Point Of Interest Data Research Articles

Urban noise exposure assessment based on principal component analysis of points of interest

Accurate qualitative and quantitative information on the characteristics of traffic noise exposure in densely populated urban areas is an important prerequisite for reasonable traffic noise control. The primary objective of this study is the development and application of a traffic noise exposure evaluation method based on points of interest (POIs). First, an automatic query arithmetic is used to acquire geospatial information, POIs data, building and network information from the webmap. Second, the attribute matrix of preprocessed POIs for the population is constructed. And the population distribution is obtained by principal component analysis (PCA) of POIs and Gaussian decomposition of demographic data. Then, the modified traffic noise line-source model is applied to calculate the noise distribution considering attenuation among buildings based on measured traffic flow parameters. Finally, with the help of the proposed noise evaluation indicators, and considering the noise function requirements (NFRs, which can be divided into four classes according to different area land-use types), traffic noise evaluation is realized. The proposed method is applied to a typical region with four NFR classes. It is concluded that the characteristics of traffic noise exposure are affected by traffic conditions, buildings, NFR classes and population distribution. And the crowds exposed to noise present aggregation effects, which are usually centered around specific buildings. In addition, POI types which people actives related suffer more serious noise exposure, and exposure is overestimated at low requirement regions without considering crowd distribution of the setting scenario.

Research on the Extraction Method Comparison and Spatial-Temporal Pattern Evolution for the Built-Up Area of Hefei Based on Multi-Source Data Fusion

With the development of urban built-up areas, accurately extracting the urban built-up area and spatiotemporal pattern evolution trends could be valuable for understanding urban sprawl and human activities. Considering the coarse spatial resolution of nighttime light (NTL) data and the inaccurate regional boundary reflection on point of interest (POI) data, land surface temperature (LST) data were introduced. A composite index method (LJ–POI–LST) was proposed based on the positive relationship for extracting the boundary and reflecting the spatial-temporal evolution of urban built-up areas involving the NTL, POIs, and LST data from 1993 to 2018 in this paper. This paper yielded the following results: (1) There was a spatial-temporal pattern evolution from north-east to south-west with a primary quadrant orientation of IV, V, and VI in the Hefei urban area from 1993–2018. The medium-speed expansion rate, with an average value of 14.3 km2/a, was much faster than the population growth rate. The elasticity expansion coefficient of urbanization of 1.93 indicated the incongruous growth rate between the urban area and population, leading to an incoordinate and unreasonable development trend in Hefei City. (2) The detailed extraction accuracy for urban and rural junctions, urban forest parks, and other error-prone areas was improved, and the landscape connectivity and fragmentation were optimized according to the LJ–POI–LST composite index based on a high-resolution remote sensing validation image in the internal spatial structure. (3) Compared to the conventional NTL data and the LJ–POI index, the LJ–POI–LST composite index method displayed an extraction accuracy greater than 85%, with a similar statistical and landscape pattern index result. This paper provides a suitable method for the positive relationship among these LST, NTL, and POI data for accurately extracting the boundary and reflecting the spatial-temporal evolution of urban built-up areas by the fusion data.

Examining Changes in Consumer Spatial Structure and Sustainable Development Issues in Beijing before and after the Outbreak of COVID-19

Urban consumption spatial structure has a direct impact on the sustainable development and quality of life of urban residents. This study investigates the impact of COVID-19 on Beijing’s urban consumption spatial structure within the Sixth Ring Road. Utilizing POI (Point of Interest) data and the Kernel Density method, the spatial distribution of commercial centers is analyzed. Consumption data from China UnionPay for 2019 and 2020, along with the Weighted Voronoi diagram method, are employed to assess changes in the radiation range of commercial centers. The findings indicate that: (1) owing to the pandemic’s repercussions, commercial centers at different levels and locations have different changes. (2) There is an overarching decline in UnionPay consumer spending across diverse streets in Beijing. (3) Following the epidemic, large-scale consumption hubs have maintained their dominance, ensuring the overall stability of the city’s consumption spatial pattern. In conclusion, the changes in commercial centers and the significant decrease in consumer spending underscore the dynamic interplay between urban consumption and external shocks like the pandemic. These insights are crucial for urban planning strategies aiming to enhance both resilience and sustainability in the face of unforeseen challenges. The spatial restructuring of traditional commercial centers requires nuanced urban planning. Recognizing the resilience and expansion of smaller centers suggests the importance of fostering localized economic activities. Policymakers could incentivize their development to promote community engagement and economic sustainability.

Penentuan Sistem Pusat Pelayanan Perkotaan Berdasarkan Data Point of Interest di Kota Pangkalpinang

An interconnected city service system exists in urban areas. A good urban space structure should be able to encourage sustainable development for the people and the countryside, as a process of changing the use of dynamic urban space. The high service requirements of the central area of Pangkalpinang City are not offset by the availability of adequate urban facilities. The purpose of this study is to describe the service center system in Pangkalpinang City. The method used in this research with kernel density analysis uses POI (Point of Interest) data related to social, cultural, economic, and/or public administration. Analysis results showed that the Padi Market Area is still a potential service center in Pangkalpinang City, with the main character of trade and services. This is in line with the 2011-2030 RTRW of Pangkalpinan City that makes the Pedi Market area the service center of Pangcalpinang Town. Several areas have the potential to develop functions and roles as a City Service Subcenter. The results of this analysis are expected to provide recommendations to policymakers in drawing up the structure of urban spaces in urban spatial documents.

A Deep-Learning-Based Multimodal Data Fusion Framework for Urban Region Function Recognition

Accurate and efficient classification maps of urban functional zones (UFZs) are crucial to urban planning, management, and decision making. Due to the complex socioeconomic UFZ properties, it is increasingly challenging to identify urban functional zones by using remote-sensing images (RSIs) alone. Point-of-interest (POI) data and remote-sensing image data play important roles in UFZ extraction. However, many existing methods only use a single type of data or simply combine the two, failing to take full advantage of the complementary advantages between them. Therefore, we designed a deep-learning framework that integrates the above two types of data to identify urban functional areas. In the first part of the complementary feature-learning and fusion module, we use a convolutional neural network (CNN) to extract visual features and social features. Specifically, we extract visual features from RSI data, while POI data are converted into a distance heatmap tensor that is input into the CNN with gated attention mechanisms to extract social features. Then, we use a feature fusion module (FFM) with adaptive weights to fuse the two types of features. The second part is the spatial-relationship-modeling module. We designed a new spatial-relationship-learning network based on a vision transformer model with long- and short-distance attention, which can simultaneously learn the global and local spatial relationships of the urban functional zones. Finally, a feature aggregation module (FGM) utilizes the two spatial relationships efficiently. The experimental results show that the proposed model can fully extract visual features, social features, and spatial relationship features from RSIs and POIs for more accurate UFZ recognition.

OGC P17 Reporting nutritional outcomes following gastrectomy: A systematic mapping review

Abstract Background Following an international consensus agreement of patients and healthcare professionals (GASTROS Study), ‘nutritional outcomes’ have been identified as critically important to report in surgical trials for gastric cancer. A previous review has demonstrated that nutritional outcomes are rarely reported by trials, and little is known about how best to measure them. In preparation for future work on establishing an optimum approach to report and measure nutritional outcomes, a mapping review was undertaken to examine current approaches used by researchers in this field. Methods We conducted a systematic literature search (using MEDLINE, Embase, CINAHL, Cochrane and PsychInfo) and included randomised control trials and prospective studies reporting any form of nutritional outcome in patients following gastrectomy, between 2012 and 2022. The review was conducted using PRISMA guidelines and registered on the PROSPERO database (CRD42022365652). Full text reviews and data extraction were completed independently by three reviewers. Datapoints of interest included general trial and surgery characteristics, study types, duration and frequency of nutritional outcomes and the utility of nutritional screening measurement tools. Data collection and analysis was conducted on Microsoft Excel. Results 3288 abstracts were screened, yielding 87 studies (17220 patients) eligible for inclusion. 90% of trials were based in East Asia. 24% of trials reported biochemical, anthropometric, and weight-based outcomes. 21 different biochemical tests were reported by 74% trials, the most common marker being ‘albumin’. Anthropometric measurements were heterogenous with BMI and skeletal muscle index being the most frequently reported. 66% of studies reported patient weight as an outcome. 28% studies reported both nutritional screening and assessment. 48% of trials utilised screening tools. The frequency and time points at which nutritional outcomes were measured varied greatly. Conclusions This mapping review highlights significant variation in the reporting of nutritional outcomes. Consensus agreement is required to establish the optimum approach to use in trials and clinical practice.

