POI Data Research Articles

Spatial accessibility of medical facilities based on green travel mode - Take the central urban area of Wuhan as an example

Based on the concept of “people-oriented”, this paper simulates the green travel mode of Wuhan residents in the main urban area by walking and public transportation (bus and rail) based on the public transportation network and walking network of Wuhan city, combined with the POI data of residential community points and medical public service facilities. ArcGIS network analysis technology was used to quantitatively analyze the spatial accessibility distribution characteristics and qualitatively analyze the problems of medical service facilities in the main city of Wuhan. The spatial accessibility distribution characteristics of the three types of medical service facilities (General hospital, Specialist hospital and community hospital) in Wuhan city were quantitatively and qualitatively analyzed. The conclusions are as follows: under the different green travel mode (walk, by bus or take the subway), the spatial accessibility of the three types of medical facilities in the central urban area of Wuhan is good. The areas with the spatial accessibility of the three types of medical facilities within 15min account for most of the central urban area of Wuhan. However, the spatial accessibility of the three types of medical facilities shows obvious imbalance in the spatial distribution characteristics. The spatial accessibility of three medical facilities in the west of Jianghan district, the north of Jiangan district, the east of Wuchang district and the middle of Hongshan district is poor. And the spatial accessibility in these areas is more than 15min. There are distinct differentiating characteristics in areas with good and bad spatial accessibility. Based on the above analysis results, suggestions and countermeasures are put forward to improve the spatial accessibility of medical public service facilities in the central city of Wuhan.

Demography-Oriented Urban Spatial Matching of Service Facilities: Case Study of Changchun, China

People-oriented urban planning requires that service facilities should efficiently meet individual and community activity needs across the demographic landscape that defines a city. To develop a conceptual basis for urban spatial infrastructure optimization, we empirically studied existing population activities and service facilities in Changchun, China, using kernel density estimation, bivariate spatial autocorrelation analysis, and other models. The spatial relationships we derived from multiple sources of big data such as mobile phone signaling and POI data indicated that the intensity of population activity has obvious temporal regularity, and its spatial distribution is “center-periphery.” Service facilities display a “One main and two subs” distribution with no obvious spatial dependence between the core’s density and diversity. Population activities and service facility diversity show a high-high spatial correlation and multiple matching patterns. At the same time, a certain degree of spatial mismatch between different age groups and service facilities was also observed. Our research suggests several urban renewal actions to rectify this mismatch, such as: decentralizing the core area medical service facilities; reducing the attractiveness of the core area and its traffic pressure; and renewing and renovating old facilities to reduce construction costs. At a government planning level, construction along the periphery of the urban can enrich the diversity of its service facilities to improve the efficiency of spatial allocation.

A GloVe Model for Urban Functional Area Identification Considering Nonlinear Spatial Relationships between Points of Interest

As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI linear location relationships and do not comprehensively mine POI spatial information, such as spatial interaction information. This results in less accurate modeling of the relationship between POI-based and urban function types. In addition, they all use Euclidean distance for proximity assessment, which is not realistic. This paper proposes an urban functional area identification method that considers the nonlinear spatial relationship between POIs. First, POI adjacency is determined according to road network constraints, which forms the basis of a co-occurrence matrix. Then, a Global Vectors (GloVe) model is used to train POI category vectors and the feature vectors for each basic research unit are obtained using weighted averages. This is followed by clustering analysis, which is realized by a K-Means++ algorithm. Lastly, the functional areas are labeled according to the POI category ratio, enrichment factors, and mobile phone signal heat data. The model was tested experimentally, using core areas of Zhengzhou City in China as an example. When the results were compared with a Baidu map, we confirmed that making full use of nonlinear spatial relationships between POIs delivers high levels of identification accuracy for urban functional areas.

