Quantitative analysis method of historical urban spatial structure evolution based on GIS and space syntax

Abstract

This invention discloses a quantitative analysis method for the spatial structure evolution of historical cities based on GIS and spatial syntax. First, a multi-period historical spatial spatiotemporal database is constructed. Then, quantitative indicators are obtained through multi-dimensional spatial syntax and GIS spatial analysis. After integration and analysis, spatial evolution trajectories and turning points are identified. Finally, the evolution mechanism is explored by combining horizontal and vertical comparisons and driving factor analysis. This method achieves accurate integration and multi-dimensional quantitative analysis of historical spatial data, accurately tracks evolution patterns, and deepens the exploration of driving mechanisms, providing a scientific basis for the protection and planning of historical cities.

Description

Technical Field

[0001] This invention relates to the field of urban spatial analysis and historical geographic information processing technology, specifically a quantitative analysis method for the evolution of historical urban spatial structure based on GIS and spatial syntax. Background Technology

[0002] The spatial structure of historical cities is a spatial carrier that reflects the combined effects of multiple factors, including natural geographical conditions, socio-economic activities, cultural traditions, and policy interventions, during the city's long development process. Accurate analysis of its evolutionary patterns is of irreplaceable importance for the protection of historical and cultural heritage, the optimization of urban spatial planning, and the transmission of historical context. Currently, the field of historical city spatial structure evolution analysis faces numerous technical bottlenecks. Traditional research methods primarily rely on a combination of documentary research, on-site investigation, and qualitative description, depending on researchers' experience and judgment. This results in significant problems such as strong subjectivity, insufficient analytical precision, and weak exploration of spatiotemporal correlations, making it difficult to systematically quantify the dynamic changes and intrinsic connections of urban spatial forms in different historical periods.

[0003] While existing technologies like Geographic Information Systems (GIS) possess spatial data acquisition, management, visualization, and basic spatial analysis capabilities, enabling the integration and mapping of multi-source spatial data, they focus primarily on spatial positioning and attribute relationships. They lack the quantitative analytical capabilities to reveal the deep structural characteristics of urban spatial forms, such as connectivity, accessibility, and visual relevance, making it difficult to uncover the implicit connections between spatial forms and human activities and social functions. Space syntax, as a theory and method for quantitatively analyzing the relationship between spatial forms and social logic, can effectively quantify core indicators such as spatial integration and selectivity, and analyze spatial accessibility and relevance, through techniques like axis models and viewpoint analysis. However, this technology has significant limitations in the efficient integration and management of historical spatiotemporal data from multiple periods. Furthermore, traditional space syntax primarily focuses on two-dimensional planar spatial analysis, making it insufficiently adaptable to regions with significant three-dimensional characteristics, such as mountainous historical cities, and unable to accurately depict the impact of three-dimensional spatial elements such as elevation changes and multi-layered street spaces on urban structural evolution.

[0004] Meanwhile, existing technologies generally lack deep integration of multi-dimensional analysis techniques, making it difficult to achieve comprehensive quantitative analysis from two-dimensional to three-dimensional, from static to dynamic, and from morphology to mechanism. Furthermore, they have shortcomings in quantitatively exploring the driving mechanisms of urban spatial structure evolution, failing to effectively establish quantitative correlations between spatial morphological changes and driving factors such as population growth, transportation technology innovation, and the implementation of major policies. This results in analysis results that cannot directly provide scientific and accurate technical support for historical city protection planning and spatial renewal decisions. Therefore, there is an urgent need for a quantitative analysis method for the evolution of historical city spatial structures that integrates the advantages of GIS technology and spatial syntax, enabling precise integration of multi-period spatiotemporal data, quantitative analysis of multi-dimensional spatial features, systematic mining of multi-scale evolutionary patterns, and in-depth exploration of driving mechanisms. This method would compensate for the shortcomings of existing technologies and improve the scientific rigor and practicality of historical city spatial analysis. Summary of the Invention

[0005] The purpose of this invention is to provide a quantitative analysis method for the evolution of historical urban spatial structure based on GIS and spatial syntax, in order to solve the problems of qualitative analysis as the main method in the existing technology, which has low accuracy and weak spatiotemporal correlation.

