The sharing of geographical analysis models is of crucial importance for simulating geographic processes and phenomena in the current geographical information systems (e.g. Digital Earth), but there remain some issues that have not been completely resolved. The challenges include, eliminating model heterogeneity and searching for suitable infrastructures to support the open sharing and effective execution of models. Taking advantage of cloud computing, this article aims to address the above issues and develop an open environment for geographical analysis model sharing. On the basis of the analysis of the applicability of cloud computing, the architecture of the open environment is proposed. More importantly, key strategies designed for heterogeneous model description, model encapsulating as well as model deploying and transparent accessing in the cloud are discussed in detail to establish such an environment. Finally, the prototype environment is implemented, and experiments were conducted to verify the environment's feasibility to support the sharing of geographical analysis models.

Existing online virtual worlds, or electronic environments, are of great significance to social science research, but are somewhat lacking in rigour. One reason is that users might not participate in those virtual worlds in the way they act in real daily life, communicating with each other in familiar environments and interacting with natural phenomena under the constraints of the human–land relationship. To help solve this problem we propose the real-geographic-scenario-based virtual social environment (RGSBVSE). The aim is to enhance the ability of current virtual worlds in social issues studies by promoting virtual geographic environments that are built with real scenarios in the physical world. In this paper we first discuss the potential shortage of current virtual worlds for serious social research. We then explain how real geographic scenarios can contribute to building a virtual social environment by providing (1) real geographic data, including the time dimension, in terms of data acquisition and organisation; (2) dynamic or real-time natural phenomena and processes for scenario simulation and expression; (3) shared spaces that enhance participants' interaction through a mix of virtuality and reality; and (4) shared hot spots of social phenomena for researchers from multidisciplinary (eg, sociology, psychology) performing collaborative research. Furthermore, two of our projects, the virtual Chinese University of Hong Kong and the Virtual Globe of the Chinese Family Tree, are introduced as case studies, to illustrate how the RGSBVSE can play a significant role in a number of critical social research issues from the local to regional scale.

In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts.