Jane Law

Dr. Jane Law is an Associate Professor at the University of Waterloo, with appointments in the School of Planning (Faculty of Environment) and the School of Public Health Sciences (Faculty of Health). Her research focuses on advancing methodological approaches to understand complex urban systems and their impacts on health and safety outcomes.

Her work integrates spatial epidemiology, crime analysis, and geospatial data science, with research interests in Bayesian spatial modelling, spatiotemporal analysis, and artificial intelligence. She develops and applies advanced methods to examine how built, social, and environmental factors interact to shape neighbourhood-level risks and inequalities.

Dr. Law’s research is grounded in complex systems thinking, moving beyond traditional analytical frameworks to capture the dynamic and interconnected nature of urban environments. Her work contributes to a deeper understanding of spatial patterns in crime, injury, and public health and informs the development of predictive, policy-relevant models for urban planning and population health.

Through her interdisciplinary approach, Dr. Law advances the integration of geospatial analytics with public health and safety research, contributing to evidence-based strategies for healthier, safer, and more equitable communities.

• Spatial epidemiology

• Environmental epidemiology

• Urban health

• Urban safety

• Crime analysis

• Geospatial data science

• Geoinformatics

• Geographic information science

• Spatial statistics

• Bayesian spatial modelling

• Spatiotemporal modelling

• Complex systems

• GeoAI

• Artificial intelligence

• Machine learning

• Health informatics

• Public health analytics

• Neighbourhood effects

• Health inequalities

• Social determinants of health

• Environmental determinants of health

• Environmental exposures

• Built environment

• Spatial demography

• Public health

• Spatial data analysis

• Risk modelling

• Urban planning analytics

Dr. Jane Law’s research program advances methodological innovation in the analysis of complex urban systems and their impacts on health and safety outcomes. Her work lies at the intersection of spatial epidemiology, urban health, and crime analysis, and focuses on developing and applying advanced geospatial and computational approaches to understand how built, social, and environmental factors interact across space and time. By integrating high-resolution spatial data with spatial statistics, Bayesian spatial modelling, and spatiotemporal methods, her research addresses limitations of conventional approaches in capturing fine-scale patterns and neighbourhood-level inequalities.

A central contribution of her work is the incorporation of complex systems thinking into spatial health and crime research. Her research conceptualizes urban environments as dynamic, interconnected systems in which multiple risk factors operate simultaneously and interact nonlinearly. To address this complexity, she develops and applies approaches that integrate artificial intelligence, GeoAI, and machine learning with interpretable modelling frameworks. This enables both improved predictive performance and greater transparency in identifying the relative importance and interactions of key determinants, supporting more robust inference and policy relevance.

Dr. Law’s research advances the integration of geospatial analytics, public health, and urban planning. Her work provides methodological and empirical insights into spatial inequalities in crime, injury, and population health, and supports the development of predictive, scalable, and policy-relevant models. Through this interdisciplinary approach, her research aims to inform evidence-based interventions and planning strategies that promote healthier, safer, and more equitable urban environments.

Dr. Jane Law’s industrial research focuses on applying advanced geospatial analytics to real-world challenges in urban health, safety, and planning. Her work involves collaborating with government agencies, industry partners, and community organizations to develop data-driven solutions that inform policy and decision-making. By integrating spatial data from diverse sources, including administrative records, environmental data, and built environment indicators, she applies spatial statistics, Bayesian modelling, and artificial intelligence and GeoAI approaches to analyze complex urban issues such as crime patterns, injury risks, and health inequalities. Her industrial research focuses on translating methodological advances into practical tools and actionable insights, including predictive models, risk mapping, and decision-support systems. Through these collaborations, she contributes to the development of scalable and policy-relevant approaches that support evidence-based planning and improve health and safety outcomes in urban environments.

• 2000, Ph.D., Geodesy and Geomatics Engineering, University of New Brunswick, Canada

• 1999, University Teaching Diploma, University of New Brunswick, Canada

• 1994, M.Sc., Land Information Systems, Hong Kong Polytechnic University, Hong Kong

• 1985, B.Sc., Surveying and Mapping Sciences, North East London Polytechnic (now University of East London), United Kingdom

• GEOG 281 - Introduction to Geographic Information Systems (GIS)
  • Taught in 2026
• HLTH 455 - Disease Mapping and Geographic Information Systems
  • Taught in 2022, 2024
• HLTH 661 - Geographic Information Systems and Public Health
  • Taught in 2021, 2023, 2024, 2025, 2026
• PLAN 105 - Introduction to Planning Analysis
  • Taught in 2023
• PLAN 205 - Spatial and Demographic Analysis in Planning
  • Taught in 2025
• PLAN 281 - Introduction to Geographic Information Systems (GIS)
  • Taught in 2026
• PLAN 350 - Research Methods for Planners
  • Taught in 2021, 2023, 2024, 2025

* Only courses taught in the past 5 years are displayed.

