Urban Spatial Patterns Research Articles

Quantifying urban expansion and its driving forces in Chengdu, western China

Understanding urban sprawl and its drivers is crucial for sustainable urban development. Most studies on Chinese urbanization have focused on coastal areas, paying little attention to urban centers in western China. This study examines urban expansion based on the Google Earth Engine (GEE), remotely sensed image, urban expansion model, and analysis of buffer and quadrant location in the Geographic Information System (GIS). Additionally, driving forces of urban expansion are examined based on the principle component analysis (PCA). Results indicate that urban land area increased more than 5.60 times, reaching 124,723 ha, an increase of over 400 % during 1990–2020. The urban expansion rate and intensity significantly increased and exhibited spatio-temporal heterogeneity. We identified that urban spatial expansion patterns changed from patch filling to patch border expansion, and urban expansion direction was mainly in the southern, northeastern, southwestern, and northwestern regions, extending along the traffic corridor, ring road, and adjacent cities. We suggest that economic development, population, and urbanization have become the driving factors of urban expansion. The GEE provides a new geographic processing algorithm based on massive image datasets, facilitating remote sensing processing. The results revealed that Chengdu is following trends witnessed in coastal cities of China; however, the significance of various drivers of urban expansion in these cities differs from that of the eastern cities. This study will help formulate policies for better urban land management and sustainable land development.

Assessing Land Cover Change Projections and Its Suitability with Urban Spatial Pattern Plan Using Google Earth Engine and Cellular Automata in the National Activity Centres on Sumatera Island

The national activity centres (Pusat Kegiatan Nasional – PKN) areas, especially in Sumatera Island, are closely related to the land cover change phenomenon. This study aimed to identify the land cover change projection and assess its suitability with the spatial pattern plan in the “Rencana Tata Ruang Wilayah” (RTRW) which focuses on the study area including Medan, Pekanbaru, and Padang. The integration of remote sensing data, geographic information systems (GIS), and spatial analysis techniques are used to identify and assess the patterns of land cover change over the specific period. The analysis of land cover change patterns is performed using Landsat-7 and Sentinel-2A imagery data which processed in Google Earth Engine (GEE). Furthermore, Cellular Automata and Artificial Neural Network (CA-ANN) modelling is used to identify land cover prediction using transition rules like main road as driving factor then followed by overlay analysis to assess its suitability with the spatial pattern plan. The result shows the different level of each land cover classification in sub-district level. The proportion of the land cover projection suitability in Medan at 2042 was 84,92%; in Pekanbaru at 2040 was only 58,51%; while in Padang at 2030 was 77%. This findings can be able to serve as a suggestion for stakeholders in formulating effective strategies for spatial planning evaluation and managing land cover uses, especially in several national activity centers in Sumatera.

Comprehensive strength evaluation system of commercial centres based on multi-source data: a case of Hefei central city

Urban commercial centres are the most concentrated areas of economic activity. Understanding the spatial distribution pattern and comprehensive strength of urban commercial centres is important to guide the reasonable graded allocation of urban commercial space, spatial structure optimization and sustainable development of the commercial economy. Herein, mobile phone signalling data are used to identify the functional connection between recreational and residential places and local spatial autocorrelation analysis is used to identify the 24 commercial centres in the central city of Hefei. The comprehensive strength evaluation system of commercial centres is constructed from their basic conditions and customer consumption behaviours, and their comprehensive strength indices are accordingly measured and graded. The spatial distribution characteristics of commercial centres at all levels are analysed, and optimisation suggestions are made for the whole area and region. The following conclusions are drawn from the results of this study. (1) Compared to traditional single-perspective evaluations, the developed comprehensive strength evaluation system considers supply and demand perspectives for commercial centres, providing a more holistic and accurate portrayal of the strengths of various centres within a region. (2) The current commercial centres are characterised by ‘large dispersion and small concentration’ in spatial and hierarchical distribution. (3) The commercial centres in Hefei have formed a relatively complete ‘first, second, and third level’ commercial centre system, with the first level as the core and relying on the urban road system to form a network spatial connection. (4) Most of the commercial centres are concentrated in the First Ring Road, Swan Lake in the Government Affairs District and Binhu Century Town Estate, while most areas north of the Second Ring Road and Binhu New District still lack large-scale commercial centres. This study provides a technical reference for analysing urban commercial spatial structure patterns and provides decision support for optimising the spatial layout of urban commercial service functions.