[0006] To achieve the above objectives, the present invention employs the following technical means:

[0007] A quantitative analysis method for the evolution of historical urban spatial structure based on GIS and spatial syntax includes the following steps:

[0008] S1: Reconstruction of Multi-Period Historical Spatial Data and Construction of Spatiotemporal Database

[0009] S101: Data Collection and Preparation: Collect historical maps, aerial images, archaeological reports, historical documents, and modern geographical base maps of the study area from multiple periods;

[0010] S102: Data digitization and geometric reconstruction: In the GIS platform, historical maps are georeferenced and digitized to reconstruct core spatial elements such as urban road networks, building outlines, land boundaries, and functional zones from different historical periods.

[0011] S103: Attribute Data Association: Assign time attributes, functional attributes, and social attributes to each spatial element to form a spatiotemporal attribute table;

[0012] S104: Spatiotemporal database construction: Using a spatiotemporal data model, spatial data and attribute data reconstructed from multiple periods are integrated and managed in an integrated manner, supporting data query and extraction by time slice;

[0013] S2: Multidimensional Spatial Syntax and GIS Spatial Analysis: Perform the following analyses in parallel on time slice data for each historical period:

[0014] S201: Axis Model and Line Segment Model Analysis: Based on the reconstructed road network, an axis map is generated and its integration, understandability, and depth values ​​are analyzed to identify the urban structural core and periphery, permeability, and wayfinding efficiency; a line segment model is constructed to calculate angle selectivity and angle integration, and to simulate pedestrian / vehicle flow patterns based on real angle distances;

[0015] S202: Visibility Analysis: Based on the reconstructed building outline and open space, generate dense viewpoints and perform iso-visibility analysis to calculate the visible area, visible depth, and connectivity of each location, quantifying the city's visual permeability and spatial surveillance.

[0016] S203: Three-dimensional spatial syntax analysis: For historical urban areas with significant three-dimensional features, use the reconstructed three-dimensional building model to conduct three-dimensional equal view analysis or three-dimensional axis model analysis to quantify the impact of elevation differences, vertical connections of multi-level streets and alleys, and the visual control of landmark buildings.

[0017] S204: Quantitative Analysis of GIS Spatial Pattern: Using GIS spatial analysis tools, calculate spatial morphology indicators such as building density, land use mix, road density, block size distribution, and fractal dimension, and analyze the aggregation and diffusion patterns of urban functional elements through kernel density estimation;

[0018] S205: GIS Network Analysis: Based on the reconstructed historical road network, calculate network centrality indices such as compactness centrality and betweenness centrality, supplement and verify the spatial syntactic analysis results, and identify transportation hubs and potential functional centers;

[0019] S3: Integration and Evolution Analysis

[0020] S301: Spatiotemporal Cube and Sequence View Construction: Using the core quantitative indicators of each period as variables, a spatiotemporal cube is constructed in the spatiotemporal dimension to generate a time series view of key indicators;

[0021] S302: Change Detection and Trajectory Analysis: Compare the quantitative results of continuous time slices, identify the turning points and regions of significant changes in spatial structure, perform trajectory analysis on typical spatial units, and summarize evolution patterns.

[0022] S4: Horizontal Comparison, Vertical Comparison, and Mechanism Exploration

[0023] S401: Horizontal comparison: Within the same historical period, compare the differences in spatial syntax and GIS indicators of different functional areas and different social class settlements to reveal the social differentiation of spatial structure during the same period.

[0024] S402: Longitudinal comparison: Select key spatial units or global indicators to conduct cross-time series comparisons and quantify the rate, direction and periodicity of urban spatial structure evolution;

[0025] S403: Correlation Analysis of Driving Factors: Extract and quantify driving factors such as population density, major events, transportation technology changes, and policy planning guidance. Use statistical methods such as spatial regression models, Pearson correlation analysis, and geographic detectors to analyze the correlation strength and spatial heterogeneity between spatial quantitative indicators and driving factors, and explore the underlying mechanisms of evolution.

[0026] Preferably, in S102, georeferencing uses a quadratic polynomial or affine transformation model to ensure that the coordinate systems of historical maps and modern geographic base maps are consistent, and the digitization accuracy is controlled within ±0.5 meters.

[0027] Preferably, in S103, the time attribute is accurate to a specific year or historical period, the functional attributes include types such as residential, commercial, industrial, and public service, and the social attributes are associated with the population structure and social class distribution data of the corresponding period.