• 1.     Law, J. & Petric A. (2025). Neighbourhood risk factors and spatiotemporal trends for overdoses following cannabis legalization and pandemic restrictions in Toronto, Canada. Health and place, Vol. 96. https://doi.org/10.1016/j.healthplace.2025.103557

• 2.     Law, J. & Abdullah, A.Y.M.* (2025). Comparisons Between Robbery and Break-And-Enter: Area-Specific Trends, Socioeconomic Risk Factors, and Hotspots Analysis Using a Bayesian Spatial and Spatiotemporal Approach. Geographical Analysis. 57: 1-46.

• 3.     Law, J. & Abdullah, A.Y.M.* (2024). A Bayesian shared component spatial modeling approach for identifying the geographic pattern of local associations: A case study of young offenders and violent crimes in Greater Toronto Area. Crime Science. DOI: 10.1186/s40163-024-00235-5

• 4.     Law, J. & Petric A. (2024). Monitoring day and dark traffic collisions in Toronto neighbourhoods with implications for injury reduction and Vision Zero initiatives: A spatial analysis approach. Accident analysis and prevention. 207: 1-10.

• 5.     Law, J. & Abdullah, A.Y.M.*, (2024). An Offenders-Offenses Shared Component Spatial Model for Identifying Shared and Specific Hotspots of Offenders and Offenses: A Case Study of Juvenile Delinquents and Violent Crimes in the Greater Toronto Area. Journal of Quantitative Criminology, 40(1), pp.75-98.

• 6.     Abdullah, A.Y.M. * & Law, J. (2024). Bayesian Shared Component Spatial Modeling for Assessing the Shared and Age Group-Specific Mental Health Disorder Risk of Young and Old Age Groups: A Case Study of Toronto Neighborhoods, Canada. ISPRS International Journal of Geo-Information, 13(3), 75.

• 7.     Nazia, N. *, Law, J., & Butt, Z. A. (2023). Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada. Health & Place, 80, 102988.

• 8.     Nazia, N*; Law, J & Butt, Z.A. (2022). Spatiotemporal clusters and the socioeconomic determinants ofCOVID-19 in Toronto neighbourhoods, Canada.  Spatial and Spatio-temporal Epidemiology. https://doi.org/10.1016%2Fj.sste.2022.100534.

• 9.     Abdullah, A. Y. M.*, Law, J., Perlman, C. M., & Butt, Z. A. (2022). Age-and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study. JMIR Public Health and Surveillance, 8(7), e34782.

• 10.  Nazia, N.*, Butt, Z. A., Bedard, M. L., Tang, W. C., Sehar, H., & Law, J. (2022). Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review. International Journal of Environmental Research and Public Health, 19(14), 8267.

• 11.  Nazia, N.*, Law, J., & Butt, Z. A. (2022). Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling. Scientific Reports, 12(1), 1-13.

• 12.  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.

• 13.  Rutter, E.C., Tyas, S.L., Maxwell, C.J., Law, J., O’Connell, M., Konnert, C., & 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 2020;10:e037301. DOI: 10.1136/bmjopen-2020-037301

• 14.  Law, J., Quick M. *, & Jadavji, A*. (2020) A Bayesian spatial shared component model for identifying crime-general and crime-specific hotspots, Annals of GIS, DOI: 10.1080/19475683.2020.1720290 (Best Paper award)

• 15.  Oremus, M., Konnert, C., Law, J., Maxwell, C.J., O’Connell, M., & Tyas, S. (2020) Social support and cognitive function in middle- and older-aged adults: descriptive analysis of CLSA tracking data, European Journal of Public Health, Volume 29, Issue 6, December 2019, Pages 1084–1089, https://DOI.org/10.1093/eurpub/ckz047

• 16.  Quick, M.*, Li, G., & Law, J. (2019). Spatiotemporal modelling of correlated small-area outcomes: Analyzing the shared and type-specific patterns of crime and disorder. Geographical Analysis, 51, 221–248, DOI:10.1111/gean.12173

• 17.  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. DOI:10.1177/2399808317744779

• 18.  Leung, A*, Law, J, Cook, M. & Leatherdale, S. (2019). Exploring and visualizing the small-area level socio-economic factors, alcohol availability and built environment influences of alcohol expenditure for the city of Toronto: A spatial analysis approach. Health Promotion and Chronic Disease Prevention in Canada, Vol. 39, No. 1, https://DOI.org/10.24095/hpcdp.39.1.02

• 19.  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. DOI:10.3390/ijerph15040593

• 20.  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. DOI:10.1177/0706743717745870

• 21.  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. DOI:10.1016/j.sste.2017.12.001

• 22.  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. DOI:10.1007/s12061-016-9191-5

• 23.  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. DOI:10.3390/ijgi5090162

• 24.  Luan, H.*, Minaker, L. & Law, J. (2016). Do marginalized neighborhoods have less healthy retail food environments? An analysis using Bayesian spatial latent factor and hurdle models. International Journal of Health Geographics, 15(1), 29. DOI:10.1186/s12942-016-0060-x

• 25.  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. DOI:10.3390/ijgi5070118

• 26.  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. In P. Congdon (ed.) AIMS Public Health, Special issue: Spatial Aspects of Health Methods and Applications, 3(1), 65-82. DOI:10.3934/publichealth.2016.1.65

