Bettering the Functionality of the Streets in the Historic Urban Areas: A Quantitative Method of Identification
Publicado en línea: 25 sept 2025
Recibido: 14 ene 2025
Aceptado: 19 abr 2025
DOI: https://doi.org/10.2478/amns-2025-1024
Palabras clave
© 2025 Yao Yifeng et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Since the reform and opening up, China has experienced the largest and fastest urbanization process in the history of the world, and the idea of outward expansion of urban development has dominated for a long time, and the tilting of development resources to the new areas of the city has brought about a series of development problems such as lowering of the environmental quality of the old urban areas, congestion of traffic, aging of facilities, low vitality of the industry, and the demise of the culture, etc. [1-3]. Historic urban area is not only the area with the highest concentration of historical and cultural resources and the highest protection requirements, but also the area with a high concentration of modern residential functions and the urgent need to improve the living environment, and at the same time, it is also the core bearing place for the future development of culture, business and tourism industry focus, the built-up area of the city under the superposition of diversified development demands is overstretched in terms of space [4-6]. Particularly in the confined streets, it is more and more difficult to reconcile the competition and encroachment of different functions in the limited space [7-8].
In the past, academic research on street space enhancement was mostly based on a single perspective of vehicular traffic, slow-moving system, spatial quality, industrial function, etc., which is difficult to take into account the multiple demands and realize the overall optimization, and cannot fully reflect the new goals and requirements of the national urban development [9-12]. The old city is usually an important carrier of urban history and culture, representing the historical development of the current area for a long time, reflecting the historical features of the area, and is the concentration of urban vitality in the past. With the eastward shift of Qingdao’s economic center of gravity and the continuous emergence of various new commercial forms, the commercial space in the old city has shrunk or relocated to varying degrees, resulting in the continuous weakening of its development vitality [13-16]. In the current era, people’s cultural literacy continues to improve, the tourism industry is booming, and new commercial business forms rich in historical flavor are gradually generated in the old city, which is expected to become the main driving force for the revival of the vitality of the old city [17-18].
In recent years, with the rapid development of urban economy, the construction goal of urban development has shifted from spatial expansion to spatial optimization. And the current limited street transportation facilities in the old urban areas of Chinese cities have led to the widespread problems of street congestion and degradation of the quality of the walking environment in the old urban areas of Chinese cities, and the street space that citizens rely on in their public life has been seriously compressed [19-20]. However, the renovation and renewal of the street environment is practicable due to the relatively good location of old urban areas in the city and the relatively high land price, which leads to the excessive cost of development and demolition [21-22].
Therefore, the article is structured into four main parts as following: in Part 2, it reviews the concepts related to the functionality of streets and current analytical methods; in Part 3, it presents the conceptualization and development of the proposed method; in Part 4, the method is tested and applied on Shangqiu Ancient City in China; in Part 5, it engages in a discussion focusing on the comparison of the method with the other existent approaches; and finally, the conclusion section provides a brief summary of the research findings.
Based on the review in the former parts, the article conceptualizes and develops a method of identifying the functionality of streets in historic urban areas, utilizing the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) technology. The core idea to conceptualize the method to serves as a supplementary technical tool and a complementary approach to precisely understand and analyse the various functions performed by the streets in these historic urban areas and help to coordinate and integrate the determination of the functional roles during the conservation planning and policy making process. Four principal challenges must be overcome to construct the method as the general framework illustrates below in Figure 1, including step 1 finding out all potential functions attributed to the streets in historic urban areas and defining the indicators that represent each street function; step 2 defining and weighting the functional indicators using the Analytic Hierarchy Process, based on the established principles of the ranking of importance; step 3 creating a database and quantifying the functional indicators by using diverse data types on the GIS platform; and step 4 evaluating and identifying the functionality of the streets based on the quantitative analysis results.