High-resolution temporal and spatial evolution of carbon emissions from building operations in Beijing

Building, a main greenhouse gas source, accounts for approximately 38% of all carbon emissions, and whole-life-cycle carbon emissions from building operations account for 60%–80% of construction-related emissions. However, the high-resolution spatiotemporal distribution of building-operation carbon emissions is unclear. We developed a top-down method to calculate and map carbon emissions by (1) compiling a building-operations carbon-emission inventory that includes direct energy consumption and calculating the carbon emissions of four key energy-intensive sectors via an emissions-factor approach; (2) cleaning, reclassifying and assigning weights to POI data obtained from AutoNavi; and (3) using a GIS to obtain sectoral carbon emissions in each grid according to the POI weights and to overlay and map the total carbon emissions. In Beijing, overall carbon emissions peaked at 95.87 million tons (2012). Emissions showed a clustered core with a multicenter discrete radial distribution pattern. Additionally, the carbon emissions intensity peaked at 487.44 kg/m2 (2015). The per-unit-grid carbon emissions distribution range was generally normally distributed, and the range of 103–105 kg/grid had the widest distribution and largest number of associated grids. Southeastern carbon emissions hotspots developed, and cold spots appeared along the periphery. This research could help planners quickly zone and reduce carbon emissions, leading to improved future development strategies.

Classification of Industrial Heat Source Objects Based on Active Fire Point Density Segmentation and Spatial Topological Correlation Analysis in the Beijing–Tianjin–Hebei Region

The development of industrial infrastructure in the Beijing–Tianjin–Hebei(BTH) region has been accompanied by a disorderly expansion of industrial zones and other inappropriate development. Accurate industrial heat source classification data become important to evaluate the policies of industrial restructuring and air quality improvement. In this study, a new classification of industrial heat source objects model based on active fire point density segmentation and spatial topological correlation analysis in the BTH Region was proposed. First, industrial heat source objects were detected with an active fire point density segmentation method using NPP-VIIRS active fire/hotspot data. Then, industrial heat source objects were classified into five categories based on a spatial topological correlation analysis method using POI data. Then, identification and classification results were manually validated based on Google Earth imagery. Finally, we evaluated the factors influencing the number of industrial heat sources based on an OLS regression model. A total of 493 industrial heat source objects were identified in this study with an identification accuracy of 96.14%(474/493). Compared with results for nighttime fires, the number of industrial heat source objects that were identified was higher, and the spatial coverage was greater; the minimum size of the detected objects was also smaller. Based on the function of the identified industrial heat source objects, the objects in the BTH region were then divided into five categories: cement plants (21.73%), steel plants (53.80%), coal and chemical industry (12.66%), oil and gas developments (7.81%), and other (4.01%). An analysis of their operations showed that the number of industrial heat source objects in operation in the BTH region tended to first rise and then decline during the 2012–2021 period, with the peak being reached in 2013. The results of this study will aid the rationalization of industrial infrastructure in the BTH region and, by extension, in China as a whole.

Construction of Ideological and Political Practice Teaching System of Documentary Creation Course Based on Deep Learning

In order to improve the effect of landscape design, a landscape design method based on distributed integrated model is proposed. Based on the spatial location information, ancient books and documents, POI data, and text data of garden landscape, a multisource distributed data distribution model of garden landscape design is established. Extract the word frequency of keywords to calculate the garden heat, and analyze the pattern and changes of garden landscape according to the landscape index. The distribution and integration model of landscape design is established, which takes history and culture, landscape ecology, tourism, and health as the control object parameters, and the spatial relationship between landscape and its adjacent areas is studied by image integration. The virtual three-dimensional visual reconstruction method is used to optimize the landscape design and image processing. The simulation test results show that the distributed integration ability of landscape design using this method is better, and the visual performance effect is better, which improves the visual expression effect and design quality level of landscape design.

Identification of Urban Agglomeration Spatial Range Based on Social and Remote-Sensing Data—For Evaluating Development Level of Urban Agglomeration

The accurate identification of urban agglomeration spatial area is helpful in understanding the internal spatial relationship under urban expansion and in evaluating the development level of urban agglomeration. Previous studies on the identification of spatial areas often ignore the functional distribution and development of urban agglomerations by only using nighttime light data (NTL). In this study, a new method is firstly proposed to identify the accurate spatial area of urban agglomerations by fusing night light data (NTL) and point of interest data (POI); then an object-oriented method is used by this study to identify the spatial area, finally the identification results obtained by different data are verified. The results show that the accuracy identified by NTL data is 82.90% with the Kappa coefficient of 0.6563, the accuracy identified by POI data is 81.90% with the Kappa coefficient of 0.6441, and the accuracy after data fusion is 90.70%, with the Kappa coefficient of 0.8123. The fusion of these two kinds of data has higher accuracy in identifying the spatial area of urban agglomeration, which can play a more important role in evaluating the development level of urban agglomeration; this study proposes a feasible method and path for urban agglomeration spatial area identification, which is not only helpful to optimize the spatial structure of urban agglomeration, but also to formulate the spatial development policy of urban agglomeration.