Intra-urban differences underlying leprosy spatial distribution in central Brazil: geospatial techniques as potential tools for surveillance.

This ecological study identified an aggregation of urban neighbourhoods spatial patterns in the cumulative new case detection rate (NCDR) of leprosy in the municipality of Rondonópolis, central Brazil, as well as intra-urban socioeconomic differences underlying this distribution. Scan statistics of all leprosy cases reported in the area from 2011 to 2017 were used to investigate spatial and spatiotemporal clusters of the disease at the neighbourhood level. The associations between the log of the smoothed NCDR and demographic, socioeconomic, and structural characteristics were explored by comparing multivariate models based on ordinary least squares (OLS) regression, spatial lag, spatial error, and geographically weighted regression (GWR). Leprosy cases were observed in 84.1% of the neighbourhoods of Rondonópolis, where 848 new cases of leprosy were reported corresponding to a cumulative NCDR of 57.9 cases/100,000 inhabitants. Spatial and spatiotemporal high-risk clusters were identified in western and northern neighbourhoods, whereas central and southern areas comprised low-risk areas. The GWR model was selected as the most appropriate modelling strategy (adjusted R²: 0.305; AIC: 242.85). By mapping the GWR coefficients, we identified that low literacy rate and low mean monthly nominal income per household were associated with a high NCDR of leprosy, especially in the neighbourhoods located within high-risk areas. In conclusion, leprosy presented a heterogeneous and peripheral spatial distribution at the neighbourhood level, which seems to have been shaped by intra-urban differences related to deprivation and poor living conditions. This information should be considered by decision-makers while implementing surveillance measures aimed at leprosy control.

Spatial Configuration of Urban Greenspace Affects Summer Air Temperature: Diurnal Variations and Scale Effects

Optimizing the spatial pattern (spatial compositive and spatial configuration) of urban greenspace can effectively alleviate the urban heat island effect. While the relationship between air temperature (AT) and spatial composition of urban greenspace has been widely studied, this study aimed to investigate the relationship between AT and spatial configuration of urban greenspace and its diurnal variations and scale effects. Based on hourly AT data from 36 meteorological stations in Changsha, China, and land cover data interpreted from the Gaofen 2 remote sensing images, this study first quantified spatial composition (i.e., percent of greenspace) and spatial configuration (i.e., average patch area, patch density, edge density, landscape shape index, and mean shape index) of urban greenspace at different scales (30 m to 2000 m buffer surrounding the air station), then Pearson correlations (between AT and each landscape metric) and partial Pearson correlations (between AT and spatial configuration metrics with percent of greenspace controlled) were analyzed. Multiple linear regression was applied to model the variation of AT using the landscape metrics as independent variables. Finally, the variance partitioning analysis was performed to investigate the relative importance of spatial composition and spatial configuration of urban greenspace to explain the variation of AT. The results showed that (1) the temperature range reached 1.73 °C during the day and 2.94 °C at night. Urban greenspace was fragmented especially at small scales. (2) The Pearson correlation between AT and percent of greenspace fluctuated with the increase of scale and was generally higher during the day than during the night. (3) The spatial pattern of urban greenspace explained as high as 55% of the AT variation, showing diurnal variations and scale effects (i.e., a maximum of 0.54 during the day at 50 m buffer and a maximum of 0.55 during the night at 400 m buffer). (4) A higher percent of greenspace, more aggregated greenspace patches, and simpler greenspace shapes can generate a stronger cooling effect. (5) The relative importance of spatial composition and spatial configuration of greenspace varied among spatial scales and showed diurnal variations. These results emphasize the scale effect as well as diurnal variation of the relationship between urban greenspace spatial pattern and AT. These findings provide theoretical guidance for urban greenspace planning and management to improve the urban thermal environment in rapidly developing subtropical cities such as Changsha, China.