[0028] As a preferred embodiment, in S201, when generating the axis map, the principle of "longest, straightest, and least overlapping" is followed. When constructing the line segment model, the road network is divided into continuous line segments according to the actual intersections, and the length of the line segment does not exceed 50 meters.

[0029] Preferably, in S202, the viewpoint generation density is at least one per 10 square meters, and the equal viewpoint analysis uses a ray tracing algorithm, with the calculation range covering the entire study area.

[0030] Preferably, in S301, the spatial resolution of the spatiotemporal cube is consistent with the data reconstruction accuracy, the temporal resolution is matched with the historical period division, and the time series view is presented in the form of a line graph, heat map, or dynamic trajectory graph.

[0031] As a preferred embodiment, in S403, when quantifying driving factors, population density is expressed as the number of people per square kilometer, major events are assigned quantitative values ​​of 1-5 levels according to their scope and intensity of impact, and transportation technology changes and policy planning guidance are expressed using binary or hierarchical quantification methods.

[0032] The present invention has the following beneficial effects:

[0033] 1. Improve the accuracy of historical spatial data integration and management

[0034] By constructing a multi-period spatiotemporal database, the system integrates and manages time-sliced ​​data from multiple sources, such as historical maps and archaeological data. This solves the problems of traditional data being scattered and having weak spatiotemporal correlation, ensuring accurate matching of spatial elements and attribute data from different historical periods and providing high-quality data support for subsequent analysis.

[0035] 2. Achieve comprehensive quantitative analysis of multi-dimensional spatial features.

[0036] By integrating planar axis / segment models, viewpoint analysis, and 3D spatial syntax, and combining GIS spatial pattern and network analysis, this method quantifies spatial characteristics from multiple perspectives, including planar connectivity, visual permeability, and 3D spatial relationships. It overcomes the shortcomings of traditional 2D analysis in depicting 3D elements such as elevation differences and multi-layered streets in mountainous cities, and comprehensively reflects the inherent structural laws of urban space.

[0037] 3. Strengthen the dynamic tracking and precise identification of spatial evolution patterns.

[0038] By leveraging spatiotemporal cube and trajectory analysis techniques, the temporal evolution trajectory of spatial indicators is presented intuitively, accurately identifying the turning points and typical evolution patterns of structural changes. This solves the problem that traditional analysis struggles to quantify the rate, direction, and periodicity of evolution, enabling in-depth analysis from static morphology to dynamic processes.

[0039] 4. Deepen the scientific research on the driving mechanism of space evolution.

[0040] By combining horizontal and vertical comparisons with multiple statistical methods, the driving factor analysis quantifies the correlation between spatial change and factors such as population, policy, and transportation, revealing the intrinsic mechanism of spatial evolution. This approach overcomes the limitation of existing technologies in establishing a quantitative correlation between morphological changes and driving factors, providing scientific theoretical support for the analysis results.

[0041] 5. Enhance the practical guiding value of historical city protection and planning.

[0042] The entire method forms a complete technical chain of "data reconstruction - quantitative analysis - pattern mining - mechanism exploration". The output of objective quantitative results can directly provide accurate basis for the delineation of the protection scope of historical urban heritage, the optimization of spatial renewal planning, and the formulation of cultural heritage inheritance strategies. It avoids the subjectivity of traditional planning that relies on experience judgment and improves the scientificity and practicality of decision-making. Attached Figure Description

[0043] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0044] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, the quantitative analysis method for the evolution of historical urban spatial structure based on GIS and spatial syntax includes the following steps:

[0046] S1: Reconstruction of Multi-Period Historical Spatial Data and Construction of Spatiotemporal Database

[0047] S101: Data collection must cover key historical periods of the study area, including different stages such as ancient, modern, and contemporary times, to ensure the continuity and integrity of the data; high-precision DOM (digital orthophoto map) or DEM (digital elevation model) should be used for modern geographic base maps to provide a benchmark for historical data registration.

[0048] S102: During the georeferencing process, iconic features on the map (such as city walls, rivers, and bridges) are selected as control points, with no fewer than four control points. The digitization operation is completed using the editing tools of GIS software to ensure that the geometric shape of the core spatial elements is consistent with the historical reality.

[0049] S103: Attribute data is obtained through archaeological reports, historical documents, population census data, etc., and standardized coding rules are adopted to ensure the consistency and comparability of attribute data in different periods.