Researching Tourism Space in China’s Great Bay Area: Spatial Pattern, Driving Forces and Its Coupling with Economy and Population

Analysis of the spatial patterns and dynamics of tourism services and facilities is crucial for tourism and land use planning. However, most studies in the spatial analysis of tourism rely on the city- or regional-level data; limited research has used POI (point of interest) data to accurately uncover the spatial distribution of tourism, especially its interactive and coupling relationship with the local economy and population. Based on POI data, this paper, therefore, investigates the spatial patterns and driving forces of tourism services distribution and how tourism space is coupled with the local economy and population in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. The results show the following: (1) Different categories of tourism services (catering, shopping, scenic spots, leisure, and sports) exhibit diverse spatial patterns and agglomerations, but they tend to align with the variables of economic level and population in a grid of 1 km2. (2) The spatial econometric models further reveal that population density, transportation, and hospitality facilities are positively correlated with the spatial distribution of tourism services, but GDP in a grid of 1 km2 shows a weak negative correlation with the POI of tourism services, which may be attributed to the incoordination between GDP and tourism in some areas. (3) The analysis of coupling degree further identifies the areas where tourism services have good interaction/coupling with the local GDP and population density, such that these areas can be viewed as hotspots suitable for tourism promotion. This paper thus offers meaningful policy implications by calling for an optimization of the coupling of tourism services with local social–economic factors in the GBA.

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.

Spatial distribution pattern and driving mechanism of tourist attractions in Gansu Province based on POI data.

The article utilizes POI (Point of Interest) data of tourist attractions in Gansu Province in 2021, adopts Moran's I and kernel density analysis to study the spatial distribution pattern of tourist attractions in Gansu Province, and uses spatial autoregressive modeling to explore the driving mechanism affecting their spatial distribution pattern. The results show that: (1) Gansu Province has a large number and rich types of tourist attractions, and there are differences in the number of different types of tourist attractions; (2) The spatial distribution pattern of different types of tourist attractions in different cities and towns shows the phenomenon of both agglomeration and dispersion, with a higher degree of agglomeration in the central and northwestern regions of the province and a lower degree of agglomeration in the southwestern and southeastern corners; (3) The overall spatial distribution pattern of tourist attractions shows the distribution characteristics of multi-core decentralized distribution, forming 8 core aggregation areas in the southeast of the province; (4) The article analyzes the driving mechanism of the spatial distribution pattern of tourist attractions in Gansu Province using the buffer zone and OLS models, and the results show that the natural environment, transportation location, national policies and socio-economics all have a positive impact on the distribution of tourist attractions.

Feature multi-level attention spatio-temporal graph residual network: A novel approach to ammonia nitrogen concentration prediction in water bodies by integrating external influences and spatio-temporal correlations

Accurate prediction of ammonia nitrogen concentration in water is of great significance for urban water quality management and pollution early warning. In order to improve the prediction accuracy of ammonia nitrogen concentration in water, this study developed a novel model based on graph neural networks called Feature Multi-level Attention Spatio-Temporal Graph Residual Network (FMA-STGRN). The FMA-STGRN model utilizes external influencing factors such as meteorological factors and point of interest data, as well as the spatio-temporal correlation information of ammonia nitrogen concentration between water quality monitoring stations, to accurately predict the concentration of ammonia nitrogen in water. The model consists of four main components: feature multi-level attention module, spatial graph convolution module, temporal-domain residual decomposition module, and feature fusion and output module. Through the organic combination of these four modules, FMA-STGRN can more effectively explore the complex spatio-temporal correlation relationships between water quality monitoring stations and more accurately integrate and utilize external influencing factors, thereby improving the prediction accuracy of ammonia nitrogen concentration in water. Experimental results show that the FMA-STGRN model outperforms other benchmark models such as RF, MART, MLP, LSTM, GRU, ST-GCN, and ST-GAT in various aspects. In addition, a series of feature ablation experiments were conducted to further reveal the key contributions of meteorological factors and point of interest data to the model performance. Overall, our research provides a powerful and practical tool for water quality monitoring and urban water management, with broad application prospects.