Spatial Association Rules and Thermal Environment Differentiation Evaluation of Local Climate Zone and Urban Functional Zone

Urban heat islands (UHIs) caused by urbanization have become a major issue affecting the sustainable development of the ecological environment. The distribution of UHIs is mainly affected by the reflection and transmission of heat radiation caused by differences in urban spaces, and the anthropogenic heat emissions caused by social activities. At present, the research on the urban thermal environment involves two spatial classification systems: local climate zone (LCZ), based on urban morphology and spatial patterns; and urban functional zone (UFZ), based on socio-economic activities. It is not clear whether there are association rules between these two systems in different cities. Against this background, this study explores the association rules between the UFZ and LCZ classification systems using the selected Chinese cities in different regions as typical examples. Our results confirm that there are common association rules from UFZ to LCZ, as the form of buildings is greatly influenced by the types of functional areas in urban construction. Specifically, the medical zone corresponds to the compact mid-rise zone (LCZ2); the business service area and the office area also correspond to the compact zone (LCZ1-LCZ3); and the industrial area corresponds to the compact low-rise zone (LCZ3). These functional zones have the same association rules in different cities. The cross-regional mining of the relationship between different urban functional systems will help to coordinate different planning departments and carry out the integration of multiple spatial plans. Furthermore, we found that LCZ has a better differentiation effect on the surface temperature through our comparison research, which makes it more suitable as a reference for research on the thermal environment and the heat island effect.

Exploring and evaluating the spatial association between commercial and residential spaces using Baidu trajectory data

The spatial association between commercial and residential spaces strongly influences residents' daily lives, social equity, and economic performance. Based on Baidu trajectory dataset of Zhuhai and the dynamic consumption travel flow perspective, this study proposes a research framework to explore the spatial association patterns between commercial and residential spaces and to evaluate the commercial accessibility of different social groups. Findings indicate that the formation of spatial association communities is partially limited by natural barriers and administrative boundaries. The spatial association patterns follow a paradigm: a polycentric spatial association formed in the main urban area with medium travel distance, a monocentric spatial association formed in the suburbs with short travel distance, and a bicentric spatial association formed in the outer suburbs with long travel distance. For social justice in consumption, the mid-high-level income social group has the highest commercial accessibility, whereas the low-level income social group suffers from long consumption travel distance. Moreover, social disparities in commercial accessibility are also found among urban circles and spatial association patterns. This study has implications for governments in terms of designing policies to enhance commercial accessibility, mitigate social inequality in consumption, and achieve their goal of self-containment during urbanization.

Urban Spatial Patterns in Banda Aceh City Using the Shannon's Entrophy Model

The rapid development of the city of Banda Aceh occurred in general in the suburbs. The impact is the occurrence of propagation which is a complex urban phenomenon and difficult to measure. Policy makers need a simple method to control and evaluate urban spread. This study integrates the Shannon's Entropy model with remote sensing and GIS to analyze spreading patterns in each sub-district in Banda Aceh City based on two aspects of distance, namely the distance to the city center and the road network to describe urban spreading patterns. The application of the Shannon's Entropy method in Banda Aceh City in 2010-2020 can be seen in the Relative Entropy Matrix which shows that the linear propagation pattern (Quadrant C) is more dominant in Banda Raya District, Baiturrahman District, Kuta Raja District, Kuta Alam District, Lueng District Bata, and the District of Syiah Kuala. Whereas the spreading pattern of Meuraxa District and Jaya Baru District is in quadrant B, namely the elongated (Radial) type of propagation pattern, but in 2020 Jaya Baru District there has been a change in the propagation pattern, namely the linear propagation pattern. In the Ulee Kareng sub-district, there was a pattern of propagation changes in 2020 which was in quadrant D, namely the leapfrog development pattern from the previous year, namely 2010 and 2015 in quadrant C with a linear propagation pattern. The spreading pattern of Banda Aceh City tends to lead to the East. This is mostly caused by the shape of the land which is very suitable to be used as a built-up area

