Intelligent management information system of urban planning based on GIS

Abstract

With the development of urbanization, the application of GIS technology is more and more extensive. This study mainly discusses the development of urban planning intelligent management information system based on GIS. To design and build a rule-detailed spatial data model, provide the physical model and the data model corresponding to the logical layer from top to bottom in all steps, based on the attribute information stored in the Geodatabase model. According to the parameters set, connect to the database through the Oracle Connection class. The defined query criteria are converted into SQL statements that are executed using the Oracle Command class. Multi-source data integration middleware integrates various data formats with a GIS software format conversion tool or direct reading tool and then uses the geometric encoding semantics of data dictionary to represent the integrated data of system data model after merging. Property queries use the interactive search function for properties and spatial information to query the land use index for a particular area of the chart. If there is a scene roaming request from the input device, the 3D scene needs to be adjusted according to the input. Display the scene effects of a 3D virtual demonstration on a computer monitor. Start the GIS management operation function to deal with the case, and realize the user’s management of the urban planning system function with the concept of stratification. Fuzzy recognition mode is applied to identify the degree of the environmental impact of eco-city planning. The impact of urban planning on the environment is H ≈ 0.11 (0.1 < H < 1), which meets the expected standard. The results show that the system demand evaluation designed in this study is good, and the overall operation of the system is relatively stable, which plays a promoting role in urban planning.

Keywords

GIS technology urban planning intelligent management system research

1 Introduction

With the development of computer technology, the application of geographic information systems (GIS) is more and more extensive, and the corresponding technical design and application are more and more mature. Regarding spatial information, previous management information methods seem to be ineffective. To make reasonable analysis and decision of spatial information, better analysis and processing methods are needed. Urban planners must make full and reasonable use of urban spatial information, which is conducive to the overall planning, management, and decision-making of the city, and realize the rational construction of urban planning as soon as possible.

If you do not make a careful plan for the city, you will not be able to achieve city construction and operation management. GIS is a formal computer-based information system that can merge data from multiple sources to provide the information needed for planning and decision-making. Its application and promotion have a great influence on the planning industry, but there are still some problems in planning information management in non-standard markets, information asymmetry, and information distortion. Therefore, the development of a GIS-based planning management information system and the research on the application of GIS in the planning field have important value for analysis and discussion.

The GIS-based urban planning statistical evaluation system uses a comprehensive secondary development method based on ArcGISEngine. In order to realize the development of the GIS-based urban planning statistical evaluation system, firstly, according to the demand analysis of the system and the design requirements of functional modules, the components and tools encapsulated in ArcGISEnge were selected. Next, query other data documents related to interfaces, properties, and related components and tools. Finally, use a programming language to access related interfaces, organically combine various components and tools to complete the development of a GIS-based urban planning statistical evaluation system. The main contents of this study are as follows:

Chapter one and chapter two introduce the statistical evaluation of urban planning at home and abroad, which is the basis of theoretical research and practical work. Statistical assessment includes data formats and deficiencies in systems science methods. In order to provide an efficient statistical evaluation of urban planning, we combine urban planning with the statistical evaluation index and use the urban planning system based on GIS for urban planning.

The third chapter introduces the related technology of system design. Based on application requirements, paperless office mode can improve the efficiency and accuracy of the office process. Use GIS to provide analysis function, help staff to make decisions, improve the level of system information, improve the efficiency of urban planning and management.

The fourth chapter explores the three-dimensional virtual display to provide a clearer display platform for urban planning. Let the public participate in the construction of urban planning.

The fifth chapter is to implement the proposed system according to the design concept. Several different modules of the urban planning system based on WebGIS are validated and further analyzed according to specific data. The design of the system meets the design requirements and achieves the expected results, but there are also different shortcomings and areas to be improved.

The sixth chapter puts forward the overall summary of the urban planning system and the prospect of the future.