Fitness Culture and Green Space Equity: Accessibility Evaluation of Shanghai Communities

In China, sports parks, and green spaces are often spatially integrated to realize the multiple functions of shared green spaces and play an important role in the production and living services of its residents. In this article, it is collectively referred to as green sport space (GSS). Whether the distribution of GSS is equal has an important impact on the sustainable lifestyle and the rehabilitation under the pandemic. Based on the POI data of the Shanghai urban area, it is preliminarily found that the areas with extremely high and high production and living densities are mainly distributed in downtown Shanghai. Polarization of the GSS distribution area and the high heat of points of interest can be seen. When the service radius of the GSS in Shanghai is 500, 750, and 1,000 m, the green space ecological service area can reach 2089.08, 3164.62, and 4469.75 km2, covering 26.17, 39.64, and 55.99% of the total area, respectively. The coverage for walking accessibility of GSSs in Shanghai is extremely uneven. Based on network analysis, the overall accessibility of GSS under the walking mode in each residential district fails to meet the standard of a 15-min living circle, with an average of 15.37 min. The evaluation results of this plan demonstrate that Shanghai needs to further provide GSS space for the public in future to improve public wellbeing and diversify sports spaces.

RESEARCH ON THE INFLUENCE FACTORS OF FRESH STORE LAYOUT ON CONSUMERS' BEHAVIOR IN BEIJING

BackgroundIn recent years, the development of online retail has faced difficulties, intensified competition and reduced dividends. At the same time, the development situation of offline retail is still grim, retail enterprises are forced to transform, and the new retail format of deep integration of online and offline has developed rapidly in China. Fresh hippo is its typical representative. It implements the sales model of “supermarket + catering”, and stores provide fresh retail, processing and takeout services. Studying the spatial layout of fresh HEMA store can provide a scientific reference for the location of new retail enterprises. This study examines the influence mechanism of consumer emotion and emotion from the perspective of consumer psychology.Subjects and methodsUsing the POI data of 2021 Beijing fresh blood horse store, the spatial distribution of fresh blood horses was described by standard deviation ellipse analysis, buffer analysis, kernel density estimation and average nearest neighbor index. Combined with consumer psychology, this paper discusses the factors affecting their location choice, and uses binary logistic model to verify it. At the same time, in order to verify the impact of spatial layout on consumers' emotion, this study uses relevant scales to investigate. (1) Positive emotion scale. The Panas emotion scale developed by Wason and others is widely used to measure emotion. The scale includes two dimensions of positive emotion and negative emotion as measurement indicators. There are 6 questions in this dimension, which are scored by Likert 5 points (1 means “very inconsistent”, 5 means “very consistent”, the same below). In this study, the clonbach coefficient of the questionnaire is 0.90. (2) Motivation scale. The topic of measuring motivation mainly refers to the entrepreneurial motivation scale compiled by Phan, which has 8 questions and adopts Likert's 5-point scoring. (3) Social support scale. The scale is adapted from the social support scale prepared by Ye Yuemei and others. It has 8 questions and is scored by Likert 5 points. Clone Bach of the scale α the coefficient is 0.87. (4) Behavioral propensity scale. Entrepreneurial orientation dimension in intention measurement [4]. The scale has 6 questions and is scored by Likert 5 points. The clonbach coefficient of the scale is 0.95.ResultsThe fresh blood horse shop was distributed in the Northeast southwest direction around the center of Beijing, with a low high low radial distribution; Its high-value core density area is located between the second ring road and the Fourth Ring Road in Beijing, close to large commercial areas or communities, presenting a multi center structure. The distribution of stores is mutually exclusive and scattered. In urban areas, the location of stores is greatly affected by the population, subway traffic environment and store rent level, while the distribution of traditional retail competitors has little impact, which can meet the convenience needs of consumersConclusions“Supermarket + catering” new retail stores should increase the layout of residents' activity places such as main business districts and office buildings, and launch convenience stores that more meet the needs of the site; Strengthen the layout of the suburbs and reduce the operation cost of stores while occupying the new retail market in the suburbs; Increase the layout in areas with convenient transportation, take advantage of transportation advantages, expand the distribution scope, consider the distribution of competitors in space, and avoid excessive competition, so as to optimize the layout of stores and expand enterprise profits. The suggestions of this paper can not only provide reference for the spatial layout and location improvement of “supermarket + catering” new stores, but also provide reference for the layout of other new stores, which has certain practical significance. However, the research scope of this paper is relatively small. This will be improved in future research. In short, the important influence of spatial layout design factors on consumers' emotions should be fully taken into account, the changes of their psychological emotions and behaviors and their impact on positive and active behaviors should be actively concerned, and efforts should be made to tune builders, managers, coordinators and collaborators.AcknowledgmentsSupported by a project grant from the key project of Beijing Municipal Education Commission’s Social Science Program “Research on the Reasonable Utilization of Space for Evacuation and Retreat in Beijing” (SZ201910011004), the key project of the National Social Science Fund “Study on the extended trend, structural causes and countermeasures of currency fluctuations in the new era”; and Beijing Philosophy and Society Phased Achievements of Scientific Capital Circulation Industry Research Base (JD-YB-2021-40).