The general framework of the method
Currently, there have been several research achievements in the evaluation of historical urban areas, primarily focusing on historic preservation, street traffic flow, and street vitality. For instance, scholars aiming to promote sustainable transportation have identified indicators such as traditional architecture, ancient landscapes, traffic flow, travel time, and adequate parking spaces. Furthermore, some researchers analyse the preservation of historical heritage and landscapes through selected indicators, including the continuity of the overall landscape, the environmental comfort, the utilization of architectural heritage resources, the use of cultural heritage resources, entertainment facilities, parking amenities, heritage preservation, and street design scale. Additionally, the reuse of historical resources and the objectives of tourism development is also included with some unique indicators to represent the tourist attraction density, environmental comfort, etc. Therefore, the indicators for representing the street functions could extensively include the population density, the density of bus stops, the overall accessibility, density of cultural tourism attractions, density of natural tourism attractions, the density of tourist hotels, and the density of functional points of interest (POIs), and among which the cultural heritage, traffic flow, parking facilities, commercial services are common indicators concerning the streets in the historic urban areas.
In order to truthfully represent and quantify the functions assumed by the streets in the historic urban areas as much as possible, the indicators defined for the method should represent three primary and principal functions: Protection, Accessibility, and Service as well as the availability and quantifiability of the data of diverse sources. Opinions are solicited from professionals and experts in urban planning, transportation planning, and the conservation of the historical and cultural heritage (see Table 1). For Protection, three indicators are defined: the density distribution of historic heritages along the streets, the surface areas of streets within the conservation buffer zone, and the preservation requirements for the street spaces, sections and streetscapes as defined by urban conservation policies, regulations, and plans. Regarding the Accessibility, four indicators are defined: the traffic flow of motor vehicles, the street accessibility, the connectivity to parking lot, entrances and exits, and the connectivity to external public transport hubs, such as bus stops and rail transit stations. And for Service, three indicators are identified: the distribution of the active population, the density of the service facilities for living, and the density of the service facilities for tourism.
The Indicators and their significance for the Principal Functions
| Function | Indicators | Significances |
|---|---|---|
| The distribution density of historical heritage | The quantity and density of historic buildings and other heritages along the Street. | |
| The coverage ratio of buffer zone area | Area of the street within the buffer zone area of the protection heritage. | |
| The protection requirements for historic city streets | According to the street protection guidelines, it is essential to strictly adhere to the requirements and avoid altering the street cross-section and facades. | |
| The motor vehicle traffic volume | Concentration of motor vehicle traffic volume. | |
| The street accessibility | The ease with which people can reach and navigate urban streets. | |
| The parking facility density | Density of facilities such as parking lots and parking spaces around the street. | |
| The accessibility to public transportation hubs on the periphery of the historic urban area | Accessibility to public transportation facilities such as nearby bus stops and metro stations from the historical urban area. | |
| The distribution of active population | Density of active population in the street. | |
| The density of living service facilities | The density of commercial, educational, medical, sports and other life service facilities along the street. | |
| The density of tourist service facilities | The number and density of tourist attractions, visitor centers, cultural and entertainment and other tourism service facilities around the street. |
The method uses the Analytic Hierarchy Process (AHP) to evaluate the weights of the indicators by two specific steps. First, a judgment matrix is established based on the significance ranking principles of urgency, efficiency, and centralization respectively for the indicators of each principal function, referencing existing research (see Table 2). The evaluation scale ranging from 1 to 9, along with its reciprocal values, is utilized to assess the importance of each factor (see Table 3). The importance ranking is determined by comparing the degree of importance between these indicators. Secondly, to weigh all the indicators as well as to verify the values of weighing, the weight values of the recognition indicators under each type of function are calculated by using the ensemble product method, and then the consistency test is carried out (Table 4). When the consistency index CI = 0, the judgement matrix has full consistency. If CR (consistency ratio) < 0.10, the matrix consistency is considered acceptable (Table 5). Completion of the calculation and validation process finally resulted in pairs.