2 Related work

For more than ten years, people have extensively studied the integration of geographic information systems (GIS) and environmental modeling. Yassemi S believes that this integration is still a difficult task due to environmental changes and the static nature of GIS. His research combines GIS and cellular automata (CA) technology to develop a fire behavior model through a flexible and friendly end-user interface. The model he developed combines terrain, forest fuel, and weather variables. He evaluated the performance of the implemented fire alarm model by comparing it with the fire spread simulation of Prometheus (the Canadian national fire alarm behavior modeling tool based on the principle of elliptical wave propagation). He tested the developed fire behavior model using spatial data from the 2001 Dogrib Fire near Nordegg Alberta, Canada. His research lacks a theoretical basis [1]. Many urban planning projects today aim to realize the ambitions of smart cities. Axelsson K believes that planning and building a smart city area, integrating ICT into key infrastructure, and controlling and managing urban functions in innovative ways requires a new way of urban planning work. He studied urban development in Sweden and implemented new planning methods in it. The developed framework is used to analyze the complexity of the urban planning process. By defining the intelligence dimensions of each stakeholder’s main concern and analyzing its consequences, the framework can determine the contribution of each stakeholder to the process and results. His research process lacks data [2]. With the massive use of resources, the increase in urbanization has become a common problem for all countries. Al-Kofahi SD believes that geospatial data, maps, and urban growth indicators are the basic requirements for monitoring and evaluating urban expansion, development, and resource use. He conducted interviews and investigations on all national government agencies. Use Landsat-8 imagery, geographic information system, classification workflow, and statistical methods in ENVI-5 to evaluate the urban growth indicators selected by GIM. The Landsat-8 image he used was not accurate enough [3]. Lee S believes that compact urban planning factors generally also apply to metropolitan areas. Based on the changes in the relationship between the transport mode split (TMS) and the intra-city travel ratio (ITR) between 2006 and 2010, he surveyed the travel mode of each city and town. His research process lacks practice [4]. Hong R believes that with the rapid urbanization process and strict requirements for energy conservation and environmental protection, eco-city has become the top priority of China’s urban development. He constructed a 3D evaluation model of ecological city planning through index selection. Use the distance measurement method to comprehensively rank the solutions, and use the four-grid evaluation screen for projection analysis. Finally, the 3D evaluation model was used to evaluate the ecological city planning of Shenzhen, Chongqing, and Weifang. His research process lacks data [5].

3 Smart city management

3.1 GIS technology

With the help of computer technology, GIS technology, multimedia technology, the three-dimensional urban planning system establishes all social and cultural phenomena of urban infrastructure, and comprehensively stores the information contained in a specific data from through the comprehensive management of spatial databases, in the information system three-dimensional simulation module display, realize urban planning management in the process of analysis and management. By constructing a perfect three-dimensional urban planning information system, it can not only effectively display and manage the spatial information of the city, but also provide a spatial reference to simulate the current situation of the city from multiple times, multiple perspectives, and multiple levels [6, 7]. Moreover, it ensures the reliability of information for spatial decision-making in urban planning and management. Since the concept of a digital city was born, many researchers have put forward their own opinions on the field of 3D city planning [8].

Now, 3D GIS applies to all aspects of urban planning management [9, 10]. Its powerful visual analysis management function realizes the urban planning management from two-dimensional to three-dimensional changes and plays an important role in urban planning. For assessment and approval, urban planning management provides intuitive visual solutions, scientific decision analysis, efficient management style, and extensive data capacity. Using three-dimensional GIS technology, urban planners can browse plans in real-time in a macroscopic 3D space environment, realize intuitive comparison of multiple projects, adjust urban buildings and public facilities, optimize alternative plans, implement the 3D spatial analysis of urban buildings, and conduct building approval management f (x, z) digitization and automation [11].

$\begin{matrix} min f (x, z) = & \sum_{k = 1}^{m} \sum_{i = 1}^{n} a_{ki} x_{ki} z_{i} + \sum_{i = 1}^{n} \sum_{j = 1}^{l} b_{ij} x_{ij} z_{i} \\ + \sum_{k = 1}^{m} \sum_{i = 1}^{n} v_{ki} z_{i} + \sum_{k = 1}^{m} \sum_{i = 1}^{n} z_{i} F \end{matrix}$ (1)

Among them, x and z are corresponding data management services. As an important direction for future GIS technology development, 3D GIS uses 3D space coordinates to simulate and visualize the real world, break the limitations of 2D plane data performance capabilities, and conduct comprehensive management of geometric meaning and topological information. Provide detailed integrated applications in various environments [12].

