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Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.
Ting Chan; Linyuan Xia; Yimin Chen; Wei Lang; Tingting Chen; Yeran Sun; Jing Wang; Qianxia Li; Ruxu Du. Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage. Sensors 2021, 21, 1228 .
AMA StyleTing Chan, Linyuan Xia, Yimin Chen, Wei Lang, Tingting Chen, Yeran Sun, Jing Wang, Qianxia Li, Ruxu Du. Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage. Sensors. 2021; 21 (4):1228.
Chicago/Turabian StyleTing Chan; Linyuan Xia; Yimin Chen; Wei Lang; Tingting Chen; Yeran Sun; Jing Wang; Qianxia Li; Ruxu Du. 2021. "Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage." Sensors 21, no. 4: 1228.
The general transit feed specification is becoming a popular data format for the publication of public transport schedules, making possible the collection of a nation-wide public transport schedule dataset, which enables monitoring of transit supply at an up-to-date and more precise level across a country than previously possible. In this paper, we use general transit feed specification data to measure local-scale public transport availability across England based on service frequency and spatial proximity to public transport stops/stations. Moreover, to demonstrate the usefulness of public transport availability measures, we examine inequalities of public transport provision and identify areas at risk of transport poverty across England. Furthermore, we estimate population (number of households) who are likely to suffer from transport poverty, accounting for public transport availability, time-based job accessibility by public transport or walking, household income and car ownership levels. Based on the criteria, we have used to identify public transport risk, we find that investment in the development of public transport services should prioritise West Midlands, East of England, South East and South West as those regions have more households who are likely to suffer from transport poverty. This paper contributes by (1) defining more comprehensive transit availability measures than existing measures at a variety of geography levels and (2) integrating fours aspects (i.e. public transport availability, job accessibility by public transport or walking, household income and car availability) to analyse transport poverty comprehensively.
Yeran Sun; Piyushimita (Vonu) Thakuriah. Public transport availability inequalities and transport poverty risk across England. Environment and Planning B: Urban Analytics and City Science 2021, 1 .
AMA StyleYeran Sun, Piyushimita (Vonu) Thakuriah. Public transport availability inequalities and transport poverty risk across England. Environment and Planning B: Urban Analytics and City Science. 2021; ():1.
Chicago/Turabian StyleYeran Sun; Piyushimita (Vonu) Thakuriah. 2021. "Public transport availability inequalities and transport poverty risk across England." Environment and Planning B: Urban Analytics and City Science , no. : 1.
Environmental factors have both direct and indirect impacts on crime behavior decision making. This study aimed to examine to what degree the occurrences of violent crimes can be affected by social and built environment over space. Although a few studies have attempted to model crime rate using spatial regression models, there is a lack of comparison of spatial regression models. Particularly, the eigenvector spatial filtering type of spatial regression models has reportedly been effective in urban and regional studies, but it has not been widely applied to crime data. In this study, we aimed to examine whether the spatial filtering type of spatial regression models outperforms conventional types of spatial regression models in modeling violent crime rates over space. Moreover, we aimed to investigate the impacts of land use mix and street connectivity on the occurrences of violent crimes as the routine activity theory explained. In empirical studies, two types of spatial regression models (i.e., spatial error model and eigenvector spatial filtering model) were selected and estimated successfully to model local-scale violent crime rates across New York City. The eigenvector spatial filtering models outperform the spatial error models as well as the nonspatial models. Model estimation results show that occurrences of violent crimes (i.e., assaults and robberies) can be well determined by socioeconomic and built environment factors and thereby environmental factors can affect the occurrences of violent crimes. The contributions of socioeconomic and built environment factors to violent crime can offer insights on urban planning and policymaking toward violent crime prevention. Particularly, this study offers new evidence on the routine activity theory that increasing land use mix and street connectivity can enhance street activity, thereby reducing occurrences of violent crimes. Policymakers and urban planners should continue to enhance street activity through increasing land use mix and street connectivity. In addition, eigenvector spatial filtering models are advocated for use in crime or other applications in urban and regional studies.
Yeran Sun; Shaohua Wang; Jing Xie; Xuke Hu. Modeling Local-Scale Violent Crime Rate: A Comparison of Eigenvector Spatial Filtering Models and Conventional Spatial Regression Models. The Professional Geographer 2021, 73, 312 -321.
