Interpretation of contextual influences with explanatory tools: Travel mode likelihood mapping using GPS trajectories

[1]  M. Kwan,et al.  Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored , 2017, Health & place.

[2]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[3]  Fuzhong Li,et al.  Neighborhood-level influences on physical activity among older adults: a multilevel analysis. , 2004, Journal of aging and physical activity.

[4]  Weiguo Song,et al.  A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence , 2017 .

[5]  Habib Chaudhury,et al.  Neighbourhood environment and physical activity in older adults. , 2016, Social science & medicine.

[6]  A. Bohra,et al.  Application of GIS in Modeling of Dengue Risk Based on Sociocultural Data : Case of Jalore , Rajasthan , India , 2003 .

[7]  T. Calise,et al.  Environmental Correlates of Recreational Walking in the Neighborhood , 2016, American journal of health promotion : AJHP.

[8]  Seyed Amir Naghibi,et al.  GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran , 2015, Environmental Monitoring and Assessment.

[9]  Bryan J Boruff,et al.  Using GPS technology to (re)-examine operational definitions of ‘neighbourhood’ in place-based health research , 2012, International Journal of Health Geographics.

[10]  Hyung-Sup Jung,et al.  GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea , 2018 .

[11]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[12]  S. Hammer,et al.  International Journal of Health Geographics Combining Google Earth and Gis Mapping Technologies in a Dengue Surveillance System for Developing Countries , 2022 .

[13]  Greg Brown,et al.  Using participatory GIS to measure physical activity and urban park benefits , 2014 .

[14]  M. Kreuter,et al.  Social environment and physical activity: a review of concepts and evidence. , 2006, Social science & medicine.

[15]  M. Kwan.  Beyond Space (As We Knew It): Toward Temporally Integrated Geographies of Segregation, Health, and Accessibility , 2013 .

[16]  Maureen George,et al.  Mapping the urban asthma experience: Using qualitative GIS to understand contextual factors affecting asthma control. , 2015, Social science & medicine.

[17]  Jue Wang,et al.  An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposures and Body Weight , 2018, International journal of environmental research and public health.

[18]  Mei-Po Kwan,et al.  The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect , 2018, International journal of environmental research and public health.

[19]  Mei-Po Kwan,et al.  The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts , 2019, ISPRS Int. J. Geo Inf..

[20]  Laura Davis,et al.  Mapping the walk to school using accelerometry combined with a global positioning system. , 2010, American journal of preventive medicine.

[21]  J. Sallis,et al.  Ecological models of health behavior. , 2008 .

[22]  Jue Wang,et al.  Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity , 2018, ISPRS Int. J. Geo Inf..

[23]  B. Pradhan,et al.  GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks , 2016, Environmental Earth Sciences.

[24]  Chia-Yuan Yu,et al.  Environmental supports for walking/biking and traffic safety: income and ethnicity disparities. , 2014, Preventive medicine.

[25]  Fan Yang,et al.  Using random forest for reliable classification and cost-sensitive learning for medical diagnosis , 2009, BMC Bioinformatics.

[26]  A. Lawson,et al.  Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study , 2012, International Journal of Health Geographics.

[27]  J. Sallis,et al.  Violent crime and outdoor physical activity among inner-city youth. , 2004, Preventive medicine.

[28]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[29]  Kelly R Evenson,et al.  Out and about: association of the built environment with physical activity behaviors of adolescent females. , 2012, Health & place.

[30]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[31]  S. Boslaugh,et al.  Perceptions of neighborhood environment for physical activity: Is it “who you are” or “where you live?” , 2004, Journal of Urban Health.

[32]  G. Kolt,et al.  Green space is associated with walking and moderate-to-vigorous physical activity (MVPA) in middle-to-older-aged adults: findings from 203 883 Australians in the 45 and Up Study , 2013, British Journal of Sports Medicine.

[33]  Uriel Kitron,et al.  Environmental and social determinants of human risk during a West Nile virus outbreak in the greater Chicago area, 2002 , 2004, International journal of health geographics.

[34]  M. Browning,et al.  Within What Distance Does “Greenness” Best Predict Physical Health? A Systematic Review of Articles with GIS Buffer Analyses across the Lifespan , 2017, International journal of environmental research and public health.

[35]  Luc Van Gool,et al.  An Introduction to Random Forests for Multi-class Object Detection , 2011, Theoretical Foundations of Computer Vision.

[36]  Colin P.D. Birch,et al.  Rectangular and hexagonal grids used for observation, experiment and simulation in ecology , 2007 .

[37]  A. King,et al.  Theoretical approaches to the promotion of physical activity: forging a transdisciplinary paradigm. , 2002, American journal of preventive medicine.

