Publications
Journal Articles (*Denotes student co-authors)
Abdullah, A. Y. M.*, Law, J., Perlman, C. M., & Butt, Z. A. (2022). The Age-Sex Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study JMIR Public Health and Surveillance. 25/05/2022:34782 (in press)
Abdullah, A. Y. M.*, Law, J., Butt, Z. A., & Perlman, C. M. (2021). Understanding the Differential Impact of Vegetation Measures on Modeling the Association between Vegetation and Psychotic and Non-Psychotic Disorders in Toronto, Canada. International Journal of Environmental Research and Public Health, 18(9), 4713.
Law, J., Quick, M.*, & Jadavji, A*. (2020). A Bayesian spatial shared component model for identifying crime-general and crime-specific hotspots. Annals of GIS, 26(1), 65-79.
Rutter, E. C., Tyas, S. L., Maxwell, C. J., Law, J., O'Connell, M. E., Konnert, C. A., & Oremus, M. (2020). Association between functional social support and cognitive function in middle-aged and older adults: a protocol for a systematic review. BMJ open, 10(4), e037301.
Oremus, M., Tyas, S. L., Maxwell, C. J., Konnert, C., O’Connell, M. E., & Law, J. (2020). Social support availability is positively associated with memory in persons aged 45–85 years: a cross-sectional analysis of the Canadian longitudinal study on aging. Archives of gerontology and geriatrics, 86, 103962.
Oremus, M., Konnert, C., Law, J., Maxwell, C. J., O’Connell, M. E., & Tyas, S. L. (2019). Social support and cognitive function in middle-and older-aged adults: descriptive analysis of CLSA tracking data. European journal of public health, 29(6), 1084-1089.
Quick, M.*, Law, J., & Li, G. (2019). Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends. Environment and Planning B: Urban Analytics and City Science, 46(6), 1018-1035.
Quick, M.*, Li, G., & Law, J. (2019). Spatiotemporal Modeling of Correlated Small‐Area Outcomes: Analyzing the Shared and Type‐Specific Patterns of Crime and Disorder. Geographical analysis, 51(2), 221-248.
Leung, A.*, Law, J., Cooke, M., & Leatherdale, S. (2019). Exploring and visualizing the small-area-level socioeconomic factors, alcohol availability and built environment influences of alcohol expenditure for the City of Toronto: a spatial analysis approach. Maladies Chroniques et Blessures au Canada, 39(1).
Perlman, C. M., Law, J., Luan, H.*, Rios, S., Seitz, D., & Stolee, P. (2018). Geographic clustering of admissions to inpatient psychiatry among adults with cognitive disorders in Ontario, Canada: does distance to hospital matter?. The Canadian Journal of Psychiatry, 63(6), 404-409.
Law, J., & Perlman, C. (2018). Exploring geographic variation of mental health risk and service utilization of doctors and hospitals in Toronto: A shared component spatial modeling approach. International journal of environmental research and public health, 15(4), 593.
Luan, H.*, Law, J., & Lysy, M. (2018). Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment. Spatial and spatio-temporal epidemiology, 24, 39-51.
Quick, M.*, Law, J., & Luan, H.* (2017). The influence of on-premise and off-premise alcohol outlets on reported violent crime in the region of Waterloo, Ontario: Applying Bayesian spatial modeling to inform land use planning and policy. Applied Spatial Analysis and Policy, 10(3), 435-454.
Luan, H.*, Minaker, L. M., & Law, J. (2016). Do marginalized neighbourhoods have less healthy retail food environments? An analysis using Bayesian spatial latent factor and hurdle models. International Journal of Health Geographics, 15(1), 1-16.
Quick, M.*, Law, J., Christidis, T.*, & Paller, C. (2016). Exploring the socioeconomic composition of wind farm communities in Ontario: implications for wind farm planning and policy. Canadian Journal of Urban Research, 25(2), 62-72.
Luan, H.*, Quick, M.*, & Law, J. (2016). Analyzing local spatio-temporal patterns of police calls-for-service using Bayesian integrated nested Laplace approximation. ISPRS International Journal of Geo-Information, 5(9), 162.
Du, Y.*, & Law, J. (2016). How do vegetation density and transportation network density affect crime across an urban central-peripheral gradient? A case study in Kitchener—Waterloo, Ontario. ISPRS International Journal of Geo-Information, 5(7), 118.
Law, J., Quick, M.*, & Chan, P. (2016). Open area and road density as land use indicators of young offender residential locations at the small-area level: A case study in Ontario, Canada. Urban Studies, 53(8), 1710-1726.
Law, J. (2016). Exploring the specifications of spatial adjacencies and weights in Bayesian spatial modeling with intrinsic conditional autoregressive priors in a small-area study of fall injuries. AIMS public health, 3(1), 65.
Luan, H.*, Law, J., & Quick, M.* (2015). Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach. International journal of health geographics, 14(1), 1-11.
Law, J., Quick, M.*, & Chan, P. W. (2015). Analyzing Hotspots of Crime Using a Bayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in the Greater Toronto Area. Geographical Analysis, 47(1), 1-19.
