Points Of Interest Density Research Articles

Delineating Urbanicity and Rurality: Impact on Environmental Exposure Assessment.

Environmental exposures and their health impacts can vary substantially between urban and rural areas. However, different methods for classifying these areas could lead to inconsistencies in environmental exposure and health studies, which are often overlooked. We constructed different urban/rural classification systems based on multiple population-based (e.g., total population, population density, and commuting) and built-environment-based (e.g., nighttime light intensity, building density, road density, distance to urban centers, point of interest density, and urban area coverage) indicators and various classification schemes. These classification systems were applied to Virginia and West Virginia, United States. We compared differences in urban/rural spatial patterns, demographic compositions, and exposures of particulate matter (PM2.5), greenspace, and land surface temperature using these urban/rural classification systems to understand their impacts on environmental exposure and health research. Our findings reveal clear differences in spatial patterns and demographic compositions across various systems. We also observed that different systems can lead to changes in the magnitude and direction of urban/rural disparities in environmental exposure assessment. Addressing the complexities in delineating urbanicity and rurality may include careful consideration of classification systems to reflect those aspects of urbanicity and rurality that are relevant to the research question or the use of multiple, complementary systems.

Exploring non-linear urban vibrancy dynamics in emerging new towns: A case study of the Wuhan metropolitan area

New towns have been extensively developed in many worldwide metropolises to overcome a series of urban crisis caused by over-intensive population and economic activity, such as environmental deterioration, traffic congestion and space shortage. The successful development of new towns has been regarded as a common, international urban challenge aimed at creating vital and lively urban life. Quantifying the complex relationship between new town vibrancy and the built environment provides a theoretical basis for formulating adequate planning regulations and policies to solve the development predicaments of new towns. Taking the Wuhan metropolitan area as the case study, this study employs the gradient boosting decision tree (GBDT) machine learning method to delve into the intricate non-linear relationship between urban vibrancy and built environment indicators both in both peripheric new towns (NT) and central mother cities (MC), shedding light on the differences in the characteristics and complexities of vibrancy between NT and MC. The findings reveal stark disparities in vibrancy levels between NT and MC, emphasizing the critical need for tailored revitalization strategies. It is found that the indicators with the highest contribution to the impact of vibrancy in NT and MC are respectively the point of interest (POI) density and Transit-Oriented Development (TOD) development intensity. Model comparison between NT and MC concludes that enhancing POI density to meet the basic needs of citizens is a prerequisite for fostering urban vibrancy. To successfully create similarly vital and lively urban life as that in the MC, NT should focus on the impacts of TOD development and policy factors such as government relocation on vibrancy. Finally, deriving from these findings, we propose some policy insights and practical strategies for urban practitioners and policymakers to enhance the vibrancy and sustainability of the new towns.

Spatiotemporal Pattern and Influencing Factors of Thermal Environment Based on SEM Model: A Case Study in Xi'an Metropolitan Area

The urban thermal environment is an important indicator for evaluating the ecological environment of a city. It directly affects the health of residents and the sustainable development of the urban economy. However, there is currently a lack of analysis on the impact pathways of the thermal environment considering both natural and human factors. Based on the MODIS MYD11A2 land surface temperature data, meteorological data, and human activity data of Xi'an metropolitan area in 2020, ArcGIS spatial geostatistical analysis was used to study the temporal and spatial distribution pattern of the thermal environment in different seasons, and redundancy analysis was utilized to select the main factors affecting the thermal environment. Then, structural equation modeling was used to quantify the direct and indirect effects of the dominant factors on the urban thermal environment. The results showed that:① The surface temperature in the Xi'an urban area showed a spatial pattern of higher temperatures in the north and lower temperatures in the south, with a decrease in temperature from the city center to the surrounding areas. The most severe heat environment pollution occurred in the summer. ② The redundancy analysis (RDA) results indicated that the main factors that affected the thermal environment were air temperature, impermeable surfaces, vegetation, and precipitation. ③ The results of the structural equation modeling (SEM) indicated that meteorological, surface, and anthropogenic factors affected the urban thermal environment mainly through direct pathways, which were much more important than all indirect pathways. Factors such as temperature, impervious surfaces, and point of interest density had a significant positive effect on the thermal environment (0.10 and 0.33). On the other hand, factors such as water bodies, precipitation, and vegetation had a significant negative effect on the thermal environment (-0.29 and -0.25). Human activities had a greater direct impact on nocturnal surface temperatures than surface and meteorological factors. Increasing economic efficiency is beneficial for mitigating the urban heat island effect. The results of the study can provide a reference for studying local climate change in urban heat islands and for the construction of green and ecologically livable urban environments.