Disparities in spatio-temporal accessibility to fresh foods in Shanghai, China

Disparities in fresh food accessibility are an important topic in health geography. Existing studies focus on the spatial component of food accessibility and are mostly conducted in North America and Europe. This study addresses both the spatial and temporal dimensions of food accessibility and investigates the disparities in fresh food accessibility in the Asian context. We examine the impacts of socioeconomic and built environment factors on spatio-temporal accessibility to supermarkets and wet markets in Shanghai. The opening hours of markets and people's daily time use patterns are considered. Both first-hand questionnaire survey data and second-hand POI and statistic data are used. We find that Shanghai's urban districts have a slightly higher density of wet markets than the suburban districts, but the latter have a much higher density of supermarkets than the former. The built environment at home and workplace is found to be more influential than socioeconomic variables in explaining disparities in fresh food accessibility. Household income, age and private car ownership are associated with fresh food accessibility. The findings of this study enrich the existing literature on food environment and food accessibility and may help policymakers address the deficiency in fresh food accessibility.

IDENTIFICANDO CENTRALIDADES E SUBCENTRALIDADES NA REGIÃO METROPOLITANA DE BELO HORIZONTE ATRAVÉS DO GOOGLE PLACES OF INTEREST

This research provides novel exploration of the distribution of urban facilities in the Metropolitan Region of Belo Horizonte, to identify primary and secondary centralities. The main findings are based on a Kernel Density, using Google Places of Interest data. Primary and secondary metropolitan centers are detected in the largest municipalities in the Metropolitan Area, as well as smaller suburban subcenters in peripheral municipalities. Moreover, the analysis of five categories of facilities illustrates different centralities structures and inequalities in the distribution of different types of activities. Keywords: Urban Sprawl, Post-Suburbia, Centralities, POI Data.