3.2 Fuzzy theory

Set the index weight matrix W, the index eigenvalue relative member matrix R, the standard eigenvalue relative member matrix S, and the relative member matrix D_hj of all levels in the sample set of fuzzy concept A of all levels [13, 14].

$\begin{matrix} D_{hj} = & u_{hj} d_{hj} = u_{hj} {\sum_{i = 1}^{m} {[w_{ij} (r_{ij} - s_{ih})]}^{p}}^{\frac{1}{p}} \\ + min \sum_{h = aj}^{b_{j}} D_{hj}^{2} \end{matrix}$ (2)

By using neural networks to extract effective signals and input them to fuzzy inference, the difficulty of the fuzzy rule process can be greatly reduced [15]. When the neural network is delayed, the fuzzy theory is used to preprocess the input signal, and then the neural network is used to complete the fault diagnosis function, which can effectively improve the accuracy of the diagnosis result [16, 17]. If the crossover probability and mutation probability of the pure genetic algorithm are P and P_m respectively.

$\begin{matrix} E [m (S, t + 1)] \\ = m (S, t) \frac{f (S, t)}{f (t)} [1 - P_{c} \frac{δ (S)}{L - 1} - O (S) P_{m}] \end{matrix}$ (3)

Define the length ∂, and the string length is L. If the connection weight between the clarity and the rule layer is set to $δ_{i}^{3}$ , then there are:

$\begin{matrix} δ_{i}^{3} & = \frac{\partial E}{\partial O_{i}^{3}} = \sum_{i = 1}^{C} \frac{\partial E}{\partial O_{i}^{4}} \times \frac{\partial O_{i}^{4}}{\partial O_{i}^{3}} \\ = \sum_{i = 1}^{C} \frac{\partial E}{\partial O_{i}^{4}} \times W_{ij} \frac{\sum_{k = 1}^{δ} O_{k}^{3} - O_{j}^{3}}{\sum_{k = 1}^{δ} O_{k}^{3}} \end{matrix}$ (4)

Here, S is the number of fuzzy rules generated by $δ_{i}^{3}$ . Fuzzy control describes human experience knowledge in the form of natural language, which is used to express the transitional relationship between “belonging” and “not belonging”. This is a control mode that follows the habits of human thinking and reasoning [18].

$h_{p} (z (t)) = \frac{\prod_{m = 1}^{l} M_{pm} (z (t))}{\sum_{p = 1}^{r} {coprod}_{m = 1}^{l} M_{pm} (z (t))} \sum_{p = 1}^{r} h_{p} z (t) = 1$ (5)

r represents the number of defined fuzzy rules [19, 20].

3.3 City planning

In some cities in developed countries, planning departments have begun to use big data to effectively collect urban planning management information. In terms of information integration, future urban planning aims at management information collection, regional cooperation, and overall planning, and uses smart city road development as the core driving force. For urban planning services, more information collection is needed. With the deepening of landscape model research, ecologists have deepened the dynamic changes of landscape models, the evolution of landscapes, and the optimization of landscape models, speeding up the development of landscape ecology. Landscape mode has been applied to new fields. According to statistics, the theory of landscape mode applies to all fields of production and life. The main aspects include land development, resource protection, and utilization, urban and rural planning, new rural construction, large-scale regional environmental and ecological process simulation, etc.

4 Urban planning intelligent management information system design experiment

4.1 Spatial database design

Based on Geodatabase, in order to design and construct a detailed spatial data model of the rules, according to the attribute information of the stored Geodatabase model, the physical model and the data model corresponding to the logical layer are provided from top to bottom in all steps. The geographic elements associated with each spatial layer in the table are stored by default. In other words, the element is displayed on the map corresponding to the attribute information record. Each space corresponds to the entity data model of the object, each layer element corresponds to the same attribute type element category, and each space corresponds to the feature dataset.

To query database data, you must first set the database connection parameters, including data source, account, password, and other parameters. Then, according to the set parameters, connect to the database through the Oracle Connection class. The defined query conditions will be converted into SQL statements. Finally, the SQL statement is executed using the Oracle Command class, and the query results are stored in the data table using the Oracle Data Adapter class.