Exploring the Relationship between Urban Vibrancy and Built Environment Using Multi-Source Data: Case Study in Munich

Urbanization has profoundly reshaped the patterns and forms of modern urban landscapes. Understanding how urban transportation and mobility are affected by spatial planning is vital. Urban vibrancy, as a crucial metric for monitoring urban development, contributes to data-driven planning and sustainable growth. However, empirical studies on the relationship between urban vibrancy and the built environment in European cities remain limited, lacking consensus on the contribution of the built environment. This study employs Munich as a case study, utilizing night-time light, housing prices, social media, points of interest (POIs), and NDVI data to measure various aspects of urban vibrancy while constructing a comprehensive assessment framework. Firstly, the spatial distribution patterns and spatial correlation of various types of urban vibrancy are revealed. Concurrently, based on the 5Ds built environment indicator system, the multi-dimensional influence on urban vibrancy is investigated. Subsequently, the Geodetector model explores the heterogeneity between built environment indicators and comprehensive vibrancy along with its economic, social, cultural, and environmental dimensions, elucidating their influence mechanism. The results show the following: (1) The comprehensive vibrancy in Munich exhibits a pronounced uneven distribution, with a higher vibrancy in central and western areas and lower vibrancy in northern and western areas. High-vibrancy areas are concentrated along major roads and metro lines located in commercial and educational centers. (2) Among multiple models, the geographically weighted regression (GWR) model demonstrates the highest explanatory efficacy on the relationship between the built environment and vibrancy. (3) Economic, social, and comprehensive vibrancy are significantly influenced by the built environment, with substantial positive effects from the POI density, building density, and road intersection density, while mixed land use shows little impact. (4) Interactions among built environment factors significantly impact comprehensive vibrancy, with synergistic interactions among the population density, building density, and POI density generating positive effects. These findings provide valuable insights for optimizing the resource allocation and functional layout in Munich, emphasizing the complex spatiotemporal relationship between the built environment and urban vibrancy while offering crucial guidance for planning.

Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries

Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ needs. Two fundamental spatial query types, k-nearest neighbors (kNN) and range search, enable users to access specific points of interest (POIs) based on their location, which are measured by actual road distance. However, retrieving the nearest POIs using actual road distance can be computationally intensive due to the need to find the shortest distance. Using straight-line measurements could expedite the process but might compromise accuracy. Consequently, this study aims to evaluate the accuracy of the Euclidean distance method in POIs retrieval by comparing it with the road network distance method. The primary focus is determining whether the trade-off between computational time and accuracy is justified, thus employing the Open Source Routing Machine (OSRM) for distance extraction. The assessment encompasses diverse scenarios and analyses factors influencing the accuracy of the Euclidean distance method. The methodology employs a quantitative approach, thereby categorizing query points based on density and analyzing them using kNN and range query methods. Accuracy in the Euclidean distance method is evaluated against the road network distance method. The results demonstrate peak accuracy for kNN queries at k=1, thus exceeding 85% across classes but declining as k increases. Range queries show varied accuracy based on POI density, with higher-density classes exhibiting earlier accuracy increases. Notably, datasets with fewer POIs exhibit unexpectedly higher accuracy, thereby providing valuable insights into spatial query processing.

