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Berjisian E, Bigazzi A, Barkh H. By cyclists, for cyclists: Road grade and elevation estimation from crowd-sourced fitness application data. PLoS One 2023; 18:e0295027. [PMID: 38117814 PMCID: PMC10732448 DOI: 10.1371/journal.pone.0295027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/14/2023] [Indexed: 12/22/2023] Open
Abstract
Road grade or slope is a key factor for walking and cycling behavior and outcomes (influencing route, speed, energy, etc.). For this reason, the scarcity of precise road grade data presents a challenge for travel information and analysis. This paper examines the accuracy of using crowd-sourced GPS data from a fitness application to estimate roadway grade profiles, which can then be used to develop network-wide road grade datasets. We externally validate an elevation estimation method described by McKenzie and Janowicz using field surveying data, and then propose and evaluate modifications for estimation of road grade (which is more directly relevant than elevation for walking and cycling analysis). We find that a modest amount of crowd-sourced GPS data can be used to generate relatively accurate road grade estimates: better than commonly-used low-resolution elevation models, but not as accurate as high-resolution data derived from LiDAR (Light Detection and Ranging). We also find that the grade estimates are more reliable than the elevation estimates, relative to alternative data sources. The most accurate method to aggregate crowd-sourced GPS data builds a composite roadway grade profile using partition-around-medoid clustering of individual grade sequences, first smoothed with a Savitzky-Golay filter and cleaned with Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Implementing this method with an average of 150 GPS traces per location yields a root mean square error (RMSE) of 1% road grade. The findings in this paper can be used to incorporate precise road grade information into street network datasets over a wide spatial scale, which is necessary for walking and cycling analysis that fully considers the physiological aspects of active transportation.
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Affiliation(s)
- Elmira Berjisian
- Department of Civil Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexander Bigazzi
- Department of Civil Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Hamed Barkh
- Department of Civil Engineering, University of British Columbia, Vancouver, British Columbia, Canada
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2
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Pontin FL, Jenneson VL, Morris MA, Clarke GP, Lomax NM. Objectively measuring the association between the built environment and physical activity: a systematic review and reporting framework. Int J Behav Nutr Phys Act 2022; 19:119. [PMID: 36104757 PMCID: PMC9476279 DOI: 10.1186/s12966-022-01352-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 08/18/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Objective measures of built environment and physical activity provide the opportunity to directly compare their relationship across different populations and spatial contexts. This systematic review synthesises the current body of knowledge and knowledge gaps around the impact of objectively measured built environment metrics on physical activity levels in adults (≥ 18 years). Additionally, this review aims to address the need for improved quality of methodological reporting to evaluate studies and improve inter-study comparability though the creation of a reporting framework.
Methods
A systematic search of the literature was conducted following the PRISMA guidelines. After abstract and full-text screening, 94 studies were included in the final review. Results were synthesised using an association matrix to show overall association between built environment and physical activity variables. Finally, the new PERFORM (’Physical and Environmental Reporting Framework for Objectively Recorded Measures’) checklist was created and applied to the included studies rating them on their reporting quality across four key areas: study design and characteristics, built environment exposures, physical activity metrics, and the association between built environment and physical activity.
Results
Studies came from 21 countries and ranged from two days to six years in duration. Accelerometers and using geographic information system (GIS) to define the spatial extent of exposure around a pre-defined geocoded location were the most popular tools to capture physical activity and built environment respectively. Ethnicity and socio-economic status of participants were generally poorly reported. Moderate-to-vigorous physical activity (MVPA) was the most common metric of physical activity used followed by walking. Commonly investigated elements of the built environment included walkability, access to parks and green space. Areas where there was a strong body of evidence for a positive or negative association between the built environment and physical activity were identified. The new PERFORM checklist was devised and poorly reported areas identified, included poor reporting of built environment data sources and poor justification of method choice.
Conclusions
This systematic review highlights key gaps in studies objectively measuring the built environment and physical activity both in terms of the breadth and quality of reporting. Broadening the variety measures of the built environment and physical activity across different demographic groups and spatial areas will grow the body and quality of evidence around built environment effect on activity behaviour. Whilst following the PERFORM reporting guidance will ensure the high quality, reproducibility, and comparability of future research.