4.2 Multi-source data integration middleware

Multi-source data integration middleware uses format conversion tools or direct reading tools of GIS software to integrate various data formats, and then uses the geometric coding semantics of the data dictionary to represent the combined data using the system data model. The data dictionary structure is shown in Table 1. Use the converted data, metadata, metadata standards, and content to establish and form a target system for GIS data. Although the data in CAD format has no code attributes, in addition to using the thickness of the CAD graphics function and other parts of the development of AutoCAD drawing software, the codes of geographic entities are also stored to ensure the difference in colors, line widths, symbols, and other formats. Therefore, the CAD format data dictionary needs to give priority to feature names as the primary key. In this way, a data recognizer is constructed using data in CAD format, and the name of the ground object is associated with the geographic code to create a code data dictionary.

Table 1
Data dictionary structure

Feature name Layer Display method Colour Line width

GPS control points M gc132 4 0.1

Street line N 815a 1 0.2

Barbed wire P 457 3 0.3

Defined provincial boundaries Q 852a 3 0.4

Feature name	Layer	Display method	Colour	Line width
GPS control points	M	gc132	4	0.1
Street line	N	815a	1	0.2
Barbed wire	P	457	3	0.3
Defined provincial boundaries	Q	852a	3	0.4

4.3 Vector data storage

The urban planning management system contains various graphics. To establish an integrated spatial data platform, the graphic data collected by MapGIS must be formatted, imported into Oracle, and saved. When the original CAD format of road maps, road centerlines, road nodes, and other data structures need to be adjusted, the vector data software will store them in an image in the order of the SuperMap workspace. If the system needs to be defined according to the logical structure, several layers will be converted into different layers. And pay attention to maintaining data attributes.

4.4 Raster data storage

Table form data such as remote sensing images, 3D terrain models, and regional geological maps can be regarded as raster data. The raster dataset is composed of one or more raster bands of data. The raster dataset is mainly stored in the ArcEngine environment, including three-component classes: raster workspace factory, raster workspace, and raster SDE loader. The function of each component is shown in Table 2.

Table 2
Functions of each component

Component Features

Raster SDE Loaderr Raster data storage

Raster SDE Connection Set database connection properties

Raster SDE Storage Set storage parameters in new SDE raster dataset

Raster SDE Server Operation Realize data storage

Component	Features
Raster SDE Loaderr	Raster data storage
Raster SDE Connection	Set database connection properties
Raster SDE Storage	Set storage parameters in new SDE raster dataset
Raster SDE Server Operation	Realize data storage

5 Design analysis of urban planning intelligent management information system

5.1 Analysis of management information system design results

Users can import, add, delete, modify, update, and format conversion functions of graphic data through the editing management module. The editing management module includes three functional modules: graphics input, graphics processing, and drawing finishing. Different functional modules complete the corresponding tasks. The graphics data can be imported into the system through the graphics input function; the graphics processing function is mainly for the input graphics are processed purposefully to meet the actual needs of users. At the same time, the graphics can be updated according to the actual situation to maintain the current status of the system graphics. Drawing finishing is to further organize the processed graphics to achieve the effect of the beautiful drawing. After clicking the “Find attribute information from graphics” submenu in the “GIS-based comprehensive statistical analysis” menu, directly click on the graphics to be queried on the map with the mouse, and the attribute information corresponding to the graphics can be popped up in the form of a list. Take the query of the administrative division layer of the peak mining area as an example, click any area on the layer, and through the pop-up attribute list, you can know the name, population scale, land scale, town level, town function, and other information of the selected layer. Finding graphics by attribute information is to find the corresponding graphics according to the conditions met by the attribute information in the layer attribute table. After selecting the layer to be queried in the “Layer Name” drop-down box, all the attribute fields in the layer attribute table will be listed in the “Field Name” area. After selecting an attribute field, click the “Get the unique value of this field” button, then all the attribute information values of the selected field will be listed. In the “query expression” area, click the field name and various operators to construct the query expression, and then click the “query” button to obtain the layer corresponding to the attribute information that meets the conditions. After clicking the “Query” button, the query results are shown in Table 3. In Table 3, the “query result list is displayed” are all attribute records that satisfy the query expression. For example, in town A, the population is 56,789, and it mainly depends on coal mining to maintain the economy.