Using local co-location quotient and niche-based model to assess fire risk in urban environments: A case study of Beijing, China

Accurate knowledge and identification of the spatial distribution of fire incidents are vital for effective urban fire safety management. While the complexity of the physical environment poses challenges in obtaining precise fire risk distribution. To address this, we propose a novel approach that leverages the species distribution prediction model, focus on examining the spatial association between fires and physical environment, as well as the application of niche-based model for fire risk prediction. By utilizing the local co-localization quotient (LCLQ), we found that 8 out of 9 types of point of interest (POI) in Beijing, China, exhibit a spatial association with fire incident locations. Based on this relationship, we incorporate the impactful POI and fire data into MaxEnt, a species distribution prediction model, to predict the distribution of fire hazards. Additionally, we perform a comprehensive fire risk assessment by incorporating diurnal population data. Our experimental results demonstrate that the MaxEnt model achieved a maximum AUC value of 0.78 when employing a POI density search radius of 500 m. Furthermore, the high-resolution results show good integration with other geographical data. The utilization of LCLQ and niche-based model improves the efficiency and resolution of fire risk assessment, enhancing fire risk assessment practices and management.

Identification of Urban Functional Zones Based on POI Density and Marginalized Graph Autoencoder

With rapid urbanization, urban functional zones have become important for rational government and resource allocation. Points of interest (POIs), as informative and open-access data, have been widely used in studies of urban functions. However, most existing studies have failed to address unevenly or sparsely distributed POIs. In addition, the spatial adjacency of analysis units has been ignored. Therefore, we propose a new method for identifying urban functional zones based on POI density and marginalized graph autoencoder (MGAE). First, kernel density analysis was utilized to obtain the POI density and spread the effects of POIs to the surroundings, which enhanced the data from unevenly or sparsely distributed POIs considering the barrier effects of main roads and rivers. Second, MGAE performed feature extraction in view of the spatial adjacency to integrate features from the POIs of the surrounding units. Finally, the k-means algorithm was used to cluster units into zones, and semantic recognition was applied to identify the function category of each zone. A case study of Changzhou indicates that this method achieved an overall accuracy of 90.33% with a kappa coefficient of 0.88, which constitutes considerable improvement over that of conventional methods and can improve the performance of urban function identification.

Will agricultural land scale management aggravate non-point source pollution? – Chaohu Lake Basin, China as a case study

In this study, we investigated the relationship between scale management and non-point source (NPS) total phosphorus (TP) load in Chaohu Lake Basin. We used agricultural point of interest (POI) density to characterize scale management and employed the Soil and Water Assessment Tool (SWAT) model for simulations. Our findings in 2018 showed that there was a segmented relationship between agricultural POI density and TP load in townships. Significant negative correlations between POI density and TP load were observed in medium-density (0.076 < POI<0.172) and high-density (POI>0.172) scale management townships, but not in low-density (POI<0.076) townships. The slope coefficient for high-density townships was approximately one-tenth of that for medium-density townships, indicating the prevalence of agricultural land scale management at only a certain density can effectively inhibit NPS TP emissions, and this inhibitory effect has a marginal diminishing effect. These observations also applied to vegetable, fruit, and flower nurseries bases. Notably, low-density scale management of farms still had a significant inhibitory effect on TP load. However, scale management of forest farms and tea plantations did not inhibit TP loads due to the influence of high natural background phosphorus content, low hills with large gradients, and low POI density.

Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China’s multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China’s GDP moved 128.80 km to the southwest. These results indicate China’s achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy.