A Big Data-Based Commuting Carbon Emissions Accounting Method—A Case of Hangzhou

Commuting carbon emissions are an essential component of urban carbon emissions, and determining how to reduce them is an area of great debate among researchers. The current research lacks a tool and instrument that can extensively account for residents’ commuting. Traditional methods are mainly based on questionnaire surveys, which have low accuracy at spatial and temporal aspects. High accuracy carbon emission accounting methods can effectively assist urban planning and achieve precise urban emissions reductions. This study applies a taxi commuting carbon emissions accounting method divided into two main steps. Firstly, the carbon emissions of taxi trajectories are calculated using taxi trajectory data and a carbon emission calculation method developed based on VSP. Secondly, the taxi trajectory and POI data are used to filter the commuter trajectory with the help of a two-step moving search method. In this way, the taxi commuting carbon emissions were obtained. Then, the spatial distribution characteristics of residential taxi commuting carbon emissions are analysed by spatial autocorrelation tools, which could facilitate low carbon zoning management. A typical working day in Hangzhou was selected as the research object of this study. The results show that (1) morning peak commuting carbon emissions in the main urban area of Hangzhou reached 2065.14 kg per hour, accounting for 13.73% of all taxi travel carbon emissions; and evening peak commuting carbon emissions reached 732.2 kg per hour, accounting for 4% of all taxi travel carbon emissions; (2) At the grid level, the spatial distribution of commuting carbon emissions in Hangzhou shows a single central peak that decays in all directions; and (3) The results at the resident community scale show that urban public transport facilities influence resident community commuting carbon emissions. In areas such as at the urban-rural border, resident community commuting carbon emissions show high levels of aggregation, and in the main urban area, resident community commuting carbon emissions show low levels of aggregation. This study not only provides a new method of commuting investigation but also offers constructive suggestions for future carbon emission reduction under Hangzhou’s urban planning.

Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China

Urban agglomeration is a higher stage of urban development. Exploring the spatial correlation of functional areas is important for promoting high-quality urban development. However, recently the research on urban functional areas is mainly focused on how to identify urban functional areas, and they lack some methods to analyze the spatial correlation patterns of urban functional areas. Therefore, firstly, this study uses POI data and a deep learning model to identify the urban functional areas of four typical urban agglomerations in China. Then, we create a new method to mine the spatial correlation patterns of urban functional areas from two levels (city and cities in one urban agglomeration). Moreover, we find that various graphs well express the spatial correlation patterns. Based on the above, we establish a new technical process for mining the spatial correlation of urban functional areas. The main conclusions are as follows: (1) The multilayer detailed division of the functional area is helpful to mine the spatial correlation pattern of the functional area. (2) The rank of each city in the urban agglomeration can be divided according to the urban functional area; there are great differences in richness and scale of the mixed-functional areas in the urban agglomeration, but there is little difference among the urban agglomerations. (3) The spatial correlation patterns of the functional areas in the first-rank cities of each urban agglomeration area are highly similar. (4) There is a certain spatial correlation pattern of functional areas in Chinese urban agglomerations. (5) There are great differences in the similarity of spatial correlation patterns between cities in one urban agglomeration, and the spatial relationship of similarity may not surround the most developed cities. This research will help urban planners to develop functional areas in different cities.

Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion

Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population migration data based on wavelet transform, then identified the polycentric spatial structure of the GBA by carrying out cluster and outlier analysis, and evaluated the level of different urban centers byconducting geographical weighted regression analysis. Using data fusion, we identified 4579.81 km² of the urban poly-center area in the GBA, with an identification accuracy of 93.22%. Although the number and spatial extent of the identified urban poly-centers are consistent with the GBA development plan outline, the poly-center level evaluation results are inconsistent with the development plan, which shows there are great differences in actual development levels among different cities in the GBA. By identifying and grading the polycentric spatial structure of the GBA, this study accurately analyzed the current spatial distribution and could provide policy implications for the GBA’s future development and planning.

Food Retail Network Spatial Matching and Urban Planning Policy Implications: The Case of Beijing, China

Food is the core of urban daily life and socio-economic activities but is rarely the focus of urban planning. The spatial layout of food retail outlets is important for optimizing the urban food system, improving land resource allocation, and encouraging healthy food consumption. Based on food retail POI data, this study employed kernel density estimation, road network centrality, spatial autocorrelation analysis, and locational entropy to analyze the spatial characteristics of supermarkets, produce markets, and small stores in an urban center in Beijing, and explored street coupling and supply-demand matching. The results indicated that within the study area: (1) supermarkets had an obvious “core-periphery” distribution, produce markets had a polycentric distribution, and small stores had a relatively uniform distribution; (2) road network centrality indices revealed a differentiated multi-core-edge distribution; (3) streets with high locational entropy values for supermarkets and produce markets were mostly concentrated in the central area, whereas the matching distribution of small stores was relatively balanced. From the perspective of urban planning, policy implications are proposed based on spatial and social equity, urban-rural differences, population structure and distribution status, and a resilient supply chain. The study findings have practical significance for guiding the development of urban food systems in a healthy, just, and sustainable direction, as well as rational urban land planning.