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Davidson BI. The crossroads of digital phenotyping. Gen Hosp Psychiatry 2022; 74:126-132. [PMID: 33653612 DOI: 10.1016/j.genhosppsych.2020.11.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/11/2020] [Accepted: 11/11/2020] [Indexed: 12/26/2022]
Abstract
The term 'Digital Phenotyping' has started to appear with increasing regularity in medical research, especially within psychiatry. This aims to bring together digital traces (e.g., from smartphones), medical data (e.g., electronic health records), and lived experiences (e.g., daily activity, location, social contact), to better monitor, intervene, and diagnose various psychiatric conditions. However, is this notion any different from digital traces or the quantified self? While digital phenotyping has the potential to transform and revolutionize medicine as we know it; there are a number of challenges that must be addressed if research is to blossom. At present, these issues include; (1) methodological issues, for example, the lack of clear theoretical links between digital markers (e.g., battery life, interactions with smartphones) and condition relapses, (2) the current tools being employed, where they typically have a number of security or privacy issues, and are invasive by nature, (3) analytical methods and approaches, where I question whether research should start in larger-scale epidemiological scale or in smaller (and potentially highly vulnerable) patient populations as is the current norm, (4) the current lack of security and privacy regulation adherence of apps used, and finally, (5) how do such technologies become integrated into various healthcare systems? This aims to provide deep insight into how the Digital Phenotyping could provide huge promise if we critically reflect now and gather clinical insights with a number of other disciplines such as epidemiology, computer- and the social sciences to move forward.
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Affiliation(s)
- Brittany I Davidson
- Information, Decisions, and Operations Division, School of Management, University of Bath, United Kingdom; Department of Computer Science, University of Bristol, United Kingdom.
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4
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Characterisation of Temporal Patterns in Step Count Behaviour from Smartphone App Data: An Unsupervised Machine Learning Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111476. [PMID: 34769991 PMCID: PMC8583116 DOI: 10.3390/ijerph182111476] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 12/23/2022]
Abstract
The increasing ubiquity of smartphone data, with greater spatial and temporal coverage than achieved by traditional study designs, have the potential to provide insight into habitual physical activity patterns. This study implements and evaluates the utility of both K-means clustering and agglomerative hierarchical clustering methods in identifying weekly and yearlong physical activity behaviour trends. Characterising the demographics and choice of activity type within the identified clusters of behaviour. Across all seven clusters of seasonal activity behaviour identified, daylight saving was shown to play a key role in influencing behaviour, with increased activity in summer months. Investigation into weekly behaviours identified six clusters with varied roles, of weekday versus weekend, on the likelihood of meeting physical activity guidelines. Preferred type of physical activity likewise varied between clusters, with gender and age strongly associated with cluster membership. Key relationships are identified between weekly clusters and seasonal activity behaviour clusters, demonstrating how short-term behaviours contribute to longer-term activity patterns. Utilising unsupervised machine learning, this study demonstrates how the volume and richness of secondary app data can allow us to move away from aggregate measures of physical activity to better understand temporal variations in habitual physical activity behaviour.
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Eberth JM, Kramer MR, Delmelle EM, Kirby RS. What is the place for space in epidemiology? Ann Epidemiol 2021; 64:41-46. [PMID: 34530128 DOI: 10.1016/j.annepidem.2021.08.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/18/2021] [Accepted: 08/27/2021] [Indexed: 11/27/2022]
Abstract
At the heart of spatial epidemiology is the need to describe and understand variation in population health. In this review and introduction to the themed issue on "Spatial Analysis and GIS in Epidemiology," we present theoretical foundations and methodological developments in spatial epidemiology, discuss spatial analytical techniques and their public health applications, and identify novel data sources and applications with the potential to make epidemiology more consequential. Challenges with using georeferenced data are also explored, including dealing with small sample sizes, missingness, generalizability, and geographic scale. Given the increasing availability of spatial data and visualization tools, we have an opportunity to overcome traditionally siloed fields and practice settings to advance knowledge and more appropriately respond to emerging public health crises.
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Affiliation(s)
- Jan M Eberth
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC; Rural and Minority Health Research Center, University of South Carolina, Columbia, SC; Big Data Health Science Center, University of South Carolina, Columbia, SC.