Examining the importance of built and natural environment factors in predicting self-rated health in older adults: An extreme gradient boosting (XGBoost) approach

Previous studies indicate that natural and built environment factors significantly influence health outcomes in older adults. However, most cross-sectional studies exploring the impact of these factors on health fail to quantify the relative importance of each factor. Here, we use the XGBoost machine learning technique with SHAPley Additive exPlanations (SHAP) to rank the importance of built environment factors, natural environmental factors, and sociodemographic factors in shaping older adults’ odds of good self-rated health (SRH), in Shanghai, Guangzhou, and Shenzhen, China. To address self-selection bias in housing choice, older adults living in private housing, who have more freedom to choose residential locations, were distinguished from those living in public or self-built housing. To better interpret the results of XGBoost outcomes and analyse the association between factors and SRH, we used SHAP dependence plots. Results show that both built environment and natural environment factors play important roles in predicting SRH. Four built environment factors (accessibility to public transit stations, points of interest density, road density, and population density) and two natural environment factors (air quality index and surrounding greenness) have considerable predictive power for SRH for both groups. Among these factors, accessibility to public transit stations, road density, and air quality index become less important after controlling for self-selection bias. We also trace potential threshold effects of residential greenness, points of interest density, and road density on decreasing older adults' SRH within certain intervals after controlling for self-selection bias. Findings from this study can support the decision-making of policymakers regarding urban planning, landscape design, and environmental management to improve overall health of older adults.

Evaluating Urban Vitality Based on Geospatial Big Data in Xiamen Island, China

Urban vitality generally refers to the ability to attract lively businesses and human activities, which largely depends on the urban built environment and human flows. Previous studies that applied the empirical approach to analyze urban vitality from the feature of the urban built environment and the behavior of residents are scarce. This study developed a novel framework for exploring urban vitality on the street scale with geographic big data in Xiamen Island—a livable city in southern China. We measured urban vitality and analyzed its characteristics using the Shannon entropy model based on four dimensions of GIS-based data on the point of interest density, taxi flow density, building density, and road density. We then apply the regression model to explore the relationship between land-use diversity and urban vitality. Results show that four dimensions are associated with urban vitality. Furthermore, the dimensions of the taxi flow density and the road density in low vitality blocks can be distinguishable from other blocks. Results showed a stronger relationship between land-use diversity and urban vitality. The urban expansion direction of Xiamen Island is from the old town around the Xiamen Port to the new district around Yundang Lake to form a continuous covered area, which significantly impacts urban vitality. The approach of this study not only measures urban vitality through human activity but also sheds light on guidelines for sustainable urban development, which can plan and policy in practice for cities in China and many other countries.

Microplastics in urban soils of Nanjing in eastern China: Occurrence, relationships, and sources.

Although microplastic (MP) pollution has been defined as a new global challenge by the United Nations Environment Programme, their abundance and composition has only been studied in-depth within farmland soil, while minimal attention has been placed on urban soil contamination. Accordingly, within the current study, MP abundance and composition is investigated within urban soil from green spaces in Nanjing, eastern China. The average MP abundance in soil was 461±222 items/kg and primarily comprised fibers (39.1%) and fragments (37.7%). MPs <1000μm in size accounted for 83.7% of the total content and white MPs were the most abundant (26.5%). The dominant polymers were polyethylene glycol terephthalate (32.0%) and polypropylene (20.5%). Moreover, relationship network analysis generated three distinct MP modules based on community similarity. Indeed, the degree of similarity increased by ∼26.8% per kilometer. Furthermore, application of a forward selective optimal multiple regression model identified clay, sand, longitude, and points of interest for recycling bins (RecyclePOI) as the primary spatial and soil environmental factors affecting MP abundance and composition. Additionally, five potential sources of MPs were identified based on the MP diversity integrated index fitting results, and point of interest density (MDII-POI) source analysis (R2=0.21-0.62; P<0.05). In particular, the point of interest of express delivery points (ExpressPOI) were important sources of plastic emissions as they are widely distributed throughout urban and fringe areas. Collectively, the findings of this study provide novel insights regarding quantitative source appointment and regional ecological control of MPs in urban soil.