The importance ranking principles for establishing the judgement matrix
| Urgency for the protection | According to the historical value of the protected elements, the size of the protection level, and so on, the distribution density of historical heritage is more important in this study. |
| Efficiency for the accessibility | Use shorter time and routes to arrive at destination, street accessibility is more important in this study. |
| Concentration for Service | The service facilities that more concentrated and continuously distributed in the street are more important. |
The proportion scales commonly used in Analytic Hierarchy Process (AHP)
| Conventional Scales | Definition | Explanation |
|---|---|---|
| 1 | Equally important | Two factors have the same importance |
| 3 | Moderately important | One factor is slightly more important than the other |
| 5 | Strongly important | One factor is obviously more important than the other |
| 7 | Very strongly important | One factor is strongly more important than the other |
| 9 | Extremely important | One factor is extremely more important than the other |
| 2, 4, 6, 8 | Intermediate values | Intermediate values of above adjacent comparisons |
Weight values of the functional indicators for the streets in historic urban areas
| The principal functions | Importance ranking principle | The indicators | Weight value |
|---|---|---|---|
| urgency | Distribution density of historical heritage | 0.528 | |
| Coverage Ratio of the Protection and Construction Control Zone | 0.332 | ||
| Protection requirements for historic city streets | 0.140 | ||
| efficiency | Motor vehicle traffic volume | 0.091 | |
| Street accessibility | 0.508 | ||
| Parking facility density | 0.135 | ||
| Accessibility to public transportation hubs on the periphery of the historic urban area | 0.266 | ||
| centralization | Distribution of active population | 0.143 | |
| Density of living service facilities | 0.286 | ||
| Density of tourist service facilities | 0.571 |
The judgment matrix of the identification indicators of the street functions in historic urban areas
| Table 5 (a) | |||
|---|---|---|---|
| Protection | Distribution density of historical heritage | Coverage ratio of the buffer zone areas | Protection requirements for historic city streets |
| Distribution density of historical heritage | 1 | 3 | 3 |
| Coverage Ratio of buffer zone areas | 1/3 | 1 | 2 |
| Protection requirements for historic city streets | 1/3 | 1/2 | 1 |
| Table 5 (b) | ||||
|---|---|---|---|---|
| Accessibility | Motor vehicle traffic volume | Street accessibility | Parking facility density | Accessibility to public transportation hubs on the periphery of the historic urban area |
| Motor vehicle traffic volume | 1 | 1/2 | 1/3 | 1/4 |
| Street accessibility | 2 | 1 | 1/2 | 1/5 |
| Parking facility density | 3 | 2 | 1 | 1/2 |
| Accessibility to public transportation hubs on the periphery of the historic urban area | 4 | 5 | 2 | 1 |
| Table 5 (c) | |||
|---|---|---|---|
| Service | Distribution of active population | Density of living service facilities | Density of tourist service facilities |
| Distribution of active population | 1 | 1/2 | 1/4 |
| Density of living service facilities | 2 | 1 | 1/2 |
| Density of tourist service facilities | 4 | 2 | 1 |
Annotation: The protection type: CI=0.026, CR=0.051<0.10, the judgment matrix has relatively satisfactory consistency; The accessibility type: CI=0.024, CR=0.027<0.10, the judgment matrix has relatively satisfactory consistency; The service type: CI=0.000, CR=0.000<0.10, the judgment matrix has relatively satisfactory consistency.
Utilizing the GIS platform, geographic information data is collected and imported into ArcGIS software. The coordinate system is then standardized, and a comprehensive database is constructed for the study. Preliminary analyses are conducted based on data attributes and analysis objectives, employing tools such as density analysis, buffer analysis, and network analysis. The analysis values corresponding to relevant streets are extracted (refer to Table 6). Subsequently, the natural breaks classification method is applied to categorize the processed data into five intervals, ranging from low to high, with scores assigned as 5, 4, 3, 2, and 1, respectively, using the reclassification tool.