Exploring the correlation between waterbodies, green space morphology, and carbon dioxide concentration distributions in an urban waterfront green space: A simulation study based on the carbon cycle

Urban ecological spatial patterns affect the carbon sequestration capacity of green spaces. High-resolution carbon concentration data simulated by Envi-met4.4.5 were obtained in Nanyuan Riverside Green space in Huangpu District, Shanghai. And the correlation among green space morphology factors, microclimatic and bioclimatic factors were explored by Random Forest model. The results showed that the importance of the waterfront distance factor to the carbon sink impact reaches 28.88%, and gradually decreased with the change of distance from water from south to north. The importance of the arboreal area ratio and green biomass reached 15.79% and 12.65% respectively, which was much more important than non-green vegetation space proportion indices. The importance of Cohesion reflecting green patches connectivity was 10.33%. In terms of the internal influencing microclimate factors of green space carbon cycle, relative humidity, air temperature and wind speed, had a more significant influence, with the importance degree of influence being 33.50, 29.02 and 10.29%, respectively. Compared with the influence effect between the factors of the bioclimate, the index of photosynthetically active radiation was greater than the influence of the vapor flux. The study provides a reasonable explanation for the theoretical of coupling carbon sink enhancement of hydrological and green spatial factors.

Quantifying the relationship between urban blue-green landscape spatial pattern and carbon sequestration: A case study of Nanjing’s central city

Against the background of global warming, urban blue–green spaces (UBGS) are important carbon sequestration (CS) carriers that can reduce carbon emissions. However, previous studies on urban CS were mostly limited to urban green or blue spaces separately and lack a comprehensive consideration of the synergistic impact of UBGS on CS. In addition, research on the driving mechanism between urban blue–green spatial patterns (UBGSP) and CS has so far been insufficient. Therefore, in this study, we explored the correlation between UBGSP and CS, identified the affecting UBGSP factors, and proposed suggestions for planning design. We identified the UBGS of the central city of Nanjing based on Landsat-8, calculated the CS using the Carnegie-Ames-Stanford approach (CASA), and analyzed the relationship between the UBGSP and CS at both class and landscape levels by establishing sets of UBGSP indicators. At the class level, the percentage of landscape and aggregation index have positive effects on CS, whereas the landscape division and shape indexes have negative effects. Moreover, edge density and area-weighted patch fractal dimension do not correlate with CS. At the landscape level, connectance can promote the CS of UBGS. Finally, we found that the largest patch index, edge density, Shannon’s evenness index, patch cohesion index, and contagion have no significant effects. The results of this study contribute to the systematic optimization of UBGSPs, promote the integrated development of UBGSs, and provide references for urban planning practices and resource management.

Relationship Between Urban Spatial Pattern and Thermal Environment Response in Summer: A Case Study of Hefei City

With the continuous expansion of cities, the land cover type of the region is transformed, a large number of natural landscapes are replaced by man-made landscapes, and the environmental temperature rises. The study of the response relationship between urban spatial pattern and thermal environment provides some guidance for improving the ecological environment and optimizing the urban spatial layout. Based on the Landsat 8 series remote sensing image data of Hefei City in 2020 and analysis platforms such as ENVI and ARCGIS, Pearson correlation and profile lines were used to reflect the correlation between the two. Then, the three spatial pattern components with the greatest correlation were selected to construct multiple regression functions to investigate the influence of urban spatial pattern on urban thermal environment and its mechanism of action. The results showed that:① the high temperature area of Hefei City increased significantly with the advance of time during 2013-2020. For different seasons, the urban heat island effect showed that summer>autumn>spring>winter. ② In the central urban area, the building occupancy, building height, imperviousness occupancy, and population density were significantly higher than those in the suburbs, whereas fractional vegetation coverage presented a higher suburban than urban area and mainly showed a point distribution in the urban area and an irregular distribution of water bodies. ③ The urban high-temperature zone was mainly distributed in various development zones in urban areas, whereas other places in urban areas were dominated by medium-high temperature and above-temperature zoning, and suburban areas were dominated by medium-low temperature. ④ The Pearson coefficients between the spatial pattern of each element and the thermal environment were positively correlated with the building occupancy (0.395), impervious surface occupancy (0.333), population density (0.481), and building height (0.188) and negatively correlated with fractional vegetation coverage (-0.577) and water occupancy (-0.384). The coefficients of the constructed multiple regression functions, including building occupancy, population density, and fractional vegetation coverage, were 8.372, 0.295, and -5.639 respectively, with a constant of 38.555. The results of this study can provide a reference basis for optimizing urban spatial layouts and improving urban living quality.