Integrating Point-of-Interest Density and Spatial Heterogeneity to Identify Urban Functional Areas

Accurately identifying and delineating urban functional areas has seen increasing demand in smart urban planning, landscape design, and resource allocation. Recently, POI (point of interest) data have been increasingly applied to identify urban functional areas. However, heterogeneity in urban spaces or the corresponding POI data has not been fully considered in previous studies. In this study, we proposed a new scheme for urban-functional-area identification by combining POI data, OpenStreetMap (OSM) datasets, and high-resolution remote-sensing imagery. A function-intensity index that integrates the quantitative-density index and average-nearest-neighbor index (ANNI) of POIs was built for representing the urban function. The results show that the proposed function-intensity index can balance the impact of the spatial heterogeneity of each type of POI on determining the functional characteristics of the urban units. In Futian District, Shenzhen, China, the method was effective in distinguishing functional areas with fewer POI amounts but high ANNIs from those functional areas with dense POIs. The overall accuracy of the proposed method is about 11% higher than that of the method using the POI density only. This paper argues for considering both the quantitative density and spatial heterogeneity of POIs to improve urban-functional-area identification.

Identification of Urban Functional Areas and Governance Measures Based on Point of Interest Data: A Case Study of the Shenyang Railway Station Area in Shenyang City

Inventory extraction and governance measures in urban land use have become important topics in urban regeneration research. This study aimed to inform design governance in urban regeneration through a point of interest (POI) data-based case study. An approximately 15 km2 rectangular development area was assessed for its characteristics and deficiencies. Frequency density analysis was conducted based on data identification of urban built environments and governance from an integrated planning perspective, using field research, spatial autocorrelation, Getis–Ord Gi* analysis, and SPSS. We (1) applied POI data to assess the frequency ratios of the function types for the 6008 buildings in the study area; (2) analysed layouts of POI densities in hotspots for different formats; and (3) applied an evidence-based approach and overlay analysis to identify the area’s functional morphological zones. Finally, the urban physical and morphological properties were identified and compared with the identification result of the urban functional areas to qualitatively evaluate the differences. Global Moran’s I of the POI density of Retail Business (B11), Restaurants (B13), Hotels (B14), Entertainment and Recreation (B3), and Residential Services (R22) were 0.35, 0.35, 0.06, 0.20, and 0.15, respectively, displaying a significant spatial clustering feature. However, for land types including Administrative Offices (A1), Finance and Insurance (B21), and Other Business Facilities (B29), the p-values between the POI density and the random pattern were 0.23, 0.71, 0.56, respectively, showing no significant difference. Our study provides recommendations for governance and integrated urban redevelopment planning to coordinate and guide further regeneration.

Road Safety Evaluation Framework for Accessing Park Green Space Using Active Travel

The COVID-19 pandemic has led to a burgeoning demand for active travel (walking or cycling), which is a healthy, pollution-free, and affordable daily transportation mode. Park green space (PGS), as an open natural landscape, have become a popular destination for active travel trips in metropolitan areas. Pedestrians and cyclists are often at high crash risk when exposed to complicated traffic environments in urban areas. Therefore, this study aims to propose a safety assessment framework for evaluating active travel traffic safety (ATTS) near PGS from the perspective of urban planning and exploring the effect of the point-of-interest (POI) aggregation phenomenon on ATTS. First, links between ATTS and the environment variables were investigated and integrated into the framework using the catastrophe model. Second, the relationship between the POI density and ATTS was investigated using three spatial regression models. Results in the Wuhan Metropolitan Area as a case study have shown that (1) the population density, road density, nighttime brightness, and vegetation situation near PGS have pronounced effects on ATTS; (2) pedestrians near PGS enjoy safer road facilities than cyclists. Active travel traffic near PGS requires more attention than non-park neighborhoods; (3) among four park categories, using active travel to access theme parks is the safest; and (4) SEM has the best fit for POI cluster research. Increases in leisure facility density and residence density may lead to deterioration and improvement in ATTS safety levels near PGSs, respectively. The safety framework can be applied in other regions because the selected environment indicators are common and accessible. The findings offer appropriate traffic planning strategies to improve the safety of active travel users when accessing PGS.