Blind area and layout optimization of parks based on the spatial supply-demand evaluation in Taiyuan City, China

It is of great practical significance to identify service blind area, scientifically select park construction areas, and clarify the priority of parks' construction based on the co-ordination of supply-demand evaluation. With the urban parks within the Taiyuan Ring Expressway as the research subjects, we estimated the accessibility range and the service pressure of each park by using the application programming interface of Gaode map route planning and point of interest data to characterize their supply and demand levels. We identified the service blind areas of parks by overlay analysis, and used the location-allocation (LA) model to purposefully supply park green space. Results showed that the accessibility coverage rates of the parks by walking and bicycling within 15 minutes were 35.6% and 71.7%, respectively, indicating insufficient supply capacity of parks. The areas with large potential demand for park green space in Taiyuan were mainly concentrated in the business district of Qinxian-Changfeng Street and the Shuangta business district within Dongzhong ring road, which existed the obviously invisible blind areas. Finally, we proposed new park green space site selection proposal based on LA model. Optimization results indicated that the coverage rates of walking and bicycling within 15 minutes increased to 46.7% and 81.0%, respectively, and that the service pressure of parks was relieved. We combined the leisure demands of urban residents and the distribution of urban parks by utilizing network big data, which could promote the scientific nature and accuracy of the optimizing site selection and provide scientific method and theory basis for urban park construction.

A dataset of GDP spatial distribution on the Qinghai-Tibet Plateau (2020)

Economic spatial distribution data serve as an indicator for disaster loss assessment. The economy of the Qinghai-Tibet Plateau is relatively backward and geological disasters occur frequently, so it is of great significance to study the impact of disasters on the economy in this area. Based on the method of zoning and industry simulation, this study establishes the corresponding relationship between the GDP1（GDP of Primary Industry）and land use types, DEM（Digital Elevation Model）, village points, roads and river buffers. Spatial models of night lighting, building site and points of interest data after the coupling of the GDP23（GDP of Secondary and Tertiary Industries）and multiple information are established in urban areas and counties. We used the random forest to determine the weights of various indicators in the secondary and tertiary industries, and calculated the grid value of GDP23. Finally, we superimposed the grid of GDP1 and GDP23 and created the spatial distribution map of GDP（Gross Domestic Product）of 100 m×100 m on the Qinghai-Tibet Plateau in 2020. According to the accuracy verification using county-level GDP statistical data, the Root Mean Square Error is 0.0385, which shows that the data can better reflect the spatial distribution of GDP on the Qinghai-Tibet Plateau. This dataset can provide GDP spatial data support for disaster risk assessment, disaster prevention and mitigation on the Qinghai-Tibet Plateau.

Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions

Abstract Examining the spatial characteristics and accessibility of cultural facilities, such as art institutions, is crucial for understanding the lifestyles of citizens. With the increasing utilization of multi-source data in geographical studies, new opportunities have arisen for investigating the development of cultural facilities in urban areas. Consequently, this article investigates the spatial dynamics of art institutions in urban development, using Nanjing, a historic city, as a case study. The study obtained a total of 260,392 points of interest data for Nanjing in 2018 and 2022 from the GaoDe map API console. By employing network analysis and the two-step floating catchment area method, the geostatistical findings reveal that the distribution of art institutions in Nanjing has become more balanced over time. For instance, the research indicates that the proportion of residents able to reach art exchange institutions within a 30-min travel time increased from 75% in 2018 to 90% in 2022. However, the distribution of art training institutions remains more concentrated compared to art exchange institutions. Furthermore, the spatial arrangement of art institutions does not entirely align with the distribution of demand points, indicating the potential for further optimization. In conclusion, this study has significant implications for understanding the accessibility, social justice, and urban planning in Nanjing. The government should pay attention to the cultural demands of marginalized communities, promote social justice and cultural inclusiveness by increasing the accessibility of cultural facilities.

Research on the relationship between population distribution pattern and urban industrial facility agglomeration in China

Investigating the impact of industrial facility agglomeration on population distribution provides valuable insights for advancing urban and regional development, as well as aiding in planning, forecasting, and achieving regional equilibrium. However, there remains a notable gap in understanding the influence and mechanisms of industrial facility agglomeration on population distribution, particularly when considering different industry types and diverse regions comprehensively. Additionally, conventional panel data used to assess industrial facility agglomeration are constrained by limitations in coverage and timeliness. In contrast, Point of Interest (POI) data offers a superior solution with its real-time, fine-grained, and innovative advantages. This study utilizes real-time and fine-grained POI data in conjunction with the LandScan population raster dataset to precisely assess industrial facility agglomeration in 352 administrative units at the prefecture level and above in China. The key findings of this research can be summarized as follows: (1) factors influencing urban population growth rates have evolved, with increased significance attributed to Government Agencies and Social Groups, alongside a consistent impact from Science, Education, and Cultural Services. (2) The correlation between industrial facility agglomerations and population growth rates displayed linear relationships in 2015 and 2021, with varying strengths and directional shifts. (3) Regional disparities in industrial facility agglomeration patterns underscore the necessity for customized strategies to optimize industrial structures, foster innovation-driven sectors, and promote sustainable population growth.