URBAN DEVELOPMENT AND SUSTAINABILITY MILIEU: A CASE STUDY OF FAISALABAD CITY, PAKISTAN

This research aimed to analyze the spatiotemporal changes in urban development pattern and to discuss the spatial urban pattern in the context of sustainability. For this purpose, geospatial techniques were used to analyze the spatial urban development pattern of study area. The existing development pattern in Faisalabad city is linear sprawl at outer edges of the city along major roads and infill development pattern in the center of the city that has been observed very closely in this research and results also revealed this pattern is likely to sustainable. However, the major finding reflects that Faisalabad city is experiencing phenomenal physical development pattern rather than pre- planned development pattern. So, there is an urgent need of urban lens to facilitate such delineations. The broader purpose of this research work was to conceptualize the urban sustainability for better development and planning of Faisalabad city.

Flow space reveals the urban network structure and development mode of cities in Liaoning, China

As an important supplement to “location space”, “flow space” has become an important trend in urban network research. At present, the theoretical basis and empirical research of urban network structure from the perspective of “flow space” are not completely clear. Based on the urban spatial network structure research system framework, this paper comprehensively studies the urban spatial structure, spatial network pattern and development mode of Liaoning Province in China by using multivariate data flow. The results show that: (1) Cities at all levels in Liaoning Province have different functions and roles in the urban network level, with Shenyang and Dalian as the leading cities, and Anshan and Jinzhou as “transit hubs”; (2) Liaoning Province takes Shenyang-Dalian as its main axis, the Shenyang economic circle with Shenyang as the core and the coastal economic belt with Dalian as the core and Dandong and Yingkou as the two wings intersect to form the urban spatial network pattern; (3) In the future, Liaoning Province should further form the urban development model of “core city-axis-cluster”. Under the trend of globalization, it should carry out in-depth promotion in combination with “Shenyang Modern Metropolitan Circle” “Liaoning Coastal Economic Belt” and “Liaoning Northwest Strategy”, and finally form a balanced, efficient and comprehensive urban network structure.

Assessing urban spatial patterns within the implemented urban planned areas using GIS and remote sensing data

In the developing world, including Sub-Saharan Africa, reports have indicated that urban planning as a strategic instrument has not been able to guide urban spatial patterns and this poses challenges to improving urban environmental sustainability. The current study combined a city land use plan, Geographical Information Systems, and remotely sensed data to evaluate urban spatial patterns within areas where the city plan is reported to have been implemented to support strategic actions for urban environmental sustainability. Focusing on Abuja city, Nigeria, the study deployed a supervised classification on Landsat 8 remotely sensed data to analyze spatial patterns of urban land cover types, computed transition change detections and compared the urban impervious surface to the area of land designated for urban development by the city plan. Key findings indicated that the land areas designated for urban development have not been fully developed and the transition from urban green space is the highest transition from other land cover types to urban impervious surface. The baseline information provided in this study is crucial to inform decision-makers on improving and maintaining the implementation of strategic actions for urban environmental sustainability in Sub-Saharan African cities and other parts of the developing world.

