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

时空数据分析方法：贝叶斯方法

• 批准号: RGPIN-2014-06359
• 负责人: Law, Jane
• 金额: $ 1.46万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708314
• 关键词: Methods; analysis; space; time; data

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.

拟议的研究计划将解决当前的问题，提高加拿大地理研究的质量。网络技术和移动的设备（如iPhone或iPad）的普及对加拿大和其他地方的许多学科提出了新的挑战。与个人或事件相关的数据可以随时收集。它们包含关于事件（什么）发生的确切位置（地理坐标或地址）和时间（小时，分钟或秒）的信息。我们意识到，这些信息可以用来产生关于我们周围任何时候正在或将要发生的事件的知识，这可能对我们的生活产生突破性的影响，但我们不知道如何。用于分析包含精确位置和时间信息的数据（称为时空（ST）数据）的方法主要集中在环境和气候科学上，但较少关注社会，健康或经济科学。本研究计划旨在解决上述问题。贝叶斯，一个相对较新的地理分析方法将被采用，以开发统计方法，分析ST数据在社会，健康，或经济科学。 该计划包括短期和长期的研究和培训目标。短期目标是制定量化方法，以提高目前分析ST数据的能力。例如，在安全方面，所开发的方法将能够准确回答以下问题：我所在地区的暴力犯罪变化或趋势是否与其他地区有显著不同？如果是，可能导致差异的相关（当地）风险因素是什么？在特定的季节、月份、一周中的一天或一天中的一小时，哪些地区的安全性明显较低？我的邻居在夏天被盗的可能性有多大？目前还无法对上述问题提供准确和可靠的答案。长期目标是扩展ST方法以实现实时分析。然后可以解决的问题将包括根据我的智能手机的实时响应，在我当前的位置和时间（在发生灾难的情况下）成为受害者或获救的机会有多大？ 通过我目前的NSERC赠款，我已经建立了一个研究团队，开发空间和ST分析的方法。在过去的三年里，我们的团队已经发表了十多篇关于相关方法的同行评议期刊文章。在延长赠款后，将培训更多的学生使用加拿大数据开发和应用这些方法。在加拿大建立一个关于科技分析的研究中心可能会在较长时期内实现。 新的方法分析ST数据的研究可能会改变的方法，研究人员在许多学科使用和分析地理信息。许多领域都面临着“大数据挑战”--迫切需要新的数据分析方法，包括ST分析。该计划的一些实际意义包括：1）能够对ST数据进行可靠的分析; 2）为包括“谷歌眼镜”在内的移动的设备不断变化的数据收集方法做好准备; 3）加拿大不再依赖人口普查长表来收集社会经济数据。可以通过移动的设备进行人口或其他调查，以收集可以使用开发的方法进行分析的ST数据。在洪水、地震或其他危机的情况下，公共卫生和安全监测系统可以从这种现代数据收集方式中受益，前提是要有健全的ST分析方法。除了地理信息科学和加拿大进行地理研究的其他领域外，全球健康和安全将通过研究取得进展。