Study on the exposure risk based on the PM<sub>2.5</sub> pollution characteristics of POIs and their attractiveness to the crowd

Shi, Jianhan; Gao, Huaxi; Cheng, Hongguang; Sun, Haixu; Huang, Di

doi:10.1080/10807039.2020.1789841

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Unlike traditional data, the open-source map platform, the so-called volunteered geographic information (VGI) platform, covers multiple basic data such as road networks and building outlines, and has the advantages of open editing, such as Open Street Map (OSM) (Haklay, 2010, Geiß et al, 2017. POI reflects format information in various regions of the city and has the advantage of data mining based on spatial location (Hu et al, 2020, Chen et al, 2020, Shi et al, 2021. SVI has a wide coverage and detailed illustration of urban street, intuitively reflects the urban façade information, and has the advantage of lower cost than on-site data collection, provide a large sample data source and new research ideas for urban environmental assessment.…”

Section: New Data Environment Provides Support For Multi-scale Urban ...mentioning

confidence: 99%

Survey of Urban Spatial Quality Research under New Data and New Technology Environment

Yang¹,

Jia²,

Meng³

2021

Proceedings of the 57th ISOCARP World Planning Congress

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Urban space is an important part of people's life, and its spatial quality is essential to people's activities and behaviors. The emerging new data and new technologies improve traditional research methods in urban space significantly. This paper reviews, analyzes and summarizes new data and new technologies that have emerged in recent years, including build environment data, social media data, trajectory data, deep learning, virtual reality, eye tracking, and physiological sensor. This paper also analyzes the possibility of more precise analysis and more intuitive presentation of these new data and new technologies in urban spatial research methods. Finally, we propose a framework to response the possible application of these new data and new technologies in the process of urbanization.

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Section: New Data Environment Provides Support For Multi-scale Urban ...mentioning

confidence: 99%

Survey of Urban Spatial Quality Research under New Data and New Technology Environment

Yang¹,

Jia²,

Meng³

2021

Proceedings of the 57th ISOCARP World Planning Congress

View full text Add to dashboard Cite

show abstract

“…Many researchers have studied the relationship between areas of interest and air quality; in particular, they have investigated the integration of low-cost air quality monitoring Internet of Things systems and air quality big data models [ 25 , 26 , 27 , 28 , 29 , 30 ]. Over the past few years, some Chinese researchers have analyzed the air pollution characteristics of certain specific tourist attractions [ 31 , 32 ]. However, the relationship between the overall tourist attractions and air quality has not been studied.…”

Section: Introductionmentioning

confidence: 99%

Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan

Lin

Shih

Lai

et al. 2020

IJERPH

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In the past few years, human health risks caused by fine particulate matters (PM2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan’s Government enforced in 2017, “suspended particulate matter” has officially been acknowledged as a disaster-causing hazard. The long-term exposure to high concentrations of air pollutants negatively affects the health of citizens. Therefore, the precise determination of the spatial long-term distribution of hazardous high-level air pollutants can help protect the health and safety of residents. The analysis of spatial information of disaster potentials is an important measure for assessing the risks of possible hazards. However, the spatial disaster-potential characteristics of air pollution have not been comprehensively studied. In addition, the development of air pollution potential maps of various regions would provide valuable information. In this study, Hsinchu County was chosen as an example. In the spatial data analysis, historical PM2.5 concentration data from the Taiwan Environmental Protection Administration (TWEPA) were used to analyze and estimate spatially the air pollution risk potential of PM2.5 in Hsinchu based on a geographic information system (GIS)-based radial basis function (RBF) spatial interpolation method. The probability that PM2.5 concentrations exceed a standard value was analyzed with the exceedance probability method; in addition, the air pollution risk levels of tourist attractions in Hsinchu County were determined. The results show that the air pollution risk levels of the different seasons are quite different. The most severe air pollution levels usually occur in spring and winter, whereas summer exhibits the best air quality. Xinfeng and Hukou Townships have the highest potential for air pollution episodes in Hsinchu County (approximately 18%). Hukou Old Street, which is one of the most important tourist attractions, has a relatively high air pollution risk. The analysis results of this study can be directly applied to other countries worldwide to provide references for tourists, tourism resource management, and air quality management; in addition, the results provide important information on the long-term health risks for local residents in the study area.

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Exploring the Relationship between the Spatial Distribution of Different Age Populations and Points of Interest (POI) in China

Huang

Lin

Zhang

et al. 2022

IJGI

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Population spatialization data is crucial to conducting scientific studies of coupled human–environment systems. Although significant progress has been made in population spatialization, the spatialization of different age populations is still weak. POI data with rich information have great potential to simulate the spatial distribution of different age populations, but the relationship between spatial distributions of POI and different age populations is still unclear, and whether it can be used as an auxiliary variable for the different age population spatialization remains to be explored. Therefore, this study collected and sorted out the number of different age populations and POIs in 2846 county-level administrative units of the Chinese mainland in 2010, divided the research data by region and city size, and explored the relationship between the different age populations and POIs. We found that there is a complex relationship between POI and different age populations. Firstly, there are positive, moderate-to-strong linear correlations between POI and population indicators. Secondly, POI has a different explanatory power for different age populations, and it has a higher explanatory power for the young and middle-aged population than the child and old population. Thirdly, the explanatory power of POI to different age populations is positively correlated with the urban economic development level. Finally, a small number of a certain kinds of POIs can be used to effectively simulate the spatial distributions of different age populations, which can improve the efficiency of obtaining spatialization data of different age populations and greatly save on costs. The study can provide data support for the precise spatialization of different age populations and inspire the spatialization of the other population attributes by POI in the future.

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Study on the exposure risk based on the PM_2.5 pollution characteristics of POIs and their attractiveness to the crowd

Cited by 3 publications

References 33 publications

Survey of Urban Spatial Quality Research under New Data and New Technology Environment

Survey of Urban Spatial Quality Research under New Data and New Technology Environment

Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan

Exploring the Relationship between the Spatial Distribution of Different Age Populations and Points of Interest (POI) in China

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Study on the exposure risk based on the PM2.5 pollution characteristics of POIs and their attractiveness to the crowd

Cited by 3 publications

References 33 publications

Survey of Urban Spatial Quality Research under New Data and New Technology Environment

Survey of Urban Spatial Quality Research under New Data and New Technology Environment

Investigating a Potential Map of PM2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan

Exploring the Relationship between the Spatial Distribution of Different Age Populations and Points of Interest (POI) in China

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Study on the exposure risk based on the PM_2.5 pollution characteristics of POIs and their attractiveness to the crowd