Spatial Identification and Change Analysis of Production-Living-Ecological Space Using Multi-Source Geospatial Data: A Case Study in Jiaodong Peninsula, China

The significant heterogeneity in the spatial distribution of point of interest (POI) data, the absence of human socio-economic activity information in remote sensing images (RSI), and the high cost of land use (LU) data acquisition restrict their application in PLES spatial identification. Utilizing easily accessible data for detailed spatial identification of PLES remains an urgent challenge, especially when selecting a study area that encompasses both urban built-up areas (UBUA) and non-urban built-up areas (NUBUA). To address this issue, we proposed a PLES spatial identification method that combines POI data and land cover (LC) data in this paper. The proposed method first classified spatial analysis units (SAUs) into agricultural production space (APS), ecological space (ES), and ambiguous space (AS) based on the rich surface physical information from LC data. Subsequently, the AS was further classified into living space (LS) and non-agricultural production space (NAPS) based on the rich human socioeconomic information from POI data. For the AS that contains no POI, a simple rule was established to differentiate it into LS or NAPS. The effectiveness of the method was verified by accuracy evaluation and visual comparison. Applying the method to the Jiaodong Peninsula, we identified the PLES of the Jiaodong Peninsula for 2018 and 2022, further explored their spatial distribution characteristics, and analyzed their changes. Finally, we conducted a discussion on the real-world situations and driving mechanisms of the PLES changes and proposed several policy insights. The results indicated that both the spatial distribution characteristics of PLES and PLES change in the Jiaodong Peninsula were obvious and showed significant differentiation between UBUA and NUBUA. Climatic and natural resource conditions, geographic location, macro-policies, and governmental behaviors drove the PLES changes.

Modeling vibrant areas at nighttime: A machine learning-based analytical framework for urban regeneration

Enhancing vibrant areas at nighttime (VAN) is important for promoting urban regeneration. However, the process simulation of the potential impact of urban renewal initiatives on VANs has yet to achieve complete coupling with population mobility, economy, and land utilization. In this study, we developed a simulation framework to simulate the changes in VANs and reveal their potential interconnection with urban regeneration strategies. The research methods involved the use of overlaying multiple data sources, including satellite imagery, thermal and land use maps, and point of interest data, to obtain a collection of nightlife activities in Mianyang City. We were able to identify and grade VANs based on multisource big data, providing support for urban renewal planning, as well as determine the key driving factors and configuration patterns of environmental elements that impact VAN. We also developed a machine learning-based predictive model for urban regeneration and VAN redevelopment. These results show that a vibrant nightlife can be used to regenerate dilapidated urban areas, thus reducing urbanization. Moreover, the simulation method developed in this study has wide applicability in other regions for identifying potential improvements and guiding investment and revitalization efforts in a targeted and effective manner.

The quality of OpenStreetMap food-related point-of-interest data for use in epidemiological research.

We assessed the quality of food-related OpenStreetMap (OSM) data in urban areas of five European countries. We calculated agreement statistics between point-of-interests (POIs) from OSM and from Google Street View (GSV) in five European regions. We furthermore assessed correlations between exposure measures (distance and counts) from OSM data and administrative data from local data sources on food environment data in three European countries. Agreement between POI data in OSM compared to GSV was poor, but correlations were moderate to high between exposures from OSM and local data sources. OSM data downloaded in 2020 seems to be an acceptable source of data for generating count-based food exposure measures for research in selected European regions.

Construction of ecological network based on habitat risks identifying by points of interest: A case study of Wuhan Metropolitan Area.