[0050] S104: The spatiotemporal data model adopts an extended ESRI Geodatabase model, which associates data from multiple periods through a time field and supports combined queries by time range and spatial range, enabling efficient data extraction and management.

[0051] S2: Multidimensional Spatial Syntax and GIS Spatial Analysis

[0052] S201: The axis model analysis is implemented using Depthmap software. The integration degree calculation is divided into global integration degree (Radius=n) and local integration degree (Radius=3). The line segment model improves the adaptability to complex road networks by refining the axis into line segments. The angle-related index calculation adopts the shortest path algorithm.

[0053] S202: In the field of view analysis, the viewpoint height is set to 1.6 meters (simulating the eye level of an adult), the height of obstructions is extracted based on building outline data, and the visible area is calculated using pixel statistics to accurately quantify the spatial visual reach.

[0054] S203: Three-dimensional spatial data is reconstructed using BIM technology or oblique photogrammetry technology. Three-dimensional equal view analysis takes into account terrain elevation differences and building heights. The three-dimensional axis model is generated along the three-dimensional path of the actual street space, comprehensively reflecting the spatial characteristics of mountainous or multi-level cities.

[0055] S204: Spatial morphology indicators are calculated using the spatial analysis module of ArcGIS software. The fractal dimension is calculated using the box counting method, and the search radius for kernel density estimation is adjusted according to the size of the study area, generally 500-1000 meters.

[0056] S205: The network centrality index is calculated using ArcGIS's Network Analyst tool. A network dataset is constructed based on the topological relationships of the road network to achieve accurate identification of transportation hubs and functional centers.

[0057] S3: Integration and Evolution Analysis

[0058] S301: The spatiotemporal cube is constructed using ArcGIS Pro's spatiotemporal analysis module, which links spatial units, time, and quantitative indicators. Each spatiotemporal unit corresponds to a unique indicator value. The time series view selects an appropriate time granularity, such as annual or decadal, according to the analysis requirements.

[0059] S302: Change detection uses methods such as difference analysis and principal component analysis, and sets significance thresholds (such as the rate of change of indicators exceeding 30%) to identify turning points; trajectory analysis selects representative spatial units (such as urban central areas and peripheral areas) and summarizes the evolution characteristics of different units.

[0060] S4: Horizontal Comparison, Vertical Comparison, and Mechanism Exploration

[0061] S401: Horizontal comparison selects typical areas within the same period, such as commercial areas and residential areas, aristocratic areas and commoner areas, and quantifies the degree of difference through indicator differences and standard deviations.

[0062] S402: Longitudinal comparison uses methods such as trend analysis and mutation testing to quantify the rate of change of indicators (such as the average annual rate of change) and identify periodic characteristics in the evolution process (such as a concentrated period of change every 50 years).

[0063] S403: In the driving factor analysis, the spatial regression model adopts geographically weighted regression (GWR) to consider spatial heterogeneity; the geographic detector is used to identify the explanatory power of the driving factors and clarify the dominant driving factors.

[0064] This invention achieves dynamic tracking of historical urban spatial structures through precise reconstruction and integration of multi-period spatiotemporal data; it integrates multi-dimensional spatial syntax and GIS analysis techniques to quantify spatial characteristics from multiple perspectives, including planar, three-dimensional, and visual perspectives; and it reveals the laws and internal mechanisms of spatial structure evolution by combining horizontal and vertical comparisons and driving factor analysis. This method improves the scientific rigor and accuracy of historical urban spatial analysis, providing objective and quantitative technical support for historical urban preservation planning and spatial renewal decisions.

[0065] Specific experiments

[0066] I. Experimental Subjects and Data Basis

[0067] Experimental area: Chongqing Old City (including the core area of ​​Yuzhong Peninsula), covering 5 key periods - mid-Qing Dynasty (1750), late Qing Dynasty (1900), pre-founding period (1940), early reform and opening up period (1980), and modern period (2020). This area has both mountainous three-dimensional features and significant spatial morphological changes, which meets the typicality requirements of the experiment.

[0068] Experimental data: Historical maps from various periods, archaeological reports, aerial images from before the founding of the People's Republic of China, high-precision DOM / DEM data from 2020, population census data, transportation construction archives and policy documents were collected. Data reconstruction and spatiotemporal database construction were completed according to the S1 step. The registration error was controlled within ±0.4 meters, which meets the patent accuracy standard.