Description of quantifying indicators data
| Indicator | Data type | Analytical tools | Description |
|---|---|---|---|
| Distribution of historical heritage .shp | Kernel density analysis | Calculate the density of historical heritage. The denser the streets, the more historical heritage there is, and the stronger the responsibility for conservation. | |
| Protection of construction control zones .shp | Buffer analysis | Conduct a buffer zone analysis for historical heritage that needs strict protection, and calculate the proportion of adjacent streets covered by the protected construction control zone. The larger the proportion of streets, the stronger the responsibility to protect. | |
| Historic City Street Preservation Plan.jpg | Statistics | According to the requirements for the protection of streets in historic cities, 5, 4, 3, 2, and 1 are assigned to the protected historic streets, general historic streets, traditional streets, general streets, and other streets, so as to distinguish the importance of street protection. | |
| Statistical table of field surveys | Site surveys | According to the survey data of motor vehicle traffic flow in road sections, the larger the traffic of the street, the stronger the traffic responsibility. | |
| Streets in historic urban areas .shp | Network analyst | Through the network analysis tool, the accessibility analysis method based on least impedance proposed is used to calculate the overall accessibility of streets in historic urban areas without considering the purpose of travel. | |
| Entrance and exit of the parking lot .shp | Kernel density analysis | Calculate the density of parking facilities, and in areas with higher density, the more vehicles travel and the stronger the traffic responsibilities. | |
| Bus transit facilities .shp | Network analyst | Collect public transit facilities and road vector data. Calculate the accessibility of public transport hubs such as bus stops and subway stations at any point in the historic city area through network analysis tools. | |
| Daytime population distribution .shp | Kernel density analysis | Calculate the density of the distribution of people during the day, and the higher the density area, the more concentrated the crowd activity. | |
| Living service facilities .shp | Kernel density analysis | Calculate the density of service facilities such as commerce, education, healthcare, sports, and more. In areas with higher density, the more concentrated the living service facilities. | |
| Tourist service facilities.shp | Kernel density analysis | Calculate the density of tourist service facilities such as scenic spots and visitor centers. In areas with higher density, the more concentrated tourism service facilities are. |
The method uses the weighted overlay tool to derive the initial results regarding the value and layouts of various functions, such as the evaluation of the protection function (see Table 7). By comparing the scores of each function, we identify the function with the highest score as representative of single-function dominated streets (refer to Table 8). Subsequently, we calculate the average difference between the scores of the two functions to establish an upper limit for the threshold, which helps determine whether both functions are prominent on the street. Finally, we present our third finding: the layout of multiple functional co-existence streets.
Example of weighted evaluation of protection functions
| 1 | import arcpy | Data enter |
| 2 | ||
| 3 | # Set the workspace | |
| 4 | arcpy.env.workspace = r”D:\your_workspace” | |
| 5 | ||
| 6 | # Input raster layers representing the indicators | |
| 7 | raster_layers = [”Distribution density of historical heritage “, “Protect the coverage of construction control zones “, “Preservation requirements for historic cities “] | |
| 8 | ||
| 9 | # Set the raster weights | Weighting operations |
| 10 | weights = [0.528, 0.332, 0.140] | |
| 11 | ||
| 12 | # Create the output raster | |
| 13 | output_raster = arcpy.sa.WeightedSum(raster_layers, weights) | |
| 14 | ||
| 15 | # Set the workspace | Result output |
| 16 | output_raster.save(“street_ protection function_evaluation.tif”) |
Dominant functional street types and identification
| 1 | # Prompt the user to input the evaluation scores | Data enter |
| 2 | protection_score = float(input(“Enter the protection function evaluation score: “)) | |
| 3 | accessibility_score = float(input(“Enter the accessibility function evaluation score: “)) | |
| 4 | service_score = float(input(“Enter the service function evaluation score: “)) | |
| 5 | ||
| 6 | # Determine the dominant functional type | Functional analysis |
| 7 | if protection_score >= accessibility_score and protection_score >= service_score: | |
| 8 | dominant_function = “Protection-Oriented” | |
| 9 | elif accessibility_score >= protection_score and accessibility_score >= service_score: | |
| 10 | dominant_function = “Accessibility-Oriented” | |
| 11 | else: | |
| 12 | dominant_function = “Service-Oriented” | |
| 13 | ||
| 14 | # Print the result | Result output |
| 15 | print(f”The dominant functional type of the street is: {dominant_function}”) |
Shangqiu was designated as a National Historic City by the State Council of China in 1986 (Figure 2-a). The history of Shangqiu dates back over 3,500 years, making it one of the origins of Chinese civilization. The historic urban area of Shangqiu, also known as Shangqiu Ancient City, exemplifies the typical characteristics of ancient Chinese cities for the overall layout, historical fabric, and chessboard-like street network (Figure 2-b), along with the richness of the historical and cultural heritages (Figure 2-c). Although served as the city centre until ten years ago, the Ancient City still accommodate the local communities and their daily life (Figure 2-d) and meanwhile a variety of activities, such as tourism, culture, commerce, social interactions and etc, are frequently conducted by the people of the city and from other cities, particularly in the streets (Figure 2-e). However, besides the strict prohibition on the widening and altering for the protection of the streetscapes, the current Urban Conservation Plan, although adopted policies and regulations in different separated schemes, cannot still not ease the contest of the increasingly diversified and growing functions driven for the street spaces by the socio-economic and tourism development. Actually, this is a common challenge faced by historic urban areas in most other Chinese cities today.
