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The purpose of this paper is to detect spatial-temporal clusters of violence in Gwanak-gu, Seoul with space-time permutation scan statistics (STPSS) and identifies the temporal threshold for such detection to alert law enforcement officers quickly.

The case study was the Gwanak Police Station Call Database 2017 where civilian calls reporting violence were georeferenced with coordinated points. In analyzing the database, this study used the STPSS requiring only individual case data, such as time and location, to detect clusters of investigated phenomena. This study executed a series of experiments using different minimum and maximum temporal thresholds in detecting clusters of violence.

Results of the STPSS analyses with different temporal thresholds detected spatial-temporal clusters in Gwanak-gu. Number, location and duration of clusters depended on the temporal settings of the scanning window. Among four models, a model allowing the possible clusters to be detected within a 7-day minimum and 30-day maximum temporal threshold was more representative of reality than other models.

This study illustrates the clustering of violence with the STPSS by detecting spatial-temporal clusters of violence and identifying the appropriate temporal threshold in detecting such clusters. Identification of such a threshold is useful to alert