Exploring the Evolution of the Accessibility of Educational Facilities and Its Influencing Factors in Mountainous Areas: A Case Study of the Rocky Desertification Area in Yunnan, Guangxi, and Guizhou

The optimal allocation of educational resources has been a hot issue, and exploring the accessibility of educational facilities in poor mountainous areas helps to reasonably plan the layout of educational facilities and promote the balanced development of education. Taking the rocky desertification area in Yunnan, Guangxi, and Guizhou (YGGRD) as the study area, based on the POI data of educational facilities in the YGGRD in 2000, 2010 and 2019, this study explored the evolution of the accessibility of educational facilities in the YGGRD through raster accessibility. And the influencing factors were analyzed by the ordinary least square method (OLS) and geographically weighted regression model (GWR), and evaluated the model through cross validation. The results show that the overall accessibility of educational facilities improved significantly from 2000 to 2019. Educational facilities mainly have good accessibility and average accessibility. Poor accessibility areas are concentrated in the interprovincial border regions, and the boundary effect is significant. County accessibility, population density and rural per capita disposable income have a great impact on the accessibility of educational facilities in the YGGRD. It is suggested to strengthen the construction of educational facilities in the interprovincial border regions, relocate and integrate villages, and improve the education quality of township schools to improve the supply of rural educational resources.

Research on the Spatial Agglomeration Characteristics and Influencing Factors of Express Delivery Station Based on DNN.

In this paper, the POI data of 736 Cainiao stations in Nanjing is taken as the research sample. With the help of ArcGIS software, the standard deviation ellipse, spatial autocorrelation, average nearest neighbor, cold and hot spot analysis, nuclear density estimation, and other spatial analysis models are used to quantitatively characterize its business mode, spatial distribution characteristics, and equilibrium. Based on DNN, the spatial agglomeration characteristics and distribution directions of the Cainiao station in Nanjing were sorted out, the cold spots and hot spots of the spatial layout were identified, and the spatial differentiation rules and agglomeration patterns were revealed. Finally, the geographically weighted regression analysis model is used to analyze the influencing factors of the spatial agglomeration of the Cainiao station in Nanjing. The research found that Firstly, the proportion of Nanjing Cainiao station operating mode adopting the exclusive mode is 59.1%, the proportion adopting the concurrent operation mode is 33.7%, and the rest adopting the joint operation mode of cooperation with other logistics enterprises. Secondly, Nanjing Cainiao Station gathers in the central city area, forming a “central hot spot.” The urban fringe area does not form a “peripheral cold spot area,” and the whole presents a “1 + 4” five-core agglomeration model across the river. Thirdly, Regional GDP, population density, and the number of convenience stores/supermarkets are the main factors affecting the spatial agglomeration of the Cainiao station in Nanjing.

Research on Location Characteristics of UCF Layout Based on DNSCAN Algorithm

Since the reform and opening up, the prosperity of culture has always been the strong desire of the party and the country, and repeatedly put forward the goal of improving the public cultural service system (PCSS), requiring priority development of infrastructure construction related to the vital cultural interests of the people. Urban cultural facilities (UCF) are an important material basis in the cultural service system. The essential assurance for achieving the goal of developing a PCSS that covers the entire community is to improve the layout of cultural facilities (CF) and to allocate CF in a scientific and reasonable manner. This research examines the geographical layout characteristics of CF in Xiamen using POI data from libraries, art galleries, and museums crawled by Baidu API and spatial statistical tools such as ArcGIS. Taking the interest points of cultural promote serving residents as research objects, the advancement of spatial pattern of CF in Zhengzhou in 2007 and 2017 was quantitatively analyzed by using standard deviation ellipse and kernel density analysis. The paper compares and analyzes the location distribution characteristics of museums, libraries, cultural centers, art galleries and theaters from two aspects of macro spatial form and microgathering center. Residents’ behavioral characteristics are introduced to examine the influence on the spatial form of CF, and appropriate suggestions are made for the planning and development of CF in Zhengzhou, based on large-scale field survey data and geographical detectors. This article, on the analysis of the state and international public facilities location layout on the basis of optimization study, starting from the present circumstances of urban public sports facilities layout, puts forward the geographic information system (GIS) technology and local approximation (LA) model is introduced into urban public sports service facility location, using DNSCAN network analysis function of urban public sports facilities in site selection and layout optimization, Provide a new method for site selection of sports facilities. And the actual case analysis, in order to provide a reference for the future location layout of public service facilities. Zhengzhou cultural resources through the establishment of GIS database, investigation, cultural needs, put forward the planning should not only include public CF, CF planning should also include the cultural heritage, cultural creative industry, and put forward the development strategy of the construction of the overall urban culture network, realize the cultural space planning and career planning, the fusion of the culture industry planning.

