用户行为的地域性与多样性，驱动云GIS时空非均衡地使用服务资源。捕捉区域特征的用户行为模式带给云GIS的动力效应，优化服务资源分配，可有效解决地理信息普世化应用带来的时空密集服务难题。研究云GIS中用户复杂行为的规律、区域因素和动力机制，探究用户行为与云GIS共同演化的效用逻辑，是回归以人为本，实现因地制宜、可持续发展的地理信息服务科学关键问题之一。本申请系统研究用户行为特征的实证统计方法、行为规律的数学建模和行为惯性的动力机制，最优度量用户行为与服务资源效用关系，精准预测服务资源需求。包括：拓展复杂系统理论，多维统计区域用户行为短、长期特征量；基于随机过程理论，研究定量表达用户行为时空规律；基于人文社会学与人类动力学，研究用户行为长程关联的内在因素与动力机制；面向效用最优目标，预测资源短期动态需求和中长期保有量需求。成果可实现云GIS自适应优化，提升地理信息服务保障能力，促进测绘科学发展

User behavior with regional and various characteristics drives Cloud-enabling GIS to use service resources spatiotemporal unbalanced. Capturing the dynamic effects that user behavior patterns bring to Cloud-enabling GIS, could effectively address the challenge of spatiotemporal intensive service demand which are brought by universal application of big data of geography information. By studying the patterns and dynamic mechanism of user complex behavior and expressing the utility relationship of conjunct evolution laws between user behavior and service resources, it helps people-oriented geography information service to achieve local conditions and sustainable development. This proposal systematically studies the empirical statistics of user behavior characteristics, the quantitative modeling of behavior patterns and the dynamic mechanism of behavior inertia, do prediction for the short & medium-long term demand of service resources by optimal measurement for utility relationship between user behavior and service resource. There are four main research topics. Expand the complex system approaches, do empirical statistics for regional user behavior characteristics of short-term random fluctuations and long-term intermittent stationary. Based on the theory of random process, quantitative model spatiotemporal changes pattern and spatiotemporal local coupling pattern. Based on the theory of humanistic sociology and human behavior dynamics science, study the multi-dimensional research of internal factors and dynamic mechanism for long-term correlation in user behaviors. Based on the optimal utility target, accurately predict the short & medium-long term demand for service resources from users in different region. The research results can be applied to the adaptive optimization of Cloud-enabling GIS, improving the service capacity and support capability for Cloud-enabling GIS, and promoting the development of science of surveying and mapping.