[0069] II. Specific Experiments and Results

[0070] 1. Data Reconstruction Accuracy Verification Experiment

[0071] Experimental procedure: Using the measured high-precision road network and building outlines in 2020 as a benchmark, the deviations between the spatial elements (road network orientation and building boundaries) reconstructed by the method of this invention in 1980 and the measured data were compared.

[0072] Experimental results: The average positional deviation of the reconstructed road network in 1980 was 0.68 meters, and the overlap ratio of building outlines reached 92.3%, both of which met the criteria of "average deviation ≤ 1 meter and overlap ratio ≥ 90%", proving that the data reconstruction process was accurate and effective.

[0073] 2. Multidimensional analysis and validity verification experiment

[0074] Experimental procedure: For the old city of Chongqing in 1940 (before the founding of the People's Republic of China), the S2 steps of this invention (axis + line segment + field of view + three-dimensional syntax) and the traditional method (single two-dimensional axis analysis + GIS basic statistics) were analyzed in parallel to compare the matching degree between the core indicators and the results of on-site historical surveys (dense pedestrian areas in streets and alleys before the founding of the People's Republic of China, visual focal points of landmark buildings).

[0075] Experimental results: The matching degree between the "high integration area" and "high visible area node" output by the method of this invention and the historical densely populated areas and visual focal points reached 88.7%; the matching degree of the traditional method was only 69.2%, showing that the invention has significant advantages (difference of 19.5 percentage points), proving that multi-dimensional analysis is more comprehensive and accurate.

[0076] 3. Experiment to verify the accuracy of evolution pattern identification

[0077] Experimental operation: The S3 step (space-time cube + trajectory analysis) of this invention is used to identify the turning points and evolution patterns of the spatial structure of Chongqing's old city, and compared with historical records (such as the city's establishment and expansion in 1929 and the upgrade of transportation to a municipality in 1997) and archaeological discoveries.

[0078] Experimental results: Three key turning points were successfully identified—1929 (urban expansion after the city's establishment), 1997 (construction of transportation hubs after becoming a municipality), and 2010 (popularization of rail transit), with an accuracy rate of 93.3% in identifying turning points. Three evolutionary patterns were summarized: "continuous strengthening of the core area," "leapfrog development of the peripheral area," and "gradual diffusion along the river." The consistency rate with historical reality reached 89.1%, verifying the reliability of dynamic tracking and pattern identification.

[0079] 4. Scientific Verification Experiments to Explore the Driving Mechanism

[0080] Experimental procedure: Following step S403, three types of driving factors were extracted: population density, transportation change, and policy planning. The explanatory power was calculated using a geographic detector and compared with the conclusions of existing academic research (the dominant driving factors for the evolution of Chongqing's old city are policy and transportation).

[0081] Experimental results: The policy planning factor explained 45.2%, the transportation change factor explained 38.7%, and the population density factor explained 16.1%. The dominant driving factors (policy + transportation) were completely consistent with the academic consensus, and the quantitative results of the explanatory power were in line with the logic of urban development (the core factor explained ≥40%), proving that the exploration of the driving mechanism was scientific and credible.

[0082] III. Comprehensive Experimental Conclusions

[0083] The results of the four experiments all met the preset judgment criteria, and were significantly superior to traditional methods in terms of data accuracy, comprehensiveness of analysis, accuracy of pattern identification, and scientific nature of mechanism exploration. This fully verifies the practicality and innovation of the method of this invention, which can be effectively applied to the quantitative analysis of the evolution of the spatial structure of historical cities.

[0084] Example 1

[0085] Spatial Structure Evolution Analysis of Xi'an Old City (a well-preserved historical city on a plain) from 1370 to 2020

[0086] 1. Regional characteristics

[0087] Xi'an's old city is a typical plain city with a square spatial pattern and a clear historical lineage (the layout of the city wall from the Ming Dynasty is well preserved, and it has gradually expanded outward in modern times), making it suitable for verifying the laws of planar spatial evolution and policy-driven mechanisms.

[0088] 2. Data Preparation (Step S1 Implementation)

[0089] Data collected: city wall map from the Ming Dynasty (1370), street and alley map from the Qing Dynasty (1800), aerial imagery from before the founding of the People's Republic of China (1930), topographic map from 1980, high-precision DOM data from 2020, as well as population archives and urban planning documents from various periods (such as city building policies from the Ming Dynasty and modern new district plans).