| | - Michael R Kramer
- Department of Epidemiology, Emory University, Atlanta, GA; Emory Maternal and Child Health Center of Excellence, Emory University, Atlanta, GA
| | - Eric M Delmelle
- Department of Geography & Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC; Department of Geography and Historical Studies, University of Eastern Finland, Joensuu, Finland
| | - Russell S Kirby
- College of Public Health, University of South Florida, Tampa, FL
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6
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Oldroyd RA, Hobbs M, Campbell M, Jenneson V, Marek L, Morris MA, Pontin F, Sturley C, Tomintz M, Wiki J, Birkin M, Kingham S, Wilson M. Progress Towards Using Linked Population-Based Data For Geohealth Research: Comparisons Of Aotearoa New Zealand And The United Kingdom. APPLIED SPATIAL ANALYSIS AND POLICY 2021; 14:1025-1040. [PMID: 33942015 PMCID: PMC8081771 DOI: 10.1007/s12061-021-09381-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/20/2021] [Indexed: 06/12/2023]
Abstract
Globally, geospatial concepts are becoming increasingly important in epidemiological and public health research. Individual level linked population-based data afford researchers with opportunities to undertake complex analyses unrivalled by other sources. However, there are significant challenges associated with using such data for impactful geohealth research. Issues range from extracting, linking and anonymising data, to the translation of findings into policy whilst working to often conflicting agendas of government and academia. Innovative organisational partnerships are therefore central to effective data use. To extend and develop existing collaborations between the institutions, in June 2019, authors from the Leeds Institute for Data Analytics and the Alan Turing Institute, London, visited the Geohealth Laboratory based at the University of Canterbury, New Zealand. This paper provides an overview of insight shared during a two-day workshop considering aspects of linked population-based data for impactful geohealth research. Specifically, we discuss both the collaborative partnership between New Zealand's Ministry of Health (MoH) and the University of Canterbury's GeoHealth Lab and novel infrastructure, and commercial partnerships enabled through the Leeds Institute for Data Analytics and the Alan Turing Institute in the UK. We consider the New Zealand Integrated Data Infrastructure as a case study approach to population-based linked health data and compare similar approaches taken by the UK towards integrated data infrastructures, including the ESRC Big Data Network centres, the UK Biobank, and longitudinal cohorts. We reflect on and compare the geohealth landscapes in New Zealand and the UK to set out recommendations and considerations for this rapidly evolving discipline.
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Affiliation(s)
- R. A. Oldroyd
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- School of Geography, University of Leeds, Leeds, UK
| | - M. Hobbs
- GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch, Canterbury, New Zealand
- Health Sciences, College of Education, Health and Human Development, University of Canterbury, Christchurch, Canterbury, New Zealand
| | - M. Campbell
- GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch, Canterbury, New Zealand
- School of Earth and Environment, College of Science, University of Canterbury, Christchurch, Canterbury, New Zealand
| | - V. Jenneson
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch, Canterbury, New Zealand
| | - L. Marek
- GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch, Canterbury, New Zealand
| | - M. A. Morris
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- School of Medicine, University of Leeds, Leeds, UK
- Alan Turing Institute, London, UK
| | - F. Pontin
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - C. Sturley
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- School of Medicine, University of Leeds, Leeds, UK
| | - M. Tomintz
- GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch, Canterbury, New Zealand
| | - J. Wiki
- GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch, Canterbury, New Zealand
| | - M. Birkin
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Alan Turing Institute, London, UK
| | - S. Kingham
- GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch, Canterbury, New Zealand
- School of Earth and Environment, College of Science, University of Canterbury, Christchurch, Canterbury, New Zealand
| | - M. Wilson
- Geospatial Research Institute, University of Canterbury, Christchurch, Canterbury, New Zealand
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Barber TM, Kyrou I, Randeva HS, Weickert MO. Mechanisms of Insulin Resistance at the Crossroad of Obesity with Associated Metabolic Abnormalities and Cognitive Dysfunction. Int J Mol Sci 2021; 22:ijms22020546. [PMID: 33430419 PMCID: PMC7827338 DOI: 10.3390/ijms22020546] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/04/2021] [Accepted: 01/06/2021] [Indexed: 12/19/2022] Open
Abstract
Obesity mediates most of its direct medical sequelae through the development of insulin resistance (IR). The cellular effects of insulin occur through two main postreceptor pathways that are the phosphatidylinositol 3-kinase (PI3-K) and the mitogen-activated protein kinase (MAP-K) pathways. Obesity-related IR implicates the PI3-K pathway that confers the metabolic effects of insulin. Numerous and complex pathogenic pathways link obesity with the development of IR, including chronic inflammation, mitochondrial dysfunction (with the associated production of reactive oxygen species and endoplasmic reticulum stress), gut microbiota dysbiosis and adipose extracellular matrix remodelling. IR itself plays a key role in the development of metabolic dysfunction, including hypertension, dyslipidaemia and dysglycaemia. Furthermore, IR promotes weight gain related to secondary hyperinsulinaemia, with a resulting vicious cycle of worsening IR and its metabolic sequelae. Ultimately, IR underlies obesity-related conditions such as type 2 diabetes mellitus (T2D) and polycystic ovary syndrome (PCOS). IR also underlies many obesity-related malignancies, through the effects of compensatory hyperinsulinaemia on the relatively intact MAP-K insulin pathway, which controls cellular growth processes and mitoses. Furthermore, the emergent data over recent decades support an important role of obesity- and T2D-related central IR in the development of cognitive dysfunction, including effects on hippocampal synaptic plasticity. Importantly, IR is largely reversible through the optimisation of lifestyle factors that include regular engagement in physical activity with the avoidance of sedentariness, improved diet including increased fibre intake and sleep sufficiency. IR lies at the key crossroad between obesity and both metabolic and cognitive dysfunction. Given the importance of IR in the pathogenesis of many 21st century chronic diseases and its eminent reversibility, it is important that we all embrace and facilitate optimised lifestyles to improve the future health and wellbeing of the populace.