• 27.  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. Last accessed on 20 June 2018. Available at: http://cjur.uwinnipeg.ca/index.php/cjur/article/view/47

• 28.  Paller, C.*, Christidis, T.*, Majowicz, S., Aramini, J., Law, J., & Bigelow, P. (2016). Use of Admail and a geographic information system to send surveys to target populations. Canadian Journal of Rural Medicine, 21(3), 67-72. PMID:27386913

• 29.  Law, J., Quick, M.*, & Chan, P.W. (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. DOI:10.1177/0042098015576316

• 30.  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, 37. DOI:10.1186/s12942-015-0030-8

• 31.  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. DOI:10.1111/gean.12047

• 32.  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. DOI:10.3390/ijgi3020481

• 33.  Law, J., Quick, M.*, & Chan, P.W. (2014). Analyzing local patterns of crime over time at the small-area level: A Bayesian spatio-temporal modeling approach. Journal of Quantitative Criminology, 30(1), 57-78. DOI:10.1007/s10940-013-9194-1

• 34.  Christidis, T.*, & Law, J. (2013). Mapping Ontario’s wind turbines: Challenges and limitations. ISPRS International Journal of Geo-Information, 2(4), 1092-1105. DOI:10.3390/ijgi2041092

• 35.  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. DOI:10.1007/s10109-012-0164-1s

• 36.  Quick, M.*, & Law, J. (2013). Exploring hotspots of drug offences in Toronto, Ontario: A comparison of four local spatial cluster detection methods. Canadian Journal of Criminology and Criminal Justice, 55(2), 215-238. DOI:10.3138/cjccj.2012.E13

• 37.  Law, J., & Chan, P. (2012). Bayesian spatial random effect modeling for analyzing burglary risks controlling for offender, socioeconomic, and unknown risk factors. Applied Spatial Analysis and Policy, 5, 73-96. DOI:10.1007/s12061-011-9060-1

• 38.  Christidis, T.*, & Law, J. (2012). Annoyance, health effects, and wind turbines: Exploring Ontario’s planning process. Canadian Journal of Urban Research, 21(1), 81-105. Last accessed on 04 July 2018. Available at: https://www.jstor.org/stable/26193899

• 39.  Christidis, T.*, & Law, J. (2012). The use of GIS in wind turbine and wind energy research. Journal of Renewable and Sustainable Energy, 4(1), 127011-127019. DOI:10.1063/1.3673565

• 40.  Law, J., & Chan, P. (2012). Monitoring residual spatial pattern using Bayesian hierarchical spatial modelling for exploring unknown risk factors. Transactions in GIS, 15(4), 521-549. DOI:10.1111/j.1467-9671.2011.01276.x

• 41.  Chan, W.*, Law, J., & Seliske, P. (2011). Bayesian spatial methods for small-area injury analysis: A study of geographic variation of falls in older people in the Wellington-Dufferin-Guelph Health Region in Ontario, Canada. Injury Prevention, 18(5), 303-308. DOI:10.1136/injuryprev-2100-040068

• 42.  Meng, G.*, Law, J., & Thompson, M. (2010). Small-scale health-related indicator acquisition using secondary data spatial interpolation. International Journal of Health Geographics, 9, 50. DOI:10.1186/1476-072X-9-50

• 43.  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. DOI:10.1016/j.sste.2010.03.006

• 44.  Haining, R., Law, J., & Griffith, D. (2009). Modelling small area counts in the presence of overdispersion and spatial autocorrelation. Computational Statistics and Data Analysis, 53(8), 2923-2937. DOI:10.1016/j.csda.2008.08.014

• 45.  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, 170(4), 1019-1034. DOI:10.1111/j.1467-985X.2007.00477.x

• 46.  Haining, R., Law, J., Maheswaran, R., Pearson, T., & Brindley, P. (2007). Bayesian modelling of environmental risk: 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. DOI:10.1007/s00477-007-0134-1

• 47.  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 modelling. Geographical Analysis, 38(2), 140-159. DOI:10.1111/j.0016-7363.2006.00680.x

• 48.  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 for Medical Research, 15, 499-516. DOI:10.1177/0962280206071644

• 49.  Maheswaran, R., Haining, R.P., Brindley, P., Law, J., Pearson, T., Fryers, P.R., Wise, S., & Campbell, M.J. (2005). Outdoor air pollution and stroke in Sheffield, United Kingdom: A small-area level geographical study. Stroke, 36(2), 239-243. DOI:10.1161/01.STR.0000151363.71221.12

• 50.  Maheswaran, R., Haining, R.P., Brindley, P., Law, J., Pearson, T., Fryers, P.R., Wise, S., & Campbell, M. (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. DOI:10.1093/eurheartj/ehi457

• 51.  Law, J., & Haining, R. P. (2004). A Bayesian approach to modelling binary data: The case of high-intensity crime areas. Geographical Analysis, 36(3), 197-216. DOI:10.1111/j.1538-4632.2004.tb01132.x