban agglomeration attraction factor (5.1%), main road factor (3.5%), forest factor (2.5%), population factor (1.8%).

[0229] The driving force elements of the financial / service industry town are in turn: highway elements (31.2%), main roads (22.2%), traffic node elements (15.5%), urban agglomeration attraction elements (10.8%), population elements (7.4%), water body elements (5.1%), road density elements (3.5%), slope elements (2.5%), and community elements (1.8%).

[0230] The driving force elements of the residential town are in turn: water body elements (31.2%), road density elements (22.2%), slope elements (15.5%), community elements (10.8%), highway elements (7.4%), main road elements (5.1%), traffic node elements (3.5%), urban agglomeration attraction elements (2.5%), and population elements (1.8%).

[0231] Using the statistical analysis results, combined with the control system, the future growth boundary of the town is predicted. The target of town growth is mainly controlled by experts and city and superior managers, and its performance method is to select and sort all secondary control elements, and then obtain the weight through the analytic hierarchy process. The results of logistic regression express the passive adaptation of the town under the existing objective conditions, and the control system expresses the subjective will of the main body participating in the town land space planning. Therefore, the prediction results of the two account for 50% in the final result.

[0232] The identification of the town land space planning growth boundary combined with target control and statistical analysis is mainly used to describe the possibility of the surrounding land of the town being urbanized in the future. On the basis of the above analysis, the expansion coefficient needs to be combined to obtain the growth boundary of the town. The expansion coefficient (r) is the ratio of the urban construction area to the regional population growth. The calculation method is:

[0233] r = [(A1-A0) / A0] / [(P1-P0) / P0]

[0234] In the formula: A1 represents the area after the town grows, A0 represents the area before the town grows, P1 represents the population before the town grows, and P0 represents the population after the town grows.

[0235] Under the basic scenario, the expansion coefficient should take the general rate of urban development, which is 5:1. The expansion coefficients under various change modes are as follows:

[0236] Industrial mode change: service industry response, expansion coefficient decreases by 10%; industrial response, expansion coefficient increases by 10%.

[0237] Population structure change: aging society response, expansion coefficient decreases by 10%; young society response, expansion coefficient increases by 10%.

[0238] Ecological environment change: air pollution response or water pollution response, expansion coefficient decreases by 10%.

[0239] Policy management change: smart growth response, expansion coefficient decreases by 20%; fast growth response, expansion coefficient increases by 20%.

[0240] The specific calculation process is as shown in Figure 3

[0241] III. Planning implementation phase

[0242] After the planning passes the approval process, it enters the planning implementation part, which mainly corresponds to the scenario system part in the model. Combined with the positioning of urban development, possible external changes are predicted, and the control system is adjusted. Based on the basic control system, the corresponding response weight is superimposed according to the proportion, and the urban land space planning is adjusted accordingly, finally affecting the land use decision.

[0243] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.​

Claims

1. An interactive model system for urban territorial spatial planning based on dual organization and development monitoring, characterized in that, This includes a public participation platform, a construction land screening module, an urban spatial planning growth module, and an urban development monitoring module; The aforementioned public participation platform is a tool platform for various users in the town to participate in town decision-making, and it collects and integrates the interests and demands of multiple stakeholders. The entities responsible for formulating, managing, and using territorial spatial planning can interact internally through the aforementioned public participation platform, jointly participating in the entire process of formulating and managing territorial spatial planning. The construction land screening module identifies and excludes land within urban areas that is unsuitable for construction based on policy factors, environmental factors, and engineering factors. The urban spatial planning growth module includes an urban territorial spatial planning statistical growth forecast submodule and an urban territorial spatial planning adaptive growth forecast submodule; The urban development monitoring module enables the setting of development monitoring elements, the correction of expansion coefficients and control weights, the prediction of changes that may be encountered in the long-term development of towns, and the formulation of corresponding passive feedback mechanisms to make corresponding adjustments to the territorial spatial planning. The system also influences the calculation of urban spatial planning statistical growth forecast and urban spatial planning adaptive growth forecast through a control system; The dual organization realizes top-down external organization construction behavior based on regional macro space and industrial policies, and bottom-up self-organization construction behavior based on the location resources of the micro-plots themselves. The development monitoring elements are set up during the model building stage. Based on the general laws of urban development and the possible types of changes, the monitoring object parameters are preset for each potential external change. These parameters serve as the triggering premise and basis for the expansion coefficient correction and control weight correction. The expansion coefficient correction is applied to the statistical growth forecasting module, which adjusts the expansion coefficient based on changes in the external environment to control the rigid growth bottom line of the national land space planning. The control weight correction is applied to the adaptive growth prediction module, which adjusts the control system weights based on changes in the external environment to control the flexible growth range of the national land space planning. The system determines urban growth boundaries based on a rigid growth baseline from top to bottom and a flexible growth range from bottom to top.

2. The interactive model system for urban territorial spatial planning based on dual organization and development monitoring as described in claim 1, characterized in that, The urban territorial spatial planning statistical growth forecasting submodule is based on typical sample urban data and constructs a typical growth model for urban territorial spatial planning through logistic regression analysis, which reflects the quantitative analysis of objective laws in the planning process.

3. The interactive model system for urban territorial spatial planning based on dual organization and development monitoring as described in claim 1, characterized in that, The aforementioned urban land space planning adaptive growth prediction submodule is based on the target urban type and constructs a control system based on public participation through urban land use adaptability model and analytic hierarchy process, reflecting a reasonable expression of subjective guidance.

4. The interactive model system for urban territorial spatial planning based on dual organization and development monitoring as described in claim 2, characterized in that, The typical growth models for urban territorial spatial planning include industrial town models, financial / service town models, and residential town models.

5. The interactive model system for urban territorial spatial planning based on dual organization and development monitoring as described in claim 1, characterized in that, The control system includes an industrial sub-control system, a human settlement sub-control system, and an environmental sub-control system under the guidance of the overall urban development goals. Each sub-control system integrates the opinions of all stakeholders through the analytic hierarchy process (AHP) to rank the pre-set control system and obtain the proportion of sub-control elements in the final land use decision.

6. The interactive model system for urban territorial spatial planning based on dual organization and development monitoring as described in claim 5, characterized in that, The stakeholders mentioned in China's urban planning system include higher-level management agencies, urban management agencies, and citizen groups; The superior management agencies include superior comprehensive management departments, superior spatial planning departments, and superior special planning departments. The participation mode of superior management departments is through the translation of superior plans or guidance documents. The urban management agencies include local comprehensive management departments, local spatial management departments, and local industrial departments. The participation mode of urban management departments is through direct participation via public participation platforms. The participation patterns of the citizens include both conscious and unconscious online participation, which are collected and integrated through online public participation platforms.

7. The interactive model system for urban territorial spatial planning based on dual organization and development monitoring as described in claim 1, characterized in that, The public participation platform can collect conscious linear public participation data online and provide real-time feedback, while also collecting conscious network public participation data and unconscious public participation data, which are then provided to the entities responsible for formulating territorial spatial plans.

8. A method for urban land spatial planning, characterized in that, The interactive model system for urban territorial spatial planning based on dual organization and development monitoring, as described in any one of claims 1-7, is adopted.

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