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Affiliation(s)
- Thomas M. Barber
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism, University Hospitals Coventry and Warwickshire, Clifford Bridge Road, Coventry CV2 2DX, UK; (T.M.B.); (I.K.); (H.S.R.)
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry CV2 2DX, UK
| | - Ioannis Kyrou
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism, University Hospitals Coventry and Warwickshire, Clifford Bridge Road, Coventry CV2 2DX, UK; (T.M.B.); (I.K.); (H.S.R.)
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry CV2 2DX, UK
- Aston Medical Research Institute, Aston Medical School, College of Health and Life Sciences, Aston University, Birmingham B4 7ET, UK
| | - Harpal S. Randeva
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism, University Hospitals Coventry and Warwickshire, Clifford Bridge Road, Coventry CV2 2DX, UK; (T.M.B.); (I.K.); (H.S.R.)
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry CV2 2DX, UK
- Aston Medical Research Institute, Aston Medical School, College of Health and Life Sciences, Aston University, Birmingham B4 7ET, UK
| | - Martin O. Weickert
- Warwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism, University Hospitals Coventry and Warwickshire, Clifford Bridge Road, Coventry CV2 2DX, UK; (T.M.B.); (I.K.); (H.S.R.)
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry CV2 2DX, UK
- Centre for Sport, Exercise and Life Sciences, Faculty of Health & Life Sciences, Coventry University, Coventry CV1 5FB, UK
- Correspondence:
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8
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McKey T, Kim D, Seo S. Crowdsourced Mapping for Healthy Food Accessibility in Dallas, Texas: A Feasibility Study. Front Public Health 2020; 8:71. [PMID: 32211370 PMCID: PMC7068842 DOI: 10.3389/fpubh.2020.00071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Accepted: 02/24/2020] [Indexed: 02/01/2023] Open
Abstract
Since its first use for describing a neighborhood lacking access to food in the 1990's, “food deserts” has been widely addressed by researchers and adopted as an indicator of neighborhood-level food insecurity by governmental agencies, such as USDA. However, mostly due to cost and difficulty in collecting georeferenced data and characteristics of grocery stores, the USDA Food Access Research Atlas is infrequently released, and considers only income, vehicle ownership, and distance to the nearest grocery store. In this paper, we explored the feasibility of a crowdsourced geospatial data source, coupled with additional measures, in supplementing the USDA's current designation of food deserts. We used Yelp data to map food deserts in the city of Dallas and compared them with those based on the 2015 USDA food retailer database. Although direct comparison was not possible due to time mismatch between the two data sources, the discrepancies highlighted the need of a more frequent identification of food deserts for timely policy intervention. Furthermore, we extended mapping to reveal other potential areas of concerns, by adding the Transit Score metric and Yelp's price descriptor of businesses. The resulting maps illustrated the areas with grocery stores nearby but with limited accessibility due to lack of public transit or potential financial barriers in purchasing foods due to high prices. Our findings demonstrate the current status and future potential of up-to-date crowdsourced, georeferenced data as a complement of official government data, which could serve to extend food access research and guide health policies.