Influence of urban green space landscape pattern on river water quality in a highly urbanized river network of Hangzhou city

Optimizing urban green space landscape patterns is a key goal for improving river water quality. However, little is known about the spatial heterogeneity of the impact of urban green spatial patterns on river-water environments. This study investigated the influence of urban green space patterns on the spatiotemporal heterogeneity of river water quality in the Hangzhou section of the Beijing-Hangzhou Canal through exploratory regression analysis and a combination of geographically weighted regression analysis (GWR) and spatial interpolation. The results show that (1) total nitrogen (TN) and nitrate nitrogen (NO3–-N) are the leading indicators of river pollution in the study area, (2) green space configuration is more crucial for improving water quality than composition, and (3) GWR can effectively explain the impact of urban green space on river water quality. For example, landscape shape and edge indices have a great impact on ammonia nitrogen (NH4+-N), TN and total phosphorus (TP); the more complex the shape and edge of the green space, the more beneficial it is for water purification. The interpretation of NO3–-N is complex and mainly influenced by the largest patch index (LPI) and landscape composition. Given the limited land availability in urban area, the spatial configuration of urban green space should be optimized without additional land use to minimize the non-point source (NPS) pollution with the smallest possible green space area. The proposed approach provides a new understanding of the interaction between spatial patterns of green space and the urban water environment, and valuable information for developing green space planning policies for local sites.

Analysis of Urban Ecological Quality Spatial Patterns and Influencing Factors Based on Remote Sensing Ecological Indices and Multi-Scale Geographically Weighted Regression

With the acceleration of urbanization, problems such as urban ecological environment quality have become increasingly prominent. How to scientifically analyze and evaluate the spatial pattern of urban ecological environment changes and influential variables is a prerequisite for achieving green development and ecological priority new in urban planning. Our study was conducted on Pingtan Island, located in Fujian Province, China. First, we selected Landsat 8 OLI images in 2013, 2017, and 2021. Second, we extracted the remote sensing ecological index (RSEI) from these images and created RSEI maps to assess the spatial-temporal variations and spatial autocorrelation of the ecological environment condition in Pingtan Island. Third, the proportion of land-use types, road, and population density were selected as independent variable factors, RSEI as the dependent variable, least squares regression (OLS), geographically weighted regression (GWR), and multi-scale geographically weighted regression (MGWR) were used to establish global and local regression models. According to the regression coefficients of the model and its spatial distribution, the spatial heterogeneity between the ecological environment and the influencing factors was assessed. The results indicated that: (1) the mean value of the RSEI increased from 0.422 to 0.504 during 2013–2021, indicating that the overall ecological environment improved. (2) Based on the global Moran’s I value, the distribution of ecological environment quality was positively correlated. The local Moran’s I cluster map showed that the high-high cluster gradually extended to the northwest high-altitude region. Low-low clustering gradually extended to the more populous areas in the southeast. (3) The Radj2 of the MGWR model was 0.866, which was better than the results of the OLS model and GWR model, indicating that MGWR had obvious advantages in revealing the spatial heterogeneity between the ecological environment and the influencing factors. Importantly, the results indicate that population density, road density, and the proportion of cropland land and impervious surface in land-use types have varying degrees of negative effects on the urban ecological environment, with the impervious surface being more severe, followed by population density, while forest land in land-use types shows significant positive effects.

Spatial and Temporal Evolution of Urban Patterns in the China Greater Bay Area Based on Remote Sensing of Nighttime Lights

With the advancement of urbanization in China, the rapid expansion of cities has reduced the alternative land use and intensified the conflict between people and land, and the adaptation and optimization of urban spatial patterns have become an important work in current urban research. Based on the comprehensive modified the long-term series DMSP-OLS and NPP-VIIRS nighttime lighting data, the spatial analysis method of GIS and urban expansion indicators are used to reveal the spatial and temporal change process of urban built-up areas in the center of gravity of Guangdong-Hong Kong-Macao Greater Bay Area from 1992 to 2020. The results reveal that the built-up area of the urban agglomerations in the study area generally shows an increasing trend year by year, but there are some periods when the area decreases or remains unchanged, while the expansion rate shows a gradual decrease over a long time scale. The compactness of the urban agglomerations in the GBA decreases over time, and the fractal dimension fluctuates but remains unchanged, while the spatial distribution of the urban agglomerations in the GBA shows a "southwest-northeast" direction during 1992-2020, with the center of gravity moving to the northeast gradually. On the side, the urban expansion shows a fluctuation of "diffusion-coalescence-diffusion". The expansion of urban built-up areas is influenced by both natural and socio-economic factors, and the degree of influence varies in different periods.