Creating Engines of Prosperity: Spatiotemporal Patterns and Factors Driving Urban Vitality in 36 Key Chinese Cities.

Under the requirements of new urbanization in China, the improvement of urban spatial vitality has become a key aspect of the territory development plan. Based on the theory of urban vitality by Jacobs, this study analyzed the spatiotemporal characteristics of mobility, diversity, and regularity from urban vitality in 36 key Chinese cities from 1990 to 2015; the urban vitality was evaluated by considering the society, economy, environment, culture, and cyberspace and by exploring its driving factors. The results suggest that vitality was higher in the southern/eastern coastal cities than in the northern/western inland ones. Cities with the highest vitality levels (top 10) were mostly located in the Pearl and Yangtze River deltas, the Beijing-Tianjin region, and the middle reaches of the Yangtze River. Shenzhen demonstrated the highest vitality in the stage of extremely high coordination. Most of the cities with a middle level of vitality were located in northern China, while cities with poor vitality were small-scale cities with underdeveloped economies in the west and provincial capitals in northeastern China with a shrinking population. Areas with high-density facilities, high-accessibility transportation, multiple functional land use, and a high standard of living always have high vitality. The point of interest density had a significant positive effect on daytime vitality in regions with moderate and high vitality, but varying effects on nighttime vitality; it had a higher influence on nighttime vitality than on daytime vitality in areas of extremely high vitality. The study also provides corresponding measures for infrastructure, which could be invested in and constructed at different urban development stages to promote vitality. This could provide guidance for creating a booming space and revitalize and increase urban vitality, and could help reasonably regulate and control urban population and land use.

Association between leisure-time physical activity and the built environment in China: Empirical evidence from an accelerometer and GPS-based fitness app.

To reexamine the relationship between leisure-time physical activity (LTPA) and the built environment (BE), this paper takes advantage of the massive amount of data collected by an accelerometer and GPS-based fitness mobile app. Massive LTPA data from more than 3 million users were recorded by Codoon in 500m by 500m grid cells and aggregated to 742 natural cities in mainland China. Six BE indicators were quantified using GIS at the city scale. Robust regression analysis was used to estimate the correlation between LTPA and BE. Five of six BE indicators—connectivity, road density, land use mix, points of interest density, and density of parks and squares—were significantly, positively, independently, and linearly related to LTPA in the regression analysis. The study obtains findings that are consistent with the previous literature but also provides novel insights into the important role of POI density in encouraging LTPA, as well as how the relationship between LTPA and BE varies by time of day. The study also sheds light on the embrace of new technology and new data in public health and urban studies.

Multidimensional Urban Vitality on Streets: Spatial Patterns and Influence Factor Identification Using Multisource Urban Data

Urban vitality is a key indicator for measuring urban development. This topic has been trending in urban planning and sustainable development, and significant progress has been made in measuring single indicators of urban vitality based on parcel or block units. With the continuous development of smart sensing technology, multisource urban data are becoming increasingly abundant. The application of such data to measure the multidimensional urban vitality of street space, reflecting multiple functions of an urban space, can significantly improve the accuracy of urban vitality analyses and promote the construction of people-oriented healthy cities. In this study, streets were taken as the analysis unit, and multisource data such as the trajectories of taxies and shared bicycles, user reviews and cultural facility points of interest (POIs) in Chengdu, a city in southwestern China, were used to identify spatial patterns of urban vitality on streets across social, economic and cultural dimensions. The correlation between the built environment factors and the multidimensional urban vitality on the street was analyzed using a multiple regression model. The spatial distribution of the different dimensions of urban vitality of the street space in Chengdu varies to a certain extent. It is common for areas with high social vitality to have production and life centers nearby. High economic vitality centers are typically found along busy streets with a high concentration of businesses. Areas with high cultural vitality centers tend to be concentrated on the city’s central streets. Land use, transportation, external environment, population and employment are all closely linked to urban vitality on streets. The crowd counting and POI density have the greatest impact on multidimensional urban vitality. The crowd and the level of service facilities profoundly affect social interaction, trade activities and cultural communication. The goodness of fit (R2) of the regression models for social, economic and cultural vitality are 0.590, 0.423 and 0.409, respectively. Using multisource urban data, our findings can help stakeholders better understand the spatial patterns and influencing factors of multidimensional urban vitality on streets and provide sustainable urban planning and development strategies for the future.