AMA StyleYeran Sun, Shaohua Wang, Jing Xie, Xuke Hu. Modeling Local-Scale Violent Crime Rate: A Comparison of Eigenvector Spatial Filtering Models and Conventional Spatial Regression Models. The Professional Geographer. 2021; 73 (2):312-321.
Chicago/Turabian StyleYeran Sun; Shaohua Wang; Jing Xie; Xuke Hu. 2021. "Modeling Local-Scale Violent Crime Rate: A Comparison of Eigenvector Spatial Filtering Models and Conventional Spatial Regression Models." The Professional Geographer 73, no. 2: 312-321.
Residential self-selection is commonly viewed as a force confounding the association between greenness exposures and subjective wellbeing. This paper looks at this fundamental viewpoint by exploring the role of residential self-selection concerning natural environment from the perspective of family composition. Using a combination of individual survey and street view greenness data from Beijing, we find that individuals are less satisfied when they live in neighborhoods with limited exposure to greenness. Additional findings provide the evidence on the contextualized nature of greenness-wellbeing is dependent upon not just residential self-selection and its realization but also family composition characteristics such as the presence of school-age children.
Wenjie Wu; Yanwen Yun; Jingtong Zhai; Yeran Sun; Guanglai Zhang; Ruoyu Wang. Residential self-selection in the greenness-wellbeing connection: A family composition perspective. Urban Forestry & Urban Greening 2021, 59, 127000 .
AMA StyleWenjie Wu, Yanwen Yun, Jingtong Zhai, Yeran Sun, Guanglai Zhang, Ruoyu Wang. Residential self-selection in the greenness-wellbeing connection: A family composition perspective. Urban Forestry & Urban Greening. 2021; 59 ():127000.
Chicago/Turabian StyleWenjie Wu; Yanwen Yun; Jingtong Zhai; Yeran Sun; Guanglai Zhang; Ruoyu Wang. 2021. "Residential self-selection in the greenness-wellbeing connection: A family composition perspective." Urban Forestry & Urban Greening 59, no. : 127000.
COVID-19 containment policies are not only curbing the spread of COVID-19 but also changing human behavior. According to the routine activity theory, owing to lockdown, the closure of entertainment sites (e.g., pubs and bars), an increase in stay-at-home time, and an increase in police patrols are likely to influence chance of committing a crime. In this study, we aimed to further examine the spatial association of COVID-19 infection rate and crime rate. Particularly, we empirically validated the speculation that increase in COVID-19 cases is likely to reduce crime rate. In the empirical study, we investigated whether and how COVID-19 infection rate is spatially associated with crime rate in London. As the spatial data used are mainly areal data, we adopted a spatial regression mode (i.e., the “random effects eigenvector spatial filtering model”) to investigate the spatial associations after controlling for the socioeconomic factors. More specifically, we investigated the associations for all the four crime categories in three consequent months (March, April, and May of 2020). The empirical results indicate that 1) crime rates of the four categories have no statistically significant associations with COVID-19 infection rate in March; 2) violence-against-the-person rate has no statistically significant association with COVID-19 infection rate; and 3) robbery rate, burglary rate, and theft and handling rate have a statistically significant and negative association with COVID-19 infection rate in both April and May.
Yeran Sun; Ying Huang; Ke Yuan; Ting Chan; Yu Wang. Spatial Patterns of COVID-19 Incidence in Relation to Crime Rate Across London. ISPRS International Journal of Geo-Information 2021, 10, 53 .
AMA StyleYeran Sun, Ying Huang, Ke Yuan, Ting Chan, Yu Wang. Spatial Patterns of COVID-19 Incidence in Relation to Crime Rate Across London. ISPRS International Journal of Geo-Information. 2021; 10 (2):53.
Chicago/Turabian StyleYeran Sun; Ying Huang; Ke Yuan; Ting Chan; Yu Wang. 2021. "Spatial Patterns of COVID-19 Incidence in Relation to Crime Rate Across London." ISPRS International Journal of Geo-Information 10, no. 2: 53.