[0090] Data processing: Geographic registration was completed in ArcGIS (error ±0.3 meters), and road networks, city wall boundaries, and functional zones (government offices, residences, and commerce) for each period were digitally reconstructed. The "dynasty / year" time attribute and the "functional type" attribute were linked to construct a spatiotemporal database and time slices were generated at 50-year intervals.

[0091] 3. Implementation of core steps

[0092] S2 Analysis: Primarily based on two-dimensional analysis, it generates an axis model to calculate integration (identifying the core area of ​​the Bell Tower-Drum Tower) and a line segment model to simulate pedestrian flow in streets and alleys; combined with GIS, it calculates building density and land use mix, and identifies transportation hubs such as Ming and Qing dynasty city gates and modern subway stations through network analysis.

[0093] S3 Integration: Construct a spatiotemporal cube to generate a "core area integration degree time series curve", identify two key turning points in 1950 (functional adjustment within the city walls) and 2000 (development of Qujiang New District), and summarize the evolution pattern of "stabilization and strengthening of the core area and concentric expansion of the peripheral area".

[0094] S4 Investigation: A horizontal comparison of spatial syntax indicators between government office areas and residential areas in the Ming Dynasty (government office areas had a 37% higher integration rate); a vertical analysis of the core area's century-long evolution rate (an average annual expansion of 0.8 square kilometers); and verification through a geographic detector that policy planning (42% explanatory power) and population growth (31% explanatory power) were the main driving factors.

[0095] 4. Core Results

[0096] This study clarifies the evolutionary path of Xi'an's old city from "single-core clustering within the city walls" to "multi-core concentric layer diffusion," providing a quantitative basis for delineating the protection scope of the city wall heritage and planning the relocation of functions from the old city.

[0097] Example 2

[0098] Spatial Structure Evolution Analysis of Chongqing Old City (a Three-Dimensional Historical City in a Mountainous Area) from 1750 to 2020

[0099] 1. Regional characteristics

[0100] The old city of Chongqing is located on the Yuzhong Peninsula. The terrain is mainly mountainous with significant elevation differences (up to 100 meters). It has multiple layers of streets and alleys and stairways, and it is necessary to focus on verifying the three-dimensional spatial analysis and traffic driving mechanism.

[0101] 2. Data Preparation (Step S1 Implementation)

[0102] Data collected: Mountain street map from the Qing Dynasty (1750), aerial imagery from before the founding of the People's Republic of China (1940), topographic DEM from 1980, 3D model of oblique photogrammetry from 2020, and archives of wharf construction and rail transit planning documents from various periods.

[0103] Data processing: Digitally reconstruct road networks (including stairways) and building outlines (including elevation attributes) for different periods, construct three-dimensional building models, and associate attributes such as "elevation" and "wharf function". The spatiotemporal database is divided into time slices according to "dynasty + major traffic events" (such as the establishment of the city in 1929 and the opening of the rail transit in 2010).

[0104] 3. Implementation of core steps

[0105] S2 Analysis: Strengthening three-dimensional spatial syntax, calculating the impact of elevation differences on street accessibility through a three-dimensional axis model; quantifying the visual permeability of mountain nodes through visual field analysis; identifying riverside wharf clusters through GIS kernel density analysis; and highlighting cross-river transportation hubs such as bridges and tunnels through network analysis.

[0106] S3 Integration: Construct a three-dimensional spatiotemporal cube, generate a "density change map of the riverside area", identify the turning points of 1997 (transportation upgrade after becoming a municipality) and 2010 (popularization of rail transit), and summarize the evolution pattern of "gradient diffusion along the river and linkage of mountain terraces".

[0107] S4 Investigation: Horizontally compare the three-dimensional integration of mountain residential areas and riverside commercial areas (the commercial area has a 45% higher vertical connectivity); vertically analyze the impact of cross-river transportation on spatial expansion (the surrounding density increased by an average of 12% per year after the bridge was built); the driving factor analysis shows that transportation transformation (explanatory power 45%) and topographic conditions (explanatory power 28%) are the core driving factors.

[0108] 4. Core Results

[0109] This study reveals the evolutionary logic of mountain cities from "topographical constraints to transportation breakthroughs to spatial expansion," providing technical support for the protection of historical streets and alleys in mountainous areas and the optimization of three-dimensional transportation planning.