The current situation of Shangqiu Ancient City
The method is applied to all 114 streets in Shangqiu Ancient City by using the open-source data (Table 9). The results could be divided into three parts. The first part presents the values and layouts of Protection, Accessibility, and Service for each street in Shangqiu Ancient City (Figure 3-5). It reveals the composition and intensity of the functions attributed to each street. By the first part, the streets dominated by a single function can be output (Figure 6), which includes 95 streets, accounting for 83.4% of the total. Specifically, the streets dominated by accessibility comprise 52 streets, representing 45.7%, followed by protection-dominant streets at 21.9% and service-dominant streets at 15.8%. However, the remaining 19 streets, which account for approximately 16.6%, exhibit coexisting functions without any single dominant function.
The sources of data used in this research
| Data | Source | Collection time | Data types | Instruction |
|---|---|---|---|---|
| Guihuayun sharing platform, the website: |
2024.04 | Vector | A coordinate system for georeferencing and unifying spatial data. | |
| OSM open-source map platform, the website: |
2024.04 | Vector | It is used to calculate street traffic indicators and accessibility indicators. | |
| Field observation and survey collection | 2023.10 | Vector | It is used to analyze motor vehicle traffic volume indicators. | |
| Shuweiguancha platform, the website: |
2024.04 | Vector | Used to calculate density indicators. | |
| Shuweiguancha platform, the website: |
2024.04 | Vector | It is used to calculate the distribution index of human density. | |
| Field observation and survey collection | 2022 | Tiff | It is used to calculate the index of the density of historical heritage and the control zone of protection and construction. | |
| Field observation and survey collection | 2022 | Tiff | Used to calculate street protection indicators. |
The intensity for the Protection function and its layout.
The intensity for the Accessibility function and its layout.
The intensity for the Service function and its layout.
The layout of single-function dominated streets and multiple functional streets in Shangqiu Ancient City
The values of two or three principal functions on single streets differ significantly less (see Figure 7 and Table 10). The average difference between the values of any two functions ranges from 0.9 to 1.0. In other words, when considering intensity, more than one principal function is nearly equally represented by any individual street. The method uses half of the average value, 0.5, as a threshold to categorize another group of streets with multiple co-existing functions. Consequently, these could can furtherly further classified into two categories: functional equivalent equivalents the comprehensive the streets classified as and protection equivalent equivalents account for and while those classified as and service functional equivalent equivalents 6.1%, for 6.1%. Additionally, comprehensive streets account up. Ultimately, this identification process, categories of the functionality and their layout distribution throughout whole entire could can obtained established.
The identification result of the street functions in Shangqiu Ancient City
The histogram of the difference in intensity
| Histogram of calculated values | |
With the application of the method, several new discoveries have emerged that fundamentally alter the understanding and recognition of the functionality of the streets in Shangqiu Ancient City. Instead of being regarded as having a single, exclusive function for the streets, the functions of the streets are discovered to be segmented and could be identified by the method. For instance, the Central Avenue in Shangqiu Ancient City could be divided into six distinct functional sections using this method, whereas most specialized schemes treat the entire Central Avenue as having the same function (see Figure 8). Therefore, the identification yields more accurate and reasonable results for each street, thereby aiding in planning decisions and policy formulation.
