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

由于从移动电话收集位置信息以及便携设备(例如GPS-全球定位系统)、网络交通控制器和传感器的普及，时空数据增长迅速。这项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。这对于加强我们对机动性以外的变化进程的知识是必要的，在交通运输研究、开发基于位置的服务、城市和区域规划……使用现成的地理信息系统或时空数据库技术模拟变化时，由于它们把重点放在原子数据上，以及它们在合成事实和发现趋势方面的相对笨拙，这是必要的。为了克服这些局限性，开发用于检测时空模式和模拟数千个行为者演化的微观模拟方法，本研究结合了几种技术：地理信息系统、时空数据库、多智能体多尺度模拟、元胞自动机、遗传算法和常用的统计分析方法。该理论框架有多个根基：Hagestrand的时空棱镜、效用理论、进化理论(遗传算法)和概率理论。该方法通过魁北克市的五个应用案例得到验证：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-专业人士居住、专业和家庭轨迹的回顾分析；3-购物行为的多主体建模；4-城市形态的演变建模；5-使用私人和公共交通工具建立城市便利设施的可达性模型。在使用调查期间从人们那里收集的个人数据时，重点放在隐私问题上。

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.