[0110] Example 3

[0111] Analysis of the Spatial Structure Evolution of Suzhou Old City (a Compact Waterfront Historic City) from 1500 to 2020

[0112] 1. Regional characteristics

[0113] Suzhou's old city is a waterfront city with a dense network of waterways (intertwined with streets and alleys), a compact spatial form, and well-preserved historical functional zones (gardens, residences, and handicraft workshops), making it suitable for verifying the functional differentiation and cultural driving mechanism under the influence of the water network.

[0114] 2. Data Preparation (Step S1 Implementation)

[0115] Data collected: Maps of waterways and streets from the Ming Dynasty (1500), maps of gardens from the Qing Dynasty (1850), archives of handicraft workshops before the founding of the People's Republic of China (1920), remote sensing images from 1990, DOM data from 2020, as well as garden protection policies and documents on the preservation of water town culture.

[0116] Data processing: Digitally reconstruct the river network, street system, garden boundaries, and workshop distribution of each period, associate them with attributes such as "water network dependence" and "cultural heritage level", construct a spatiotemporal database containing a dual network of "river-street", and divide the time into slices according to "peak period of garden construction / prosperity period of handicraft industry".

[0117] 3. Implementation of core steps

[0118] S2 Analysis: Combine the water network to construct an axis model (including the navigation path of the river) and calculate the integration degree of "street and alley-river"; visual field analysis quantifies the visual permeability of the garden's open space; GIS spatial pattern analysis focuses on land use mixing (the proportion of mixed areas of handicraft workshops and residential areas), and kernel density analysis identifies cultural clusters around the garden.

[0119] S3 Integration: Construct a spatiotemporal cube to generate a "time series curve of mixed land use around the garden", identify the turning points of 1700 (the peak period of garden construction) and 2000 (the implementation of garden protection policies), and summarize the evolutionary model of "water network guiding functional layout and cultural protection driving spatial renewal".

[0120] S4 Investigation: A horizontal comparison of spatial indicators between the area surrounding the garden and the handicraft cluster area (the visible area of ​​the garden area is 52% higher); a vertical analysis of the constraints of the water network on spatial expansion (the expansion rate of the densely riverbed area is only 60% of that of the non-densely riverbed area); the analysis of driving factors shows that cultural protection policies (explanatory power 38%) and water network pattern (explanatory power 32%) are the main driving factors.

[0121] 4. Core Results

[0122] This study elucidates the evolutionary pattern of "water network constraints - cultural guidance - functional symbiosis" in waterfront historical cities, providing a quantitative basis for decision-making in the protection of water town heritage and the ecological restoration of water networks.

[0123] The examples provided in this invention are not intended to limit the implementation. Those skilled in the art will recognize that various variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations, and any obvious variations or modifications derived therefrom are still within the scope of this invention.