Urban vibrancy contributes towards a successful city and high-quality life for people as one of its vital elements. Therefore, the association between service facilities and vibrancy is crucial for urban managers to understand and improve city construction. Moreover, the rapid development of information and communications technology (ICT) allows researchers to easily and quickly collect a large volume of real-time data generated by people in daily life. In this study, against the background of emerging multi-source big data, we utilized Tencent location data as a proxy for 24-h vibrancy and adopted point-of-interest (POI) data to represent service facilities. An analysis framework integrated with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models is proposed to explore the spatiotemporal relationships between urban vibrancy and POI-based variables. Empirical results show that (1) spatiotemporal variations exist in the impact of service facilities on urban vibrancy across Guangzhou, China; and (2) GTWR models exhibit a higher degree of explanatory capacity on vibrancy than the OLS models. In addition, our results can assist urban planners to understand spatiotemporal patterns of urban vibrancy in a refined resolution, and to optimize the resource allocation and functional configuration of the city.
Xucai Zhang; Yeran Sun; Ting Chan; Ying Huang; AnYao Zheng; Zhang Liu. Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou. Sustainability 2021, 13, 444 .
AMA StyleXucai Zhang, Yeran Sun, Ting Chan, Ying Huang, AnYao Zheng, Zhang Liu. Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou. Sustainability. 2021; 13 (2):444.
Chicago/Turabian StyleXucai Zhang; Yeran Sun; Ting Chan; Ying Huang; AnYao Zheng; Zhang Liu. 2021. "Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou." Sustainability 13, no. 2: 444.
In this study, we aimed to examine spatial inequalities of COVID-19 mortality rate in relation to spatial inequalities of socioeconomic and environmental factors across England. Specifically, we first explored spatial patterns of COVID-19 mortality rate in comparison to non-COVID-19 mortality rate. Subsequently, we established models to investigate contributions of socioeconomic and environmental factors to spatial variations of COVID-19 mortality rate across England (N = 317). Two newly developed specifications of spatial regression models were established successfully to estimate COVID-19 mortality rate (R2 = 0.49 and R2 = 0.793). The level of spatial inequalities of COVID-19 mortality is higher than that of non-COVID-19 mortality in England. Although global spatial association of COVID-19 mortality and non-COVID-19 mortality is positive, local spatial association of COVID-19 mortality and non-COVID-19 mortality is negative in some areas. Expectedly, hospital accessibility is negatively related to COVID-19 mortality rate. Percent of Asians, percent of Blacks, and unemployment rate are positively related to COVID-19 mortality rate. More importantly, relative humidity is negatively related to COVID-19 mortality rate. Moreover, among the spatial models estimated, the ‘random effects specification of eigenvector spatial filtering model’ outperforms the ‘matrix exponential spatial specification of spatial autoregressive model’.
Yeran Sun; Xuke Hu; Jing Xie. Spatial inequalities of COVID-19 mortality rate in relation to socioeconomic and environmental factors across England. Science of The Total Environment 2020, 758, 143595 -143595.
AMA StyleYeran Sun, Xuke Hu, Jing Xie. Spatial inequalities of COVID-19 mortality rate in relation to socioeconomic and environmental factors across England. Science of The Total Environment. 2020; 758 ():143595-143595.
Chicago/Turabian StyleYeran Sun; Xuke Hu; Jing Xie. 2020. "Spatial inequalities of COVID-19 mortality rate in relation to socioeconomic and environmental factors across England." Science of The Total Environment 758, no. : 143595-143595.
Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connectivity measures as the potential covariates in our cluster detection. Results of the most significant cluster and the second most significant cluster during five consecutive years are located around the central areas. Moreover, after adjusting the covariates, the most significant cluster moves from the central areas of London to its peripheral areas, while the second most significant cluster remains unchanged. Additionally, one potential covariate used in this study, length-based road density, exhibits a positive association with the number of road collisions; meanwhile count-based intersection density displays a negative association. Although the covariates (i.e., road density and intersection density) exhibit potential impact on the clusters of road collisions, they are unlikely to contribute to the majority of clusters. Furthermore, the method of fast Bayesian model-based cluster detection is developed to discover spatio-temporal clusters of serious injury collisions. Most of the areas at risk of serious injury collisions overlay those at risk of road collisions. Although not being identified as areas at risk of road collisions, some districts, e.g., City of London, are regarded as areas at risk of serious injury collisions.
Yeran Sun; Yu Wang; Ke Yuan; Ting Chan; Ying Huang. Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection. Sustainability 2020, 12, 8681 .
AMA StyleYeran Sun, Yu Wang, Ke Yuan, Ting Chan, Ying Huang. Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection. Sustainability. 2020; 12 (20):8681.
