Context-sensitive analysis of spatio-temporal trajectories within geogrphical information systems

由于从移动的电话收集位置以及便携式设备（例如GPS -全球定位系统）、网络交通控制器和传感器的普及，时空数据正在快速增长。本研究计划的目的是开发能够综合运动过程和模式的高效时空数据模型。这是为了增强我们对移动性以外的变化过程的了解，并应用于交通研究，基于位置的服务的发展，城市和区域规划......使用现成的地理信息系统（GIS）或时空数据库技术对变化进行建模受到阻碍，因为它们专注于原子数据，并且在综合事实和发现趋势方面相对笨拙。为了克服这些局限性，并开发微观模拟方法检测时空模式和模拟成千上万的演员的演变，本研究是结合几种技术：地理信息系统，时空数据库，多代理多尺度模拟，元胞自动机，遗传算法和通常的统计分析方法。其理论框架有多重根源：哈格斯特兰德的时空棱镜、效用理论、进化论该方法在魁北克市的五个应用案例中得到了验证：1-考虑家庭结构、空间和时间限制的个人每周活动/旅行计划，2-回顾分析专业人员的居住、职业和家庭轨迹，3-分析职业和家庭轨迹。3-购物行为的多主体模型; 4-城市形态演变的模型; 5-使用私人和公共交通工具的城市设施的模型。在使用调查期间从人们收集的个人数据时，强调隐私问题。

Spatio-temporal data are growing fast, due to the collection of position from mobile phones and the generalization of portable devices (e.g. GPS - global positioning system), network traffic controllers and sensors. The purpose of this research program is to develop efficient spatio-temporal data models able to synthesize movement processes and patterns. That is needed for enhancing our knowledge of change processes beyond mobility, with applications in transportation studies, development of location-based services, urban and regional planning...Modelling change using off-the-shelf geographical information systems (GIS) or spatio-temporal databases technologies is impeded by the focus they put on atomistic pieces of data and their relative clumsiness to synthesize facts and discover trends. To overcome those limitations and to develop micro-simulation approaches for detecting spatio-temporal patterns and for simulating the evolution of thousands of actors, this research is combining several techniques: GIS, spatio-temporal databases, multi-agent multi-scale simulation, cellular automata, genetic algorithms and usual statistical analysis methods. The theoretical framework has multiple roots: Hagestrand's space-time prism, utility theory, evolution theory (genetic algorithms) and probability theory.The methodology is validated using five application cases in Quebec City: 1- weekly activity/travel scheduling made by individuals considering household structure, space and time constraints; 2- retrospective analysis of residential, professional and household trajectories of professionals; 3- multi-agent modelling of shopping behaviour; 4- modelling evolution of the urban form; 5- modelling accessibility to urban amenities using private and public transportation means. Emphasis is put on privacy issues while using personal data collected from people during surveys.