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Affiliation(s)
- Thomas McKey
- School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, United States
| | - Dohyeong Kim
- School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, United States
| | - SungChul Seo
- Department of Environmental Health and Safety, College of Health Industry, Eulji University, Seongnam, South Korea
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Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2019. [DOI: 10.3390/ijgi8120559] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Accurate information on the number and distribution of pedestrians in space and time helps urban planners maintain current city infrastructure and design better public spaces for local residents and visitors. Previous studies have demonstrated that using webcams together with crowdsourcing platforms to locate pedestrians in the captured images is a promising technique for analyzing pedestrian activity. However, it is challenging to efficiently transform the time series of pedestrian locations in the images to information suitable for geospatial analytics, as well as visualize data in a meaningful way to inform urban design or decision making. In this study, we propose to use a space-time cube (STC) representation of pedestrian data to analyze the spatio-temporal patterns of pedestrians in public spaces. We take advantage of AMOS (The Archive of Many Outdoor Scenes), a large database of images captured by thousands of publicly available, outdoor webcams. We developed a method to obtain georeferenced spatio-temporal data from webcams and to transform them into high-resolution continuous representation of pedestrian densities by combining bivariate kernel density estimation with trivariate, spatio-temporal spline interpolation. We demonstrate our method on two case studies analyzing pedestrian activity of two city plazas. The first case study explores daily and weekly spatio-temporal patterns of pedestrian activity while the second one highlights the differences in pattern before and after plaza’s redevelopment. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time series of pedestrian occurrences captured by webcam images.
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Cesare N, Nguyen QC, Grant C, Nsoesie EO. Social media captures demographic and regional physical activity. BMJ Open Sport Exerc Med 2019; 5:e000567. [PMID: 31423323 PMCID: PMC6678033 DOI: 10.1136/bmjsem-2019-000567] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2019] [Indexed: 12/04/2022] Open
Abstract
Objectives We examined the use of data from social media for surveillance of physical activity prevalence in the USA. Methods We obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention. Results The association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes. Conclusions The regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations.
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Affiliation(s)
- Nina Cesare
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA.,Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, USA
| | - Quynh C Nguyen
- Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, Maryland, USA
| | - Christan Grant
- School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA
| | - Elaine O Nsoesie
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA.,Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, USA
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11
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Correcting Bias in Crowdsourced Data to Map Bicycle Ridership of All Bicyclists. URBAN SCIENCE 2019. [DOI: 10.3390/urbansci3020062] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Traditional methods of counting bicyclists are resource-intensive and generate data with sparse spatial and temporal detail. Previous research suggests big data from crowdsourced fitness apps offer a new source of bicycling data with high spatial and temporal resolution. However, crowdsourced bicycling data are biased as they oversample recreational riders. Our goals are to quantify geographical variables, which can help in correcting bias in crowdsourced, data and to develop a generalized method to correct bias in big crowdsourced data on bicycle ridership in different settings in order to generate maps for cities representative of all bicyclists at a street-level spatial resolution. We used street-level ridership data for 2016 from a crowdsourced fitness app (Strava), geographical covariate data, and official counts from 44 locations across Maricopa County, Arizona, USA (training data); and 60 locations from the city of Tempe, within Maricopa (test data). First, we quantified the relationship between Strava and official ridership data volumes. Second, we used a multi-step approach with variable selection using LASSO followed by Poisson regression to integrate geographical covariates, Strava, and training data to correct bias. Finally, we predicted bias-corrected average annual daily bicyclist counts for Tempe and evaluated the model’s accuracy using the test data. We found a correlation between the annual ridership data from Strava and official counts (R2 = 0.76) in Maricopa County for 2016. The significant variables for correcting bias were: The proportion of white population, median household income, traffic speed, distance to residential areas, and distance to green spaces. The model could correct bias in crowdsourced data from Strava in Tempe with 86% of road segments being predicted within a margin of ±100 average annual bicyclists. Our results indicate that it is possible to map ridership for cities at the street-level by correcting bias in crowdsourced bicycle ridership data, with access to adequate data from official count programs and geographical covariates at a comparable spatial and temporal resolution.
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12
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Mapping with Stakeholders: An Overview of Public Participatory GIS and VGI in Transport Decision-Making. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2019. [DOI: 10.3390/ijgi8040198] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Transport decision-making problems are typically spatially based and involve a set of feasible alternatives with multiple evaluation criteria. Besides, transport decisions affect citizens’ quality of life, as well as specific interests of general stakeholders (e.g., transport companies), thus needing a participatory approach to decision-making. Geographic Information Systems (GIS) have the ability to visualize spatial data and represent the impact of location based transport alternatives, thus helping experts to conduct robust assessments. Moreover, with the recent diffusion of Volunteered Geographic Information (VGI) and development of Public Participatory GIS (PPGIS) platforms, the process can be enhanced thanks to the collection of a large amount of updated spatial data and the achievement of an active community participation. In this study, we provide an overview based on a structured literature review of the use of VGI and PPGIS in transport studies, exploring the fields of application, role played by GIS, level of public involvement and decision stage at which they are applied. From the overview’s results, we propose a general framework for the evaluation of transport alternatives using GIS from a multiple stakeholder point of view; the main conclusion is the usefulness of the integration between Public Participation, GIS and quantitative evaluation methods, in particular Multi Criteria Decision Analysis, in order to foster technically sound and shared decisions.