Table 3
Query result display

Name Population size Land scale(m³) Town level Town functions Per capita income

A 56798 55.6 General Coal 4678

B 47809 98.4 Focus Tourism 6789

C 67098 78.8 Focus Farming 5567

D 47630 90.1 Central city Building materials 4798

Name	Population size	Land scale(m³)	Town level	Town functions	Per capita income
A	56798	55.6	General	Coal	4678
B	47809	98.4	Focus	Tourism	6789
C	67098	78.8	Focus	Farming	5567
D	47630	90.1	Central city	Building materials	4798

5.2 Analysis of planning technical indicators

The collaborative planning module is the most important module of the system, among which the most important is the collaborative drawing function, which mainly highlights the concept of collaboration. Urban special planning is a planning project that requires the collaboration of multiple planners from multiple departments to complete. This requires our urban special planning system to have the function of collaborative planning. Collaborative planning can allow planners to communicate in real-time and share planning results at the same time, which can greatly improve the efficiency of team planning. Because planning technical indicators have different focuses in various professional planning sections and different stages of urban planning management, to focus on different technical indicator parameters, the realization of this function should be based on the technical aspects of each professional planning section and each stage. Based on the understanding of index calculation methods and reference standards. Retrieve the existing standard value of planning technical indicators, and calculate the new planning value through the calculation formula, reflect the quantitative relationship between the standard planning value and the new planning value through the chart, and give a reasonable degree of evaluation. The data retrieval function is divided into two parts: the query of the conventional database and the query of the spatial data. The query of the database can be given different SQL query statements according to different query classifications. The query of the spatial data can be realized by the search method of the MapX component. The calculated space compactness result is shown in Fig. 1. Taking the calculation of the spatial compactness of the current land use layer as an example, after selecting the layer, start to calculate the minimum circumscribed circle of the spatial area corresponding to the current land use layer. The calculation result shows that the compactness of this area is about 0.22, which means that the area of this area is about 1/5 of the area of its corresponding smallest circumscribed circle, which means that the space area is not compact. From the perspective of realizing the function of regional planning, when the model is built, its core lies in the operation of the model, and its operation requires the input of data, so it is necessary to analyze the interface between the model and the data. The original data input method is that each model has its data or data files. This method is only suitable for the operation of a single model and cannot realize the sharing of data. The planning system needs to store all data in the database, and a database management system for unified management to facilitate data input, query, modification, and maintenance. Therefore, in order to be able to access the data in the database in the model program, an interface between the model and the database must be established. To calculate the space compactness, first, select the layer to be calculated in the “Select Layer” drop-down box, and then click the “OK” button to start calculating the space compactness of the selected layer.

Fig. 1

Calculated space compactness result.

5.3 System requirement analysis

The urban special planning system loads a large amount of data, and there are a large number of people participating in collaborative planning. At the same time, some urban geographic map data and planning results are confidential. These characteristics of urban special planning make us have certain requirements for the performance of the urban special planning system: the confidentiality of data and planning results makes us need to make the urban special planning system safe. Must provide strict authority management functions to prevent virus infringement and malicious attacks, while implementing a backup mechanism to ensure the security of critical system data. Since the format of the data may not be completely unified, the system should be compatible, and it needs to have a certain error correction ability in the planning process to avoid publishing wrong planning projects. Some problems that may be caused by existing plans should be predictive. The interface of the urban special planning system should be concise and used, simple to operate, in line with common sense, and suitable for planners and technicians who do not follow the hierarchy. An excellent software system should meet the performance requirements of all the above software systems. The specific indicators that measure the above software system include response time, throughput, resource utilization, number of clicks and number of concurrent users. In the process of regional planning based on GIS, data are generally organized and managed in layers, and maps are processed hierarchically. When implementing query and analysis, users can superimpose different layers on each other according to the needs of the system content to obtain a map that can meet their needs. The user manipulates the layer through its visible, editable, selectable and automatic labeling attributes, which makes the system’s data organization diversified, and data management is also very convenient. For statistical analysis of land use, first, select the planning layer of a certain area and the current status layer of the corresponding area in the drop-down boxes of “Select Planning Layer” and “Select Current Layer”, and then click “Conform to Planning Layer”. Get the corresponding analysis result layer save path in the text boxes of “Undeveloped Layer” and “Self-Developed Layer”, and finally click the “OK” button to start the statistical analysis of land use. For the layer that meets the planning, the land use type may change due to the change of the land use type by using the land type transfer matrix. For undeveloped land and self-developed land, according to the undeveloped map layer and self-developed map layer in the analysis results, the area of each type of land use type is counted separately. The statistical analysis results of land use are shown in Fig. 2. According to the fuzzy comprehensive evaluation method, the evaluation with the largest value is taken as the comprehensive evaluation result. That is, 30% of the respondents think that the comprehensive evaluation of the urban planning system in general. 10% of the questionnaire respondents think the comprehensive evaluation is poor, 30% of the questionnaire respondents think the comprehensive evaluation is good, and 30% of the questionnaire respondents think the comprehensive evaluation is good.