Chicago/Turabian StyleYeran Sun; Yu Wang; Ke Yuan; Ting Chan; Ying Huang. 2020. "Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection." Sustainability 12, no. 20: 8681.
To examine to what extent spatial inequalities in childhood obesity are attributable to spatial inequalities in socioeconomic characteristics across a country, we aimed to investigate the spatial associations of socioeconomic characteristics and childhood obesity. We first explored spatial patterns of childhood obesity prevalence, and subsequently investigated the spatial associations of socioeconomic factors and childhood obesity prevalence across England by selecting and estimating appropriate spatial regression models. As the data used are geospatial data, we used two newly developed specifications of spatial regression models to investigate the spatial association of socioeconomic factors and childhood obesity prevalence. As a result, among the two newly developed specifications of spatial regression models, the fast random effects specification of eigenvector spatial filtering (FRES-ESF) model appears to outperform the matrix exponential spatial specification of spatial autoregressive (MESS-SAR) model. Empirical results indicate that positive spatial dependence is found to exist in childhood obesity prevalence across England; and that socioeconomic factors are significantly associated with childhood obesity prevalence across England. In England, children living in areas with lower socioeconomic status are at higher risk of obesity. This study suggests effectively reducing spatial inequalities in socioeconomic status will plays a vital role in mitigating spatial inequalities in childhood obesity prevalence.
Yeran Sun; Xuke Hu; Ying Huang; Ting On Chan. Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England. ISPRS International Journal of Geo-Information 2020, 9, 599 .
AMA StyleYeran Sun, Xuke Hu, Ying Huang, Ting On Chan. Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England. ISPRS International Journal of Geo-Information. 2020; 9 (10):599.
Chicago/Turabian StyleYeran Sun; Xuke Hu; Ying Huang; Ting On Chan. 2020. "Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England." ISPRS International Journal of Geo-Information 9, no. 10: 599.
Air pollution can have adverse impacts on both the physical health and mental health of people. Increasing air pollution levels are likely to increase suicide rates, although the causal mechanisms underlying the relationship between pollution exposure and suicidal behaviour are not well understood. In this study, we aimed to further examine the spatial association of air pollution and suicidal behaviour. Specifically, we investigated whether or how PM2.5 levels are spatially associated with the adult suicide rates at the district level across London. As the data used are geospatial data, we used two newly developed specifications of spatial regression models to investigate the spatial association of PM2.5 levels and suicide. The empirical results show that PM2.5 levels are spatially associated with the suicide rates across London. The two models show that PM2.5 levels have a positive association with adult suicide rates over space. An area with a high percentage of White people or a low median household income is likely to suffer from a high suicide rate.
Yeran Sun; Ting Chan; Jing Xie; Xuan Sun; Ying Huang. Examining Spatial Association of Air Pollution and Suicide Rate Using Spatial Regression Models. Sustainability 2020, 12, 7444 .
AMA StyleYeran Sun, Ting Chan, Jing Xie, Xuan Sun, Ying Huang. Examining Spatial Association of Air Pollution and Suicide Rate Using Spatial Regression Models. Sustainability. 2020; 12 (18):7444.
Chicago/Turabian StyleYeran Sun; Ting Chan; Jing Xie; Xuan Sun; Ying Huang. 2020. "Examining Spatial Association of Air Pollution and Suicide Rate Using Spatial Regression Models." Sustainability 12, no. 18: 7444.
Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete geometric model that can be applied to different types of joints. In this paper, we proposed a novel geometric model and several model adaptions for typical elbow joints including the 90° and 45° types, which facilitates the use of 3D point clouds of the elbow joints collected from laser scanning. The model comprises translational, rotational, and dimensional parameters, which can be used not only for monitoring the joints’ geometry but also other applications such as point cloud registrations. Both simulated and real datasets were used to verify the model, and two applications derived from the proposed model (point cloud registration and mounting bracket detection) were shown. The results of the geometric fitting of the simulated datasets suggest that the model can accurately recover the geometry of the joint with very low translational (0.3 mm) and rotational (0.064°) errors when ±0.02 m random errors were introduced to coordinates of a simulated 90° joint (with diameter equal to 0.2 m). The fitting of the real datasets suggests that the accuracy of the diameter estimate reaches 97.2%. The joint-based registration accuracy reaches sub-decimeter and sub-degree levels for the translational and rotational parameters, respectively.