Ecological network construction plays a key role in ecological restoration, which can effectively mitigate negative effects of habitat fragmentation on biodiversity. Here, we proposed an improved analytical framework for ecological network identification. Specifically, ecological sources were identified using a three-dimension indicator system in terms of form, quality and functions of habitats. Ecological resistance surfaces were determined based on the incorporation of points of interest data that could characterize human activities into habitat risk assessment (HRA) model, while ecological corridors were extracted using circuit theory approach. With Wuhan Metropolitan Area as a case, we explored the key points and structures of ecological network. Moreover, we compared the construction method of ecological resistance surface based on points of interest and HRA model with traditional methods that determined by land use types and by traditional HRA model, to validate the proposed framework. The results showed that the ecological source area of Wuhan Metropolitan Area was 15200 km2, the length of ecological corridor was 1956.68 km, and that there were 87 ecological "pinch points" and 67 ecological barrier points. Compared with traditional methods, the material circulation, network complexity, and ecological connectivity of the ecological network identified by the improved framework were significantly improved, with network closure, dot-line ratio, and network connectivity being increased by 61.5%, 28.1% and 28.7% on average. The identified ecological "pinch points" and barrier points could provide precise decision-making support for ecological restoration and conservation.

Dynamic risk assessment of urban flood disasters based on functional area division—A case study in Shenzhen, China

The assessment of urban flood risk plays a vital role in disaster prevention and mitigation. This work aims to assess the dynamic risk of urban flood triggered by population movements through dividing urban functional zoning from the perspective of collective cognition. Firstly, the urban functional areas are identified using Points of Interest data and then the population movements mobile is detected based on functional areas using mobile signaling big data. Then, one-dimensional and two-dimensional hydrodynamic models are employed to simulate the 50-year flood scenario in Futian District, Shenzhen. Finally, a spatio-temporal dynamic assessment model for urban flood risk is constructed based on the extent of inundation, water depth, population density, and the disaster-bearing capacity of functional areas. The research findings are as follows: (1) Futian District's urban planning showcases harmonious integration of single-function and mixed-function areas. Utilizing the 50% perception standard efficiently identifies distinct functional types across diverse urban zones. The results are highly consistent with the actual situation. (2) During morning peak hours, the population exhibits a nuanced pattern of dispersal, concentration, and transition. Lunchtime witnesses multiple central clusters forming and gradually dispersing, while the evening peak witnesses population regrouping, covering broader geographical extents. Dynamic utilization of functional areas and mobile phone signaling data outperforms static population metrics, offering deeper insights into the complexities of human activity. (3) Between 12:00 and 13:00, lunchtime movements lead to a surge of 6 high-risk zones in the central area and 5 in the Meiling area. The dynamic flood risk assessment model, based on functional area delineation, effectively identifies disparities and fluctuations in flood risk across diverse functional areas during rainfall scenarios, ensuring heightened precision and accuracy in risk assessment.

Study on the spatial variability of thermal landscape in Xi’an based on OSM road network and POI data

As the primary contributor to the urban heat island effect, the construction land can be used to understand the spatial variation characteristics of the thermal landscape within the city at the microscopic scale. Taking the main urban area of Xi’an as the study area, this paper divides the minimum urban land unit by using OpenStreetMap (OSM) road network data and employs the kernel density analysis method based on Point of interest (POI) data to construct seven types of urban functional blocks. Furthermore, this paper also establishes a thermal landscape footprint characterization model to investigate the impact range of thermal landscape footprint for various types of functional blocks and quantitatively evaluate the spatial variation characteristics of urban thermal landscape, which is of great significance to the enhancement of urban ecological environment. The study indicates that: (1) The spatial distribution of urban functional blocks presents highly coupled characteristics with POI kernel density. (2) The surface thermal landscapes of seven types of urban functional blocks are predominantly medium-temperature and sub-high-temperature pixels, with the mean values of thermal fields ranked as logistics and storage blocks > industrial development blocks > transportation hub blocks > comprehensive service blocks > residential and living blocks > commercial and business blocks > strategic reserved blocks. (3) Apart from the strategic reserved blocks, the remaining urban functional blocks can produce thermal diffusion phenomena to the surrounding areas, and their thermal landscape footprints are obviously differentiated, with their influence ranges ranked as industrial development blocks > logistics and storage blocks > comprehensive service blocks > residential and living blocks > commercial and business blocks > transportation hub blocks. The findings of the study can provide scientific guidance for both the enhancement of urban ecological environment as well as the rational planning and layout of the city.