Fig. 2

Land use statistical analysis results.

5.4 Application analysis of fuzzy recognition in urban planning

The integration of data dictionary refers to the establishment of a new data dictionary using the correspondence between data dictionary items of different data sources and data dictionary items of the target system and then using this new data dictionary to unify geocoding and semantic expression of data from different sources. The analysis of the specific realization process is as follows: CASS is more detailed than ArcGIS’s classification of ground objects. For example, the triangle points are divided into triangle points and small triangle points on the mound. In order to realize the integration between the two code dictionaries, the fields in CASS that do not belong to the feature classification must be deleted from the code dictionary, and the ArcGIS code dictionary should be expanded based on the principle of reflecting the detailed feature classification. After finishing the above-mentioned sorting and expansion work, a mapping relationship is established between the two code dictionaries through “graphic element description” and “element name”, and the two code dictionaries are integrated to form an integrated data dictionary. The use of an integrated data dictionary to realize the integration of multi-source data can avoid the problem of incomplete attributes caused by data conversion, and can ensure the consistency of semantic expression of geographic entities, and has the characteristics of retaining their own coding advantages and dual recognition. In order to achieve the conversion that meets the system requirements through the designed integrated data dictionary, the CAD format data must be converted into GIS format data. This study uses the tools developed by ArcGIS to perform format conversion. By using the classification code or classification name to associate the integrated data dictionary with the spatial geographic data, the CASS classification code can be converted to the system code, and the attribute information implicit in the CAD format can be added to the system. The use of a code dictionary to carry out the semantic conversion of multi-source data, so as to realize the integration of multi-source data, is feasible in actual operation. It solves the problem of converting a large number of CAD format data to GIS format data. But the biggest difficulty of this method is the establishment and integration of a multi-source data code dictionary, which requires a lot of time and energy to process.

The fuzzy recognition mode is used to identify the fuzzy concept of the environmental impact degree of ecological city planning. Known ecological city planning programs involve six environmental themes, including sustainable development capacity building, water environment, atmospheric environment, solid waste, acoustic environment, and ecological environment protection. The sum of all environmental thematic indicators is the indicator system for planning environmental impact assessment. The evaluation standard selects three levels (ie c = 3): Level I means that the planned environmental impact is small, level II means that the environmental impact is small, and level II means that the environmental impact is greater. As the ecological city planning scheme has a characteristic value of 1 to 3 when the characteristic value is 1, the environmental impact of the scheme is small; when the characteristic value is 3, the environmental impact of the scheme is more serious. Figure 3 shows the impact of urban planning on the environment. According to the result of 3 in figure H ≈ 0 . 11 (0.1 < H < 1), it can be seen that the impact of ecological city planning on the environment is between I and II, and the implementation of urban planning may have a small impact on the environment. It can be seen that H1, H4, H5, and H6 of the four environmental themes are greater than the characteristic value H of the planning scheme, among which H5 and H6 are equal to or approximately equal to level II, which shows that the acoustic environment and ecological protection have the greatest contribution to the environmental impact. If considering further mitigation of environmental impact, we should first start with these two aspects. The characteristic values of H1 and H4 are relatively close to level II, that is, the city’s work on sustainable development capacity building and solid waste treatment need to be strengthened. H2 and H3 are relatively small, especially H3 is close to level I, which shows that the current state of these two aspects in the urban development strategy can be maintained. According to the above analysis, the focus of environmental work in urban development in the future is to strengthen the improvement of the acoustic environment and the ecological environment, and at the same time strengthen the building of sustainable development capacity and the treatment and reuse of solid waste, and change the current situation of uncoordinated development of various environmental themes, and finally achieve a more balanced situation of urban environmental theme levels.