Ting On Chan; Linyuan Xia; Derek D. Lichti; Yeran Sun; Jun Wang; Tao Jiang; Qianxia Li. Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems. Sensors 2020, 20, 4594 .
AMA StyleTing On Chan, Linyuan Xia, Derek D. Lichti, Yeran Sun, Jun Wang, Tao Jiang, Qianxia Li. Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems. Sensors. 2020; 20 (16):4594.
Chicago/Turabian StyleTing On Chan; Linyuan Xia; Derek D. Lichti; Yeran Sun; Jun Wang; Tao Jiang; Qianxia Li. 2020. "Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems." Sensors 20, no. 16: 4594.
Some studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics can contribute to spatial disparities in adult obesity. The spatial analysis was undertaken to understand the association of spatial disparities in adult obesity and spatial disparities in socio-economic, behavioural and built environment characteristics. A spatial regression model which can remove the impact of auto-correlation in the residuals of conventionally regression models was applied to modelling local-scale rate of adult obesity (N = 59). Owing to the presence of residual spatial auto-correlation in the non-spatial regression model estimated, a spatial regression model was set up successfully to model local-scale rate of adult obesity across New York City (R2 = 0.8353, N = 59). Compared with socio-economic and built environment factors, behavioural factors make statistically significant contributions to spatial disparities in the prevalence of adult obesity (POAO). Particularly, two behavioural factors (‘sugary drinks consumption’ and ‘fruits and vegetable consumption’) can explain more than 70% of the variance of POAO (adjusted R2 = 0.7323, N = 59). Surprisingly, physical activity prevalence (percent of physically active adults) makes no statistically significant contributions. The results further suggest that the reduction of adult obesity prevalence could benefit more from decreasing intake of sugary drinks than increasing physical activity. The local government and policy are advised to prioritise decreasing exposure of residents to sugary drinks through restricting advertising or increasing taxes rather than increasing neighbourhoods’ walkability through urban planning.
Y. Sun; S. Wang; X. Sun. Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City. Public Health 2020, 186, 57 -62.
AMA StyleY. Sun, S. Wang, X. Sun. Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City. Public Health. 2020; 186 ():57-62.
Chicago/Turabian StyleY. Sun; S. Wang; X. Sun. 2020. "Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City." Public Health 186, no. : 57-62.
Urbanisation from the developing world has been phenomenal and renewed the interest of studying the connection between urban greenness and subjective wellbeing. This paper responds to this greenness-wellbeing connection by shifting its focus towards systematically exploring the influences of urban greenness, perceived pollution hazards, and their interaction terms on subjective wellbeing. Using a combination of green view data and individual survey data in Beijing, we find that perceived pollution hazards about the disposal of waste, polluted water, and air pollution have significant interaction effects with eye-sensored greenness exposures on subjective wellbeing. Findings of this study suggest that policies geared towards mitigating particular domains of pollution hazards and improving green landscape should work together for shaping people’s quality of life.
Wenjie Wu; Yanwen Yun; Bo Hu; Yeran Sun; Yang Xiao. Greenness, Perceived Pollution Hazards and Subjective Wellbeing: Evidence from China. Urban Forestry & Urban Greening 2020, 56, 126796 .
AMA StyleWenjie Wu, Yanwen Yun, Bo Hu, Yeran Sun, Yang Xiao. Greenness, Perceived Pollution Hazards and Subjective Wellbeing: Evidence from China. Urban Forestry & Urban Greening. 2020; 56 ():126796.
Chicago/Turabian StyleWenjie Wu; Yanwen Yun; Bo Hu; Yeran Sun; Yang Xiao. 2020. "Greenness, Perceived Pollution Hazards and Subjective Wellbeing: Evidence from China." Urban Forestry & Urban Greening 56, no. : 126796.
Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston.
Yeran Sun; Yinming Ren; Xuan Sun. Uber Movement Data: A Proxy for Average One-way Commuting Times by Car. ISPRS International Journal of Geo-Information 2020, 9, 184 .
AMA StyleYeran Sun, Yinming Ren, Xuan Sun. Uber Movement Data: A Proxy for Average One-way Commuting Times by Car. ISPRS International Journal of Geo-Information. 2020; 9 (3):184.
Chicago/Turabian StyleYeran Sun; Yinming Ren; Xuan Sun. 2020. "Uber Movement Data: A Proxy for Average One-way Commuting Times by Car." ISPRS International Journal of Geo-Information 9, no. 3: 184.