Quantitative spatial assessment of the impact of urban growth on the landscape network of Türkiye's coastal cities.

This study aims to evaluate the impact of urban spatial expansion on the landscape patterns of coastal cities in Türkiye through the use of spatio-temporal indices. To this end, temporal samples covering a period of approximately 30years (1986-2000 and 2000-2015) were analyzed for six selected coastal cities in Türkiye. The urban spatial growth patterns in these cities were characterized using 17 geospatial indices, and Pearson's correlation coefficient and principal component analysis were employed to assess the quantitative relationships between urban spatial development and the fragmentation of landscape networks. The results of the study demonstrated that the patterns of landscape fragmentation caused by the spatial growth patterns of the selected coastal cities can be divided into two categories: disconnected landscape connections and perforated landscape patterns. Leapfrog development was identified as a major factor in the fragmentation of landscape continuity, leading to the formation of perforated green structures. The findings of this research can serve as spatial guidelines for addressing disconnected landscapes, a crucial factor in enhancing urban sustainability in Türkiye's coastal cities. Furthermore, the outcomes of this study may be useful for informing the national well-being strategy for urban planning and natural mitigating policies in Türkiye's coastal regions.

A Study of Historical Urban Landscape Layering in Luoyang Based on Historical Map Translation

China has undergone a tremendous urbanization process over the past four decades, resulting in a considerable conflict between the desire to construct contemporary urban areas and the desire to safeguard old urban structures. In addition to destroying the physical space of ancient urban sites, urban development has fragmented the natural landscape and severed its structural relationship with the historical urban landscape. In this study, we selected Luoyang as the research object and digitally translated and laminated the laminar process of Luoyang’s historical urban landscape based on historical maps and related historical documents. It is intended to establish a digital historical urban landscape information database comprising data from different periods of the city through the systematic translation of historic maps and through combining the following different landscape carriers: urban spatial patterns, roads and streets, landscape units, and landmark buildings. While reading the historical maps of the city, the spatial analysis tool, ArcGIS, was used to identify the trajectory of the spatial evolution of Luoyang’s spatial structure throughout its history. In this paper, we suggest the establishment of a Luoyang urban historical axis system to integrate the city’s fragmented historical urban landscape, thereby strengthening the social perception of such historical urban landscapes and preserving their memory.

Multi-Dimensional Evaluation of Land Comprehensive Carrying Capacity Based on a Normal Cloud Model and Its Interactions: A Case Study of Liaoning Province

Studying land comprehensive carrying capacity (LCCC) is the foundational and key requirement for determining land development planning and urban spatial development patterns of a region. However, the traditional evaluation method discounts the fuzziness and randomness of the evaluation index and its results. The cloud model combines randomness and fuzziness to reveal the correlation between randomness and fuzziness using numerical feature entropy and is used to represent the granularity of a qualitative concept. This study used the Liaoning Province as the study area, and developed a multi-dimensional evaluation index system for LCCC using a normal cloud model. Based on this, the relationship between the different elements of geological condition, resources and environment, economic scale and urban construction were studied using the coupling coordination degree model that reflected not only the system interactions but also the strengths of its degree of coordination. Our results were as follows: (1) numerical feature entropy were evaluated to determine the carrying capacity level of the land, and comprehensive land carrying capacity evaluations were conducted in terms of both quantitative results and the reliability of the results, promoting the scientific application of uncertainty theory in the field of comprehensive land evaluation as well as carrying capacity. (2) Liaoning Province’s prefecture-level cities had distinctly different LCCC, demonstrating “low in the west and high in the east” spatial distribution characteristics. Cities with established economies and relatively strong infrastructures had larger comprehensive land carrying capacities. Overall, there was considerable consistency across the region, though the “low in the west and high in the east” spatial distribution characteristics affected the degree of coordination.