Fig. 3

The impact of urban planning on the environment.

5.5 System function test analysis

In the business functions of the user management module, when testing these functional modules, the system salesman is first responsible for the management, and the system testers prepare test cases according to the needs of the system. The function test result of the system is shown in Fig. 4. It can be seen from Fig. 4 that when the user management module in the urban planning management system is specific, it is necessary to consider the demand content determined by the business function module in the demand analysis stage, and then carry out the demand analysis content and demand analysis through specific tests. One-to-one correspondence, determine whether the business functions of the system are complete, record the problems that occur and send them to the software developer to modify the program. System management is mainly for the business of the system management level. First, it is the management of user information. User information mainly refers to users who log in to the system, including ordinary users, administrator users, etc. In addition to user information management, it can also control the specific roles of users. The setting is based on the user’s operating function and the department to which it belongs, and the permission setting adopts the current mature technology model commonly used in Web information technology, which effectively improves the security of the system. In the business functions of the business management module, when testing these functional modules, the salesman of the system is first responsible for the management, and the system testers prepare test cases according to the needs of the system. The test of the urban planning management system is mainly carried out from two perspectives. The functional test specifically focuses on whether the content of the system function module is correct. After the functional test is completed, the performance of the system is tested, and the system is accessed through concurrent access and response time. After the test, the urban planning management system has the following test results in general: the urban planning management system has designed and implemented the functions determined and developed during the demand analysis stage, and the specific business logic processing process conforms to the content determined by the demand analysis. The urban planning management system complies with the relevant software development specifications and completes the needs analysis, system design, implementation, and testing tasks in each phase according to specific standards. The urban planning management system has good scalability. When designing and implementing the specific business of the system, the coupling between various functional modules is reduced, which is convenient for subsequent software developers to upgrade and maintain the functions of the system, and effectively improves the information scalability of the management system. The urban planning management system has good maintainability: effective design of the information management system’s session control, report processing, fault recovery, and other processing mechanisms, effectively improving the maintainability of the system. The urban planning management system has good operability: the interface of the system is well designed, the level of human-computer interaction is submitted, the user can operate the system after simple training, and the system will promptly give prompt information during daily user operation, which is convenient user’s next action. The urban planning management system has good security: for the system, the security of data information is the primary prerequisite. Strict protection measures are set for the database, from physical operation security, network security, permission setting, data information encryption, etc. Comprehensively improve the security of the information management system. Use the system interface to enter relevant spatial data and attribute data. After the operation is over, use the ArcGIS desktop program to open the map layer to verify whether the graphics and related attribute information have been entered. Open the batch map layer, batch land operation layer, stock land layer, stock land operation layer, and check whether all graphics covered by the land parcel have been changed to supply. The results show that the graphics and attributes in the map layer are complete, and the attributes of the relevant reserve layer of the operation layer corresponding to the reserved layer have been provided, which meets the expected standard.

Fig. 4

System function test results.

6 Conclusion

This research discusses the evaluation of urban planning based on GIS and takes urban planning as the background. It introduces the actual work of urban planning theoretical research and statistical evaluation at home and abroad and uses the systematic scientific method to design urban planning indicators. Systematic evaluation of the statistical evaluation of GIS-based urban planning system and GIS-based urban planning solutions. This research introduces the classification and characteristics of urban planning data, the content and methods of urban planning statistical evaluation, and other basic theoretical knowledge. According to the demand analysis, carry out the evaluation index design, the overall design, and the detailed design of the functional modules based on the GIS-based urban planning system. The system operation will be realized according to the design scheme. Finally, the system will be applied to actual work.

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