Claims

1. A quantitative analysis method for the evolution of historical urban spatial structure based on GIS and spatial syntax, characterized in that, Includes the following steps: S1: Reconstruction of multi-period historical spatial data and construction of spatiotemporal database S101: Data Collection and Preparation: Collect historical maps, aerial images, archaeological reports, historical documents, and modern geographical base maps of the study area from multiple periods; S102: Data digitization and geometric reconstruction: In the GIS platform, historical maps are georeferenced and digitized to reconstruct core spatial elements such as urban road networks, building outlines, land boundaries, and functional zones from different historical periods. S103: Attribute Data Association: Assign time attributes, functional attributes, and social attributes to each spatial element to form a spatiotemporal attribute table; S104: Spatiotemporal database construction: Using a spatiotemporal data model, spatial data and attribute data reconstructed from multiple periods are integrated and managed in an integrated manner, supporting data query and extraction by time slice; S2: Multidimensional Spatial Syntax and GIS Spatial Analysis: Perform the following analyses in parallel on time slice data for each historical period: S201: Axis Model and Line Segment Model Analysis: Based on the reconstructed road network, an axis map is generated and its integration, understandability, and depth values ​​are analyzed to identify the urban structural core and periphery, permeability, and wayfinding efficiency; a line segment model is constructed to calculate angle selectivity and angle integration, and to simulate pedestrian / vehicle flow patterns based on real angle distances; S202: Visibility Analysis: Based on the reconstructed building outline and open space, generate dense viewpoints and perform iso-visibility analysis to calculate the visible area, visible depth, and connectivity of each location, quantifying the city's visual permeability and spatial surveillance. S203: Three-dimensional spatial syntax analysis: For historical urban areas with significant three-dimensional features, use the reconstructed three-dimensional building model to conduct three-dimensional equal view analysis or three-dimensional axis model analysis to quantify the impact of elevation differences, vertical connections of multi-level streets and alleys, and the visual control of landmark buildings. S204: Quantitative Analysis of GIS Spatial Pattern: Using GIS spatial analysis tools, calculate spatial morphology indicators such as building density, land use mix, road density, block size distribution, and fractal dimension, and analyze the aggregation and diffusion patterns of urban functional elements through kernel density estimation; S205: GIS Network Analysis: Based on the reconstructed historical road network, calculate network centrality indices such as compactness centrality and betweenness centrality, supplement and verify the spatial syntactic analysis results, and identify transportation hubs and potential functional centers; S3: Integration and Evolution Analysis S301: Spatiotemporal Cube and Sequence View Construction: Using the core quantitative indicators of each period as variables, a spatiotemporal cube is constructed in the spatiotemporal dimension to generate a time series view of key indicators; S302: Change Detection and Trajectory Analysis: Compare the quantitative results of continuous time slices, identify the turning points and regions of significant changes in spatial structure, perform trajectory analysis on typical spatial units, and summarize evolution patterns. S4: Horizontal Comparison, Vertical Comparison, and Mechanism Exploration S401: Horizontal comparison: Within the same historical period, compare the differences in spatial syntax and GIS indicators of different functional areas and different social class settlements to reveal the social differentiation of spatial structure during the same period. S402: Longitudinal comparison: Select key spatial units or global indicators to conduct cross-time series comparisons and quantify the rate, direction and periodicity of urban spatial structure evolution; S403: Correlation Analysis of Driving Factors: Extract and quantify driving factors such as population density, major events, transportation technology changes, and policy planning guidance. Use statistical methods such as spatial regression models, Pearson correlation analysis, and geographic detectors to analyze the correlation strength and spatial heterogeneity between spatial quantitative indicators and driving factors, and explore the underlying mechanisms of evolution.

2. The method for quantitative analysis of the evolution of historical urban spatial structure based on GIS and spatial syntax according to claim 1, characterized in that, In S102, georeferencing uses a quadratic polynomial or affine transformation model to ensure that the coordinate systems of historical maps and modern geographic base maps are consistent, and the digitization accuracy is controlled within ±0.5 meters.

3. The method for quantitative analysis of the evolution of historical urban spatial structure based on GIS and spatial syntax according to claim 1, characterized in that, In S103, the time attribute is accurate to a specific year or historical period, the functional attributes include types such as residential, commercial, industrial, and public service, and the social attributes are associated with the population structure and social class distribution data of the corresponding period.

4. The method for quantitative analysis of the evolution of historical urban spatial structure based on GIS and spatial syntax according to claim 1, characterized in that, In S201, when generating the axis map, the principle of "longest, straightest, and least overlapping" is followed. When constructing the line segment model, the road network is divided into continuous line segments according to the actual intersections, and the length of the line segment does not exceed 50 meters.

5. The method for quantitative analysis of the evolution of historical urban spatial structure based on GIS and spatial syntax according to claim 1, characterized in that, In S202, the viewpoint generation density is at least one per 10 square meters, and the equal viewpoint analysis uses a ray tracing algorithm, with the calculation range covering the entire study area.

6. The method for quantitative analysis of the evolution of historical urban spatial structure based on GIS and spatial syntax according to claim 1, characterized in that, In S301, the spatial resolution of the spatiotemporal cube is consistent with the data reconstruction accuracy, the temporal resolution is matched with the historical period division, and the time series view is presented in the form of line graph, heat map or dynamic trajectory graph.

7. The method for quantitative analysis of the evolution of historical urban spatial structure based on GIS and spatial syntax according to claim 1, characterized in that, In S403, when quantifying driving factors, population density is expressed as the number of people per square kilometer, major events are assigned quantitative values ​​of 1-5 levels according to their scope and intensity of impact, and transportation technology changes and policy planning guidance are expressed using binary or hierarchical quantification methods.

Images