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13
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A Novel Dynamic Dispatching Method for Bicycle-Sharing System. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2019. [DOI: 10.3390/ijgi8030117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
With the rapid development of sharing bicycles, unreasonable dispatching methods are likely to cause a series of issues, such as resource waste and traffic congestion in the city. In this paper, a new dynamic scheduling method is proposed, named Tri-G, so as to solve the above problems. First of all, the whole visualization information of bike stations was built based on a Spatio-Temporal Graph (STG), then Gaussian Mixture Mode (GMM) was used to group individual stations into clusters according to their geographical locations and transition patterns, and the Gradient Boosting Regression Tree (GBRT) algorithm was adopted to predict the number of bikes inflow/outflow at each station in real time. This paper used New York’s bicycle commute data to build global STG visualization information to evaluate Tri-G. Finally, it is concluded that Tri-G is superior to the methods in control groups, which can be applied to various geographical scenarios. In addition, this paper also discovered some human mobility patterns as well as some rules, which are helpful for governments to improve urban planning.
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Lee K, Sener IN. Understanding Potential Exposure of Bicyclists on Roadways to Traffic-Related Air Pollution: Findings from El Paso, Texas, Using Strava Metro Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E371. [PMID: 30699896 PMCID: PMC6388168 DOI: 10.3390/ijerph16030371] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 01/22/2019] [Accepted: 01/24/2019] [Indexed: 12/14/2022]
Abstract
As bicycling on roadways can cause adverse health effects, there is an urgent need to understand how bicycle routes expose bicyclists to traffic emissions. Limited resources for monitoring reveal that bicycle travel patterns may constrain such understanding at the network level. This study examined the potential exposure of bicyclists to traffic-related air pollution in El Paso, Texas, using Strava Metro data that revealed bicycle patterns across the city networks. An initial spatial mapping analysis was conducted to explore the spatial patterns of bicycling and traffic pollutant emission, followed by exploratory descriptive statistics. A spatial bicycle model was then developed to explore factors influencing bicycling activity in El Paso. Analysis results indicated significant associations between greater bicycle volume and both higher levels of particulate matter (PM2.5) emissions and more frequent bus services, implying adverse health concerns related to traffic-related air pollution. The results also indicated significant effects of various environmental characteristics (e.g., roadway, bicycle infrastructure, topography, and demographics) on bicycling. The findings encourage extending this study to provide guidance to bicyclists whose regular trips take place on heavily trafficked roads and during rush hours in this region and to evaluate the net health impacts of on-road bicycling for the general population.
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Affiliation(s)
- Kyuhyun Lee
- Texas A&M Transportation Institute, College Station, TX 77843, USA.
| | - Ipek N Sener
- Texas A&M Transportation Institute, Austin, TX 78752, USA.
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15
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Mooney SJ, Magee C, Dang K, Leonard JC, Yang J, Rivara FP, Ebel BE, Rowhani-Rahbar A, Quistberg DA. "Complete Streets" and Adult Bicyclist Fatalities: Applying G-Computation to Evaluate an Intervention That Affects the Size of a Population at Risk. Am J Epidemiol 2018; 187:2038-2045. [PMID: 29767676 PMCID: PMC6118069 DOI: 10.1093/aje/kwy100] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 04/25/2018] [Accepted: 04/27/2018] [Indexed: 11/12/2022] Open
Abstract
"Complete streets" policies require transportation engineers to make provisions for pedestrians, bicyclists, and mass transit users. These policies may make bicycling safer for individual cyclists while increasing the overall number of bicycle fatalities if more people cycle due to improved infrastructure. We merged county-level records of complete streets policies with Fatality Analysis Reporting System counts of cyclist fatalities occurring between January 2000 and December 2015. Because comprehensive county-level estimates of numbers of cyclists were not available, we used bicycle commuter estimates from the American Community Survey and the US Census as a proxy for the cycling population and limited analysis to 183 counties (accounting for over half of the US population) for which cycle commuting estimates were consistently nonzero. We used G-computation to estimate the effect of complete streets policies on overall numbers of cyclist fatalities while also accounting for potential policy effects on the size of the cycling population. Over a period of 16 years, 5,254 cyclists died in these counties, representing 34 fatalities per 100,000 cyclist-years. We estimated that complete streets policies made cycling safer, averting 0.6 fatalities per 100,000 cyclist-years (95% confidence interval: -1.0, -0.3) by encouraging a 2.4% increase in cycling but producing only a 0.7% increase in cyclist fatalities. G-computation is a useful tool for understanding the impact of policy on risk and exposure.