The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shopping, eating, etc. The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing technology. To address this issue, we used a new social sensing approach to classify land use according to human mobility and activity patterns. Previous studies used other social sensing approaches to predict land use types at the parcel or the area level, whilst fine-grained point-of-interest (POI)-level land use data are likely to more useful in urban planning. To abridge this research gap, we proposed a new social sensing approach dedicated to classifying land use at a finer scale (i.e., POI-level or building level) according to human mobility and activity patterns reflected by location-based social network (LBSN) data. Specifically, we firstly investigated spatial and temporal patterns of human mobility and activity behavior using check-in data from a popular Chinese LBSN named Sina Weibo and subsequently applied those patterns to predicting the category of POI to refine urban land use classification in Guangzhou, China. In this study, we applied three classification methods (i.e., naive Bayes, support vector machines, and random forest) to recognize category of a certain POI by spatial and temporal features of human mobility and activity behavior as well as POIs’ locational characteristics. Random forest outperformed the other two methods and obtained an overall accuracy of 72.21%. Apart from that, we compared the results of the different rules in filtering check-in samples. The comparison results show that a reasonable rule to select samples is essential for predicting the category of POI. Moreover, the approach proposed in this study can be potentially applied to identifying functions of buildings according to visitors’ mobility and activity behavior and buildings’ locational characteristics.
Xucai Zhang; Yeran Sun; AnYao Zheng; Yu Wang. A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data. ISPRS International Journal of Geo-Information 2020, 9, 124 .
AMA StyleXucai Zhang, Yeran Sun, AnYao Zheng, Yu Wang. A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data. ISPRS International Journal of Geo-Information. 2020; 9 (2):124.
Chicago/Turabian StyleXucai Zhang; Yeran Sun; AnYao Zheng; Yu Wang. 2020. "A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data." ISPRS International Journal of Geo-Information 9, no. 2: 124.
The spatial implications of urban parks on people’s residential satisfaction are fueled by the desire to mitigate the rise of environmental injustice concerns in the developing world. While previous studies have examined the socio-spatial differentiation of park access and residential satisfaction, direct evidence on the role of park usage to play has been limited. This study shifts the focus from access to usage and quantitatively assess their associations with residential satisfaction. Our results quantify the evidence on the significant effects of park usage on residential satisfaction. Importantly, the association between park usage and residential satisfaction tends to be varied with local contextual amenities.
Wenjie Wu; Guanpeng Dong; Yeran Sun; Yanwen Yun. Contextualized effects of Park access and usage on residential satisfaction: A spatial approach. Land Use Policy 2020, 94, 104532 .
AMA StyleWenjie Wu, Guanpeng Dong, Yeran Sun, Yanwen Yun. Contextualized effects of Park access and usage on residential satisfaction: A spatial approach. Land Use Policy. 2020; 94 ():104532.
Chicago/Turabian StyleWenjie Wu; Guanpeng Dong; Yeran Sun; Yanwen Yun. 2020. "Contextualized effects of Park access and usage on residential satisfaction: A spatial approach." Land Use Policy 94, no. : 104532.
Three-dimensional (3D) pipe network modeling plays an essential part in high performance-based smart city applications. Given that massive 3D pipe networks tend to be difficult to manage and to visualize, we propose in this study a hybrid framework for high-performance modeling of a 3D pipe network, including pipe network data model and high-performance modeling. The pipe network data model is devoted to three-dimensional pipe network construction based on network topology and building information models (BIMs). According to the topological relationships of the pipe point pipelines, the pipe network is decomposed into multiple pipe segment units. The high-performance modeling of 3D pipe network contains a spatial 3D model, the instantiation, adaptive rendering, and combination parallel computing. Spatial 3D model (S3M) is proposed for spatial data transmission, exchange, and visualization of massive and multi-source 3D spatial data. The combination parallel computing framework with GPU and OpenMP was developed to reduce the processing time for pipe networks. The results of the experiments showed that the hybrid framework achieves a high efficiency and the hardware resource occupation is reduced.
Shaohua Wang; Yeran Sun; Yinle Sun; Yong Guan; Zhenhua Feng; Hao Lu; Wenwen Cai; Liang Long. A Hybrid Framework for High-Performance Modeling of Three-Dimensional Pipe Networks. ISPRS International Journal of Geo-Information 2019, 8, 441 .