Luan, H.*, & Law, J. (2014). Web GIS-based public health surveillance systems: a systematic review. ISPRS International Journal of Geo-Information, 3(2), 481-506.
Law, J., Quick, M.*, & Chan, P. (2014). Bayesian spatio-temporal modeling for analysing local patterns of crime over time at the small-area level. Journal of quantitative criminology, 30(1), 57-78.
Christidis, T.*, & Law, J. (2013). Mapping Ontario’s wind turbines: Challenges and limitations. ISPRS International Journal of Geo-Information, 2(4), 1092-1105.
Quick, M.*, & Law, J. (2013). Exploring hotspots of drug offences in Toronto: A comparison of four local spatial cluster detection methods. Canadian journal of criminology and criminal justice, 55(2), 215-238.
Law, J., & Quick, M.* (2013). Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach. Journal of Geographical Systems, 15(1), 89-113.
Chan, W. C.*, Law, J., & Seliske, P. (2012). Bayesian spatial methods for small-area injury analysis: a study of geographical variation of falls in older people in the Wellington–Dufferin–Guelph health region of Ontario, Canada. Injury prevention, 18(5), 303-308.
Law, J., & Chan, P. W. (2012). Bayesian spatial random effect modelling for analysing burglary risks controlling for offender, socioeconomic, and unknown risk factors. Applied Spatial Analysis and Policy, 5(1), 73-96.
Christidis, T.*, & Law, J. (2012). The use of geographic information systems in wind turbine and wind energy research. Journal of Renewable and Sustainable Energy, 4(1), 012701.
Christidis, T.*, & Law, J. (2012). Annoyance, health effects, and wind turbines: exploring Ontario's planning processes. Canadian Journal of Urban Research, 21(1), 81-105.
Law, J., & Chan, P. W. (2011). Monitoring residual spatial patterns using Bayesian hierarchical spatial modelling for exploring unknown risk factors. Transactions in GIS, 15(4), 521-540.
Meng, G., Law, J., & Thompson, M. E. (2010). Small-scale health-related indicator acquisition using secondary data spatial interpolation. International Journal of Health Geographics, 9(1), 1-17.
Haining, R., Li, G., Maheswaran, R., Blangiardo, M., Law, J., Best, N., & Richardson, S. (2010). Inference from ecological models: estimating the relative risk of stroke from air pollution exposure using small area data. Spatial and spatio-temporal epidemiology, 1(2-3), 123-131.
Haining, R., Law, J., & Griffith, D. (2009). Modelling small area counts in the presence of overdispersion and spatial autocorrelation. Computational Statistics & Data Analysis, 53(8), 2923-2937.
Haining, R., & Law, J. (2007). Combining police perceptions with police records of serious crime areas: a modelling approach. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(4), 1019-1034.
Haining, R., Law, J., Maheswaran, R., Pearson, T., & Brindley, P. (2007). Bayesian modelling of environmental risk: example using a small area ecological study of coronary heart disease mortality in relation to modelled outdoor nitrogen oxide levels. Stochastic Environmental Research and Risk Assessment, 21(5), 501-509.
Maheswaran, R., Haining, R. P., Pearson, T., Law, J., Brindley, P., & Best, N. G. (2006). Outdoor NOx and stroke mortality: adjusting for small area level smoking prevalence using a Bayesian approach. Statistical methods in medical research, 15(5), 499-516.
Law, J., Haining, R., Maheswaran, R., & Pearson, T. (2006). Analyzing the relationship between smoking and coronary heart disease at the small area level: a Bayesian approach to spatial modeling. Geographical Analysis, 38(2), 140-159.
Maheswaran, R., Haining, R. P., Brindley, P., Law, J., Pearson, T., Fryers, P. R., ... & Campbell, M. J. (2005). Outdoor air pollution, mortality, and hospital admissions from coronary heart disease in Sheffield, UK: a small-area level ecological study. European heart journal, 26(23), 2543-2549.
Law, J., & Haining, R. (2004). A Bayesian approach to modeling binary data: The case of high‐intensity crime areas. Geographical Analysis, 36(3), 197-216.
Van Dijk, D., & Law, J. (1995). Sublimation and aeolian sand movement from a frozen surface: experimental results from Presqu'ile Beach, Ontario. Geomorphology, 11(3), 177-187.
Book Chapters
Quick, M.*, & Law, J. (2015). Analyzing the Influence of Ethnic Composition and Immigrant Residents on the Spatial Distribution of Violent Crime. In Advances in Spatial Data Handling and Analysis (pp. 227-243). Springer, Cham.
Haining, R. P., & Law, J. (2011). Geographical information systems models and spatial data analysis (pp. 377-401). World Scientific Publishing Co. Pte. Ltd.
Law, J. & Chan, P. (2009). GIS in Public Health. In W. Dong (ed.), Public Health Sciences, (pp. 199-218) Beijing: Renmin University Press.
Law, J. & Willms, D. (2002). Provincial maps depicting neighborhood types. In D. Willms (ed.), Vulnerable Children: Findings from Canada’s National Longitudinal Survey of Children and Youth, (pp. 389-406) Alberta: University of Alberta Press and Human Resources Development Canada.