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Affiliation(s)
- Stephen J Mooney
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington
| | | | - Kolena Dang
- University of Washington, Seattle, Washington
| | - Julie C Leonard
- Center for Injury Research and Policy, The Research Institute at Nationwide Children’s Hospital, Columbus, Ohio
| | - Jingzhen Yang
- Center for Injury Research and Policy, The Research Institute at Nationwide Children’s Hospital, Columbus, Ohio
| | - Frederick P Rivara
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington
| | - Beth E Ebel
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington
| | - Ali Rowhani-Rahbar
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington
| | - D Alex Quistberg
- Harborview Injury Prevention and Research Center, University of Washington, Seattle, Washington
- Environmental and Occupational Health, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania
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16
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Shen S, Wei ZQ, Sun LJ, Su YQ, Wang RC, Jiang HM. The Shared Bicycle and Its Network-Internet of Shared Bicycle (IoSB): A Review and Survey. SENSORS 2018; 18:s18082581. [PMID: 30087263 PMCID: PMC6111972 DOI: 10.3390/s18082581] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 07/12/2018] [Accepted: 07/21/2018] [Indexed: 11/16/2022]
Abstract
With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB’s implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB’s potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper.
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Affiliation(s)
- Shu Shen
- School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
- Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China.
| | - Zhao-Qing Wei
- School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
| | - Li-Juan Sun
- School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
- Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China.
| | - Yang-Qing Su
- School of Internet of things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
| | - Ru-Chuan Wang
- School of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
- Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China.
| | - Han-Ming Jiang
- School of Internet of things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
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17
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Otero I, Nieuwenhuijsen MJ, Rojas-Rueda D. Health impacts of bike sharing systems in Europe. ENVIRONMENT INTERNATIONAL 2018; 115:387-394. [PMID: 29669687 DOI: 10.1016/j.envint.2018.04.014] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 04/02/2018] [Accepted: 04/07/2018] [Indexed: 05/19/2023]
Abstract
BACKGROUND Bike-sharing systems (BSS) have been implemented in several cities around the world as policies to mitigate climate change, reduce traffic congestion, and promote physical activity. This study aims to assess the health impacts (risks and benefits) of major BSS in Europe. METHODS We performed a health impact assessment study to quantify the health risks and benefits of car trips substitution by bikes trips (regular-bikes and/or electric-bikes) from European BSS with >2000 bikes. Four scenarios were created to estimate the annual expected number of deaths (increasing or reduced) due to physical activity, road traffic fatalities, and air pollution. A quantitative model was built using data from transport and health surveys and environmental and traffic safety records. The study population was BSS users between 18 and 64 years old. RESULTS Twelve BSS were included in the analysis. In all scenarios and cities, the health benefits of physical activity outweighed the health risk of traffic fatalities and air pollution. It was estimated that 5.17 (95%CI: 3.11-7.01) annual deaths are avoided in the twelve BSS, with the actual level of car trip substitution, corresponding to an annual saving of 18 million of Euros. If all BSS trips replaced car trips, 73.25 deaths could be avoided each year (225 million Euros saving) in the twelve cities. CONCLUSIONS The twelve major Bike-sharing systems in Europe provide health and economic benefits. The promotion of shifting car drivers to use BSS can significantly increase the health benefits. BSS in Europe can be used as a tool for health promotion and prevention.
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Affiliation(s)
- I Otero
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Unidad Docente de Medicina Preventiva y Salud Pública H. Mar- UPF- ASPB, Barcelona, Spain; Municipal Institute of Medical Research (IMIM-Hospital del Mar), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - M J Nieuwenhuijsen
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Municipal Institute of Medical Research (IMIM-Hospital del Mar), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - D Rojas-Rueda
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Municipal Institute of Medical Research (IMIM-Hospital del Mar), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
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18
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Pritchard R. Revealed Preference Methods for Studying Bicycle Route Choice-A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:E470. [PMID: 29518991 PMCID: PMC5877015 DOI: 10.3390/ijerph15030470] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 02/16/2018] [Accepted: 03/05/2018] [Indexed: 11/17/2022]
Abstract
One fundamental aspect of promoting utilitarian bicycle use involves making modifications to the built environment to improve the safety, efficiency and enjoyability of cycling. Revealed preference data on bicycle route choice can assist greatly in understanding the actual behaviour of a highly heterogeneous group of users, which in turn assists the prioritisation of infrastructure or other built environment initiatives. This systematic review seeks to compare the relative strengths and weaknesses of the empirical approaches for evaluating whole journey route choices of bicyclists. Two electronic databases were systematically searched for a selection of keywords pertaining to bicycle and route choice. In total seven families of methods are identified: GPS devices, smartphone applications, crowdsourcing, participant-recalled routes, accompanied journeys, egocentric cameras and virtual reality. The study illustrates a trade-off in the quality of data obtainable and the average number of participants. Future additional methods could include dockless bikeshare, multiple camera solutions using computer vision and immersive bicycle simulator environments.