Research on the Difference Between Recreational Walking and Transport Walking Among the Elderly in Mega Cities With Different Density Zones: The Case of Guangzhou City.

Walking is the easiest method of physical activity for older people, and current research has demonstrated that the built environment is differently associated with recreational and transport walking. This study modelled the environmental characteristics of three different building density zones in Guangzhou city at low, medium, and high densities, and examined the differences in walking among older people in the three zones. The International Physical Activity Questionnaire (IPAQ) was used to investigate the recreational and transport walking time of older people aged 65 years and above for the past week, for a total of three density zones (N = 597) and was analysed as a dependent variable. Geographic Information Systems (GIS) was used to identify 300, 500, 800, and 1,000 m buffers and to assess differences between recreational and transport walking in terms of the built environment [e.g., land-use mix, street connectivity, Normalised Difference Vegetation Index (NDVI) data]. The data were processed and validated using the SPSS software to calculate Pearson's correlation models and stepwise regression models between recreation and transit walking and the built environment. The results found that land use mix and NDVI were positively correlated with transport walking in low-density areas and that transport walking was negatively correlated with roadway mediated centrality (BtE) and Point-of-Interest (PoI) density. Moreover, recreational walking in medium density areas was negatively correlated with self-rated health, road intersection density, and PoI density while positively correlated with educational attainment, population density, land use mix, street connectivity, PoIs density, and NDVI. Transport walking was negatively correlated with land-use mix, number of road crossings while positively correlated with commercial PoI density. Street connectivity, road intersection density, DNVI, and recreational walking in high-density areas showed negative correlations. Moreover, the built environment of older people in Guangzhou differed between recreational and transport walking at different densities. The richness of PoIs has different effects on different types of walking.

Understanding the pattern and mechanism of spatial concentration of urban land use, population and economic activities: a case study in Wuhan, China

ABSTRACT Quantifying the aggregation patterns of urban population, economic activities, and land use are essential for understanding compact development, but little is known about the difference among the distribution characteristics and how the built environment influences urban aggregation. In this study, five elements are collected in Wuhan, China, namely population density, floor area ratio, business POIs, road network and built-up area as the representative of urban population, economic activities and land use. An inverse S-shape function is employed to fit the elements’ macro distribution. An aggregation degree index is proposed to measure the aggregation level of urban elements. The kernel density estimation is used to identify the aggregation patterns. The spatial regression model is used to identify the built environment factors influencing the spatial distribution of urban elements. Results indicates that all urban elements decay outward from the city center in an inverse S-shape manner. The business Point-of-Interest (POI) density and population density are highly aggregated; floor area ratio and road density are moderately aggregated, whereas the built-up density is poorly aggregated. Three types of spatial aggregation patterns are identified: a point-shaped pattern, an axial pattern and a planar pattern. The spatial regression modeling shows that the built environment is associated with the distribution of the urban population, economic activities and land use. Destination accessibility factors, transit accessibility factors and land use diversity factors shape the distribution of the business POI density, floor area ratio and road density. Design factors are positively associated with population density, floor area ratio and built-up density. Future planning should consider the varying spatial concentration of urban population, economic activities and land use as well as their relationships with built environment attributes. Results of this study will provide a systematic understanding of aggregation of urban land use, population, and economic activities in megacities as well as some suggestions for planning and compact development.