Modelling people’s perceived scene complexity of real-world environments using street-view panoramas and open geodata

Scene complexity refers to the difficulty of human perception and understanding of the specific environment. An environment is complex when it has many parts or components, and those parts or components interrelate with each other in multiple and random ways. People’s daily behaviors and spatial activities are often influenced by such complexity of real-world environments. The existing computational methods on modelling the perceived scene complexity of humans primarily focus on either visual or structural characteristics of the environment. This work presents a computational method to quantify the scene complexity of real-world environments comprehensively based on the visual, structural, and semantic characteristics, and assesses the performance of the technical approach with human-labelled “ground-truth” data. Specifically, we proposed a set of features to model the visual (e.g. color, shape, texture, and field of vision), structural (e.g. branches and nodes), and semantic (e.g. the number of POIs and their spatial patterns, well-known signs) aspects, based on street-view panoramas, road network data, POI data, and building footprint data. The results of the evaluation show that the proposed computational method is feasible to predict the perceived scene complexity of street-view environments, with an excellent Mean Absolute Error (MAE) of 0.1108 (on the scale of 1 to 5). The evaluation results on two additional cities further illustrate the high robustness of the proposed computational method. Regarding all conceivable combinations of the visual, structural, and semantic dimensions, considering all these three dimensions provides the best regression performance. In addition, the top-4 most important features for the modelling of scene complexity were: Spatial distribution of POIs, Number of POIs, Percentage of visible sky area, and Distance to the nearest street intersection. Interestingly, these 4 features all appeared in the top-5 feature list reported by human participants in the empirical studies. The employment of openly available data sources makes the proposed method widely applicable to many different cities in the whole world.

Roadmap to hydrogen society of Tokyo: Locating priority of hydrogen facilities based on multiple big data fusion

Hydrogen socialization has become a promising target in future energy development. Hydrogen fuel cell vehicle is gradually becoming a considerable substitute for fossil fuel vehicles in many countries. As for the promotion of hydrogen fuel cell vehicles, The construction of facilities like hydrogen refueling stations and depots should be strengthened. The research provides a case study of locating the priority of the construction of hydrogenation facilities, based on policy regulation, massive GPS trajectory records, geographical land use data, road network and, POI data. The PageRank value in the complex network theory is employed to estimate the priority of the Land use Unit and policies regulation is referred to to ensure the feasibility of construction. Among 24,595 Land Use Units in sum, 59.78% of them are allowed to build hydrogen facilities. The results show that the available location of hydrogen refueling stations with high priority (we pick the first 100 for analysis) and feasibility in policy are geographically clustered around the city centers and sub-centers. The place with the highest PageRank value of 0.0033 is Haneda airport, which is an important transportation hub. The PageRank values of most of the other first 100 ranked Land Use Units are under 0.001. This indicates that the vehicle transportation network is highly centralized. There is an obvious sequence of priority of hydrogen refueling stations. We furtherly provide sensitivity analysis and it shows that higher-ranked places are spatially clustered. The best hydrogen depot is located near Tokyo Bay with the advantage of low transportation cost of liquid hydrogen, convenient transportation conditions, strong safety assurance. After verification, the results showed consistency with the current hydrogenation facility construction process and demonstrated the potential for future hydrogenation facility construction.