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Affiliation(s)
- Ray Pritchard
- Department of Architecture and Planning, Faculty of Architecture and Design, NTNU-Norwegian University of Science and Technology, 7491 Trondheim, Norway.
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19
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Zeng Y, Xiang K. Adaptive Sampling for Urban Air Quality through Participatory Sensing. SENSORS 2017; 17:s17112531. [PMID: 29099766 PMCID: PMC5712849 DOI: 10.3390/s17112531] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 10/26/2017] [Accepted: 10/31/2017] [Indexed: 12/02/2022]
Abstract
Air pollution is one of the major problems of the modern world. The popularization and powerful functions of smartphone applications enable people to participate in urban sensing to better know about the air problems surrounding them. Data sampling is one of the most important problems that affect the sensing performance. In this paper, we propose an Adaptive Sampling Scheme for Urban Air Quality (AS-air) through participatory sensing. Firstly, we propose to find the pattern rules of air quality according to the historical data contributed by participants based on Apriori algorithm. Based on it, we predict the on-line air quality and use it to accelerate the learning process to choose and adapt the sampling parameter based on Q-learning. The evaluation results show that AS-air provides an energy-efficient sampling strategy, which is adaptive toward the varied outside air environment with good sampling efficiency.
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Affiliation(s)
- Yuanyuan Zeng
- Electronic Information School, Wuhan University, Wuhan 430072, China.
- Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, China.
| | - Kai Xiang
- School of Information Management and Statistics, Hubei University of Economics, Wuhan 430205, China.
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20
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A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation. SENSORS 2017; 17:s17112498. [PMID: 29088125 PMCID: PMC5712863 DOI: 10.3390/s17112498] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 09/26/2017] [Accepted: 10/12/2017] [Indexed: 11/25/2022]
Abstract
Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets.
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21
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Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users. SUSTAINABILITY 2017. [DOI: 10.3390/su9061060] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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22
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Sun Y, Du Y, Wang Y, Zhuang L. Examining Associations of Environmental Characteristics with Recreational Cycling Behaviour by Street-Level Strava Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14060644. [PMID: 28617345 PMCID: PMC5486330 DOI: 10.3390/ijerph14060644] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 05/05/2017] [Accepted: 06/12/2017] [Indexed: 12/03/2022]
Abstract
Policymakers pay much attention to effectively increasing frequency of people’s cycling in the context of developing sustainable and green cities. Investigating associations of environmental characteristics and cycling behaviour could offer implications for changing urban infrastructure aiming at encouraging active travel. However, earlier examinations of associations between environmental characteristics and active travel behaviour are limited by low spatial granularity and coverage of traditional data. Crowdsourced geographic information offers an opportunity to determine the fine-grained travel patterns of people. Particularly, Strava Metro data offer a good opportunity for studies of recreational cycling behaviour as they can offer hourly, daily or annual cycling volumes with different purposes (commuting or recreational) in each street across a city. Therefore, in this study, we utilised Strava Metro data for investigating associations between environmental characteristics and recreational cycling behaviour at a large spatial scale (street level). In this study, we took account of population density, employment density, road length, road connectivity, proximity to public transit services, land use mix, proximity to green space, volume of motor vehicles and traffic accidents in an empirical investigation over Glasgow. Empirical results reveal that Strava cyclists are more likely to cycle for recreation on streets with short length, large connectivity or low volume of motor vehicles or on streets surrounded by residential land.
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Affiliation(s)
- Yeran Sun
- Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow G12 8RZ, UK.
| | - Yunyan Du
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Yu Wang
- Urban Studies, School of Social and Political Sciences, University of Glasgow, Glasgow G12 8RS, UK.
| | - Liyuan Zhuang
- Urban Studies, School of Social and Political Sciences, University of Glasgow, Glasgow G12 8RS, UK.
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