The functions identified for each section of the Central Avenue in Shangqiu Ancient City
Some of the streets dominated by a single function have the consistent functional definition in the sectoral schemes of the current Urban Conservation Plan, include 12 streets for Protection, 36 streets for Accessibility, and 9 streets for Service which are holding the respective shares of 13.2%, 22.8% and 17.6% of the amount of all the streets. However, more single functional dominant streets are founded by this method, including 13 streets for Protection,16 streets for Accessibility, and 9 streets for Service, which increase the total amount of each single functional dominant street respectively by 108%, 44.4%, and 100% (Table 11 & Table 12).
The identified streets of a single dominant function
| The streets identified by the Method | The streets coincident with their functional definition in the Urban Conservation Plan | The new identified Streets differentiated with their functional definition in the Urban Conservation Plan | |
|---|---|---|---|
| Name | The Total Amount | ||
| The conservation dominant | 25 | 12 | 13 |
| The accessibility dominant | 52 | 36 | 16 |
| The service dominant | 18 | 9 | 9 |
A comparison of the single functional dominant streets identified by the method with the Urban Conservation Plan of Shangqiu Ancient City
| The results obtained by the Method | The sectoral schemes of Urban Conservation Plan | Comparison by overlaying the results and the sectoral schemes |
|---|---|---|
Furthermore, 19 streets of the multiple functional co-existence are discovered, which account 16.6% of the whole amount of the streets. Actually, these streets were originally defined to assume different single functions in the specialized schemes of the current Urban Conservation Plan (Table 13). Therefore, the identification can avoid the over-assumption and competition of the functional definition for the same streets between the sectoral schemes of the current Urban Conservation Plan.
A comparison of the multiple functional co-existent streets identified by the method with the Urban Conservation Plan of Shangqiu Ancient City
| The results obtained by the Method | The sectoral schemes of Urban Conservation Plan | The streets coincident with their functional definition in Urban Conservation Plan |
|---|---|---|
| 2 | ||
| 17 | ||
| 8 |
Beyond the new findings, the method also demonstrates significant methodological advantages and could be a supplementary tool to enhance credibility and quality of urban conservation planning by rectifying the definition of the street functions always impacted by the other sectoral schemes and delivering a general self-consistent strategy for the streets and all the other concerned fields (Figure 9). The method offers a quantitative analytical approach by the utilizing the geographic information data and computational models to overcome the technical difficulties confronted by the current approach. For instance, the method establishes a series of indicators to “quantify” the preservation requirements of the buffer zone and the streets of the historic urban areas and can also guarantees the identification more precise, reliable and visually intuitive. Meanwhile, the method is not exclusive or fixed in a unique subject but extended to be adaptive to the potential functions performed in the streets by enlarging the filter of the targeted functions and their indicators.
The integration of the method into the current framework and process of Urban Conservation Planning
The quantitative method proposed in the article for identifying the street functionality in historic urban areas makes use of AHP and GIS technologies and allow for an inclusion of a greater variety of the street functions. The particular functions for each segment of the streets as well as the functional composition for the entire street network are represented in both numeric and graphical forms. The feasibility of the method along with some new findings is confirmed by applying it in Shangqiu Ancient City. The application of the method yields more accurate, reasonable, and dependable outcomes, which validates nearly half of the street functional definitions made by the current approach but more critically removes the inconsistencies among the sectoral schemes in urban conservation planning. Therefore, the method could serve as a complementary to the current approach adopted by Urban Conservation Planning. Actually, by expanding the indicators, the method can also be applied to the varied historic urban areas. Nonetheless, the method could be still improved, especially a more comprehensive spectrum of the functional indicators to represent all the potential activities assumed by the streets in the historic urban areas.
No potential conflict of interest was reported by the author(s).