Methods for analysis of space-time data: a Bayesian approach

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The proposed research program will address current concerns and raise the quality of geographic research in Canada. Popularity of web technology and mobile devices, such as iPhone or iPad, has posed new challenges to many disciplines in Canada and elsewhere. Data associated with individuals or events can be collected anytime. They contain information about where (geographic coordinates or addresses) and when (hour, minute or second) exactly an event (what) occurred. We are aware that the information can be used to generate knowledge about events that are or will be happening around us at any point in time, which could have a ground-breaking impact on our life, yet we do not know how. Methods developed for analyzing data that contain precise location and time information, referred to as space-time (ST) data, have focused on environmental and climatic sciences, but less so on social, health, or economic sciences. This proposed research program aims to address the above concerns. Bayesian, a relatively new approach of geographical analysis will be adopted to develop statistical methods for analysis of ST data in social, health, or economic sciences. The proposed program consists of short and long term goals of research and training. Short-term goals are to develop quantitative methodologies that would enhance current capabilities to analyze ST data. For instance, in terms of safety, the methods developed will enable the following questions to be answered accurately: Are changes or trends of violence crime in my local areas significantly different from others? If so, what are the associated (local) risk factors that might have caused the difference? Where are the areas that are significantly less safe at a specific season, month, day of the week or hour of the day? What is the probability of my neighbourhood being burgled in the summer? Currently, it is not possible to provide accurate and statistically-sound answers to the above questions. Long-term goals are to extend the ST methods to enable real-time analysis. Questions that can be addressed then will include what is the chance of being a victim or to be rescued at my current location and time (in case of a disaster) according to the real-time response from my smartphone? Through my current NSERC grant, I have established a research team to develop methods for spatial and ST analysis. Our team has published over ten peer-reviewed journal articles on related methods in the past three years. Upon renewal of the grant, more students will be trained to develop and apply the methods using Canadian data. A research centre on ST analysis in Canada could materialize in the longer term. Novel methodologies for analysis of ST data resulted from the research could transform the approach that researchers in many disciplines use and analyze geographic information. Many fields are facing the “Big Data Challenge” – the need for new methods of data analysis including ST analyses is compelling. Some practical significance of the program include 1) enable statistically-sound analysis of ST data; 2) prepare for the changing methods of data collection by mobile devices including the “Google glass”; and 3) Canada is no longer relying on the census long form to collect socio-economic data. Population or other surveys can be conducted via mobile devices to collect ST data that can be analyzed using the methods developed. Surveillance systems for public health and safety in situations of flooding, earthquake, or other crisis can benefit from such modern ways of data collection provided that sound methods of ST analysis are in place. Apart from geographic information science and other fields in Canada that conduct geographical research, global health and safety will advance through the research.