AMA StyleShaohua Wang, Yeran Sun, Yinle Sun, Yong Guan, Zhenhua Feng, Hao Lu, Wenwen Cai, Liang Long. A Hybrid Framework for High-Performance Modeling of Three-Dimensional Pipe Networks. ISPRS International Journal of Geo-Information. 2019; 8 (10):441.
Chicago/Turabian StyleShaohua Wang; Yeran Sun; Yinle Sun; Yong Guan; Zhenhua Feng; Hao Lu; Wenwen Cai; Liang Long. 2019. "A Hybrid Framework for High-Performance Modeling of Three-Dimensional Pipe Networks." ISPRS International Journal of Geo-Information 8, no. 10: 441.
Jorge David Gonzalez Paule; Yeran Sun; Yashar Moshfeghi. On fine-grained geolocalisation of tweets and real-time traffic incident detection. Information Processing & Management 2019, 56, 1119 -1132.
AMA StyleJorge David Gonzalez Paule, Yeran Sun, Yashar Moshfeghi. On fine-grained geolocalisation of tweets and real-time traffic incident detection. Information Processing & Management. 2019; 56 (3):1119-1132.
Chicago/Turabian StyleJorge David Gonzalez Paule; Yeran Sun; Yashar Moshfeghi. 2019. "On fine-grained geolocalisation of tweets and real-time traffic incident detection." Information Processing & Management 56, no. 3: 1119-1132.
Researchers in multiple disciplines have used Twitter to study various mobility patterns and “live” aspects of cities. In the field of transportation planning, one major area of interest has been to use Twitter data to infer movement patterns and origins and destinations of trip-makers. In the area of transportation operations, researchers have been interested in automated incident detection or event detection. Because the number of geotagged tweets pinpointing the location of the user at the time of tweeting tends to be sparse for transportation applications, there is a need to consider expanding and geolocalising the sample of non-geotagged tweets that can be associated with locations. We call this process “geolocalisation”. While geolocalisation is an active area of research associated with the geospatial semantic Web and Geographic Information Retrieval, much of the work has focused on geolocalisation of users, or on geolocalisation of tweeting activity to fairly coarse geographical levels, whereas our work relates to street-level or even building-level geolocalisation. We will consider two different approaches to geolocalisation that make use of Points of Interest databases and a second information retrieval-based approach that trains on geotagged tweets. Our objective is to make a comprehensive assessment of the differences in spatial and content coverage between non-geotagged tweets geolocalised using different approaches compared to using geotagged tweets alone. We find that using geolocalised tweets allows discovery of a larger number of incidents and socioeconomic patterns that are not evident from using geotagged data alone, including activity throughout the metropolitan area, including deprived “Environmental Justice” (EJ) areas where the degree of social media activity detected is usually low. Conclusions are drawn on the relative usefulness of the alternative approaches.
Jorge David Gonzalez Paule; Yeran Sun; Piyushimita (Vonu) Thakuriah. Beyond Geotagged Tweets: Exploring the Geolocalisation of Tweets for Transportation Applications. Complex Networks and Dynamic Systems 2018, 1 -21.
AMA StyleJorge David Gonzalez Paule, Yeran Sun, Piyushimita (Vonu) Thakuriah. Beyond Geotagged Tweets: Exploring the Geolocalisation of Tweets for Transportation Applications. Complex Networks and Dynamic Systems. 2018; ():1-21.
Chicago/Turabian StyleJorge David Gonzalez Paule; Yeran Sun; Piyushimita (Vonu) Thakuriah. 2018. "Beyond Geotagged Tweets: Exploring the Geolocalisation of Tweets for Transportation Applications." Complex Networks and Dynamic Systems , no. : 1-21.
Yeran Sun; Yunyan Du. Big data and sustainable cities: applications of new and emerging forms of geospatial data in urban studies. Open Geospatial Data, Software and Standards 2017, 2, 1 .
AMA StyleYeran Sun, Yunyan Du. Big data and sustainable cities: applications of new and emerging forms of geospatial data in urban studies. Open Geospatial Data, Software and Standards. 2017; 2 (1):1.
Chicago/Turabian StyleYeran Sun; Yunyan Du. 2017. "Big data and sustainable cities: applications of new and emerging forms of geospatial data in urban studies." Open Geospatial Data, Software and Standards 2, no. 1: 1.