[38]  M. Kwan.  The Limits of the Neighborhood Effect: Contextual Uncertainties in Geographic, Environmental Health, and Social Science Research , 2018 .

[39]  A. Herring,et al.  Exploring Associations between Physical Activity and Perceived and Objective Measures of the Built Environment , 2007, Journal of Urban Health.

[40]  A. Stewart Fotheringham,et al.  Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[41]  Mustafa Neamah Jebur,et al.  Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , 2014 .

[42]  L. F. Chaves,et al.  Local impact of temperature and precipitation on West Nile virus infection in Culex species mosquitoes in northeast Illinois, USA , 2010, Parasites & Vectors.

[43]  Charles E Matthews,et al.  The built environment and location-based physical activity. , 2010, American journal of preventive medicine.

[44]  J. Gallacher,et al.  Neighbourhood green space, physical function and participation in physical activities among elderly men: the Caerphilly Prospective study , 2014, International Journal of Behavioral Nutrition and Physical Activity.

[45]  Christine M. Hoehner,et al.  Perceived and objective environmental measures and physical activity among urban adults. , 2005, American journal of preventive medicine.

[46]  Kenney Ng,et al.  Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models , 2016, CHI.

[47]  R. Davey,et al.  Do associations between objectively-assessed physical activity and neighbourhood environment attributes vary by time of the day and day of the week? IPEN adult study , 2017, International Journal of Behavioral Nutrition and Physical Activity.

[48]  K. Stronks,et al.  Social safety, self-rated general health and physical activity: changes in area crime, area safety feelings and the role of social cohesion. , 2015, Health & place.

[49]  Cynthia Rudin,et al.  Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model , 2015, ArXiv.

[50]  A. Bauman,et al.  Environmental and policy interventions to promote physical activity. , 1998, American journal of preventive medicine.

[51]  Emil Pitkin,et al.  Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation , 2013, 1309.6392.

[52]  Biswajeet Pradhan,et al.  A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS , 2013, Comput. Geosci..

[53]  Nancy Wilkins-Diehr,et al.  XSEDE: Accelerating Scientific Discovery , 2014, Computing in Science & Engineering.

[54]  J. Sallis,et al.  Neighborhood-based differences in physical activity: an environment scale evaluation. , 2003, American journal of public health.

[55]  J. Sallis,et al.  Role of Built Environments in Physical Activity, Obesity, and Cardiovascular Disease , 2012, Circulation.

[56]  Marie Lynn Miranda,et al.  Mapping for prevention: GIS models for directing childhood lead poisoning prevention programs. , 2002, Environmental health perspectives.

[57]  Mei-Po Kwan,et al.  Automatic physical activity and in‐vehicle status classification based on GPS and accelerometer data: A hierarchical classification approach using machine learning techniques , 2018, Trans. GIS.

[58]  R. Pate,et al.  Correlates of recreational and transportation physical activity among adults in a New England community. , 2003, Preventive medicine.

[59]  Ewout W Steyerberg,et al.  Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints , 2014, BMC Medical Research Methodology.

[60]  M. Jerrett,et al.  Urban greenness and physical activity in a national survey of Canadians. , 2015, Environmental research.

[61]  Jue Wang,et al.  Uncertainties in the geographic context of health behaviors: a study of substance users’ exposure to psychosocial stress using GPS data , 2018, Int. J. Geogr. Inf. Sci..

[62]  Andrew P. Jones,et al.  The relationship of physical activity and overweight to objectively measured green space accessibility and use. , 2010, Social science & medicine.

[63]  M. Kwan.  The Uncertain Geographic Context Problem , 2012 .

[64]  Shigeru Inoue,et al.  Neighborhood environments and physical activity among adults in 11 countries. , 2009, American journal of preventive medicine.

[65]  John C. Spence,et al.  Toward a comprehensive model of physical activity , 2003 .

[66]  Ross C Brownson,et al.  Built environment attributes and walking patterns among the elderly population in Bogotá. , 2010, American journal of preventive medicine.

[67]  Jue Wang,et al.  Hexagon-Based Adaptive Crystal Growth Voronoi Diagrams Based on Weighted Planes for Service Area Delimitation , 2018, ISPRS Int. J. Geo Inf..

[68]  Elizabeth C. Delmelle,et al.  Exploring spatio-temporal commuting patterns in a university environment , 2012 .

[69]  C. Nagel,et al.  The relation between neighborhood built environment and walking activity among older adults. , 2008, American journal of epidemiology.

[70]  Zohre Sadat Pourtaghi,et al.  GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran , 2016, Environmental Earth Sciences.

[71]  Dieu Tien Bui,et al.  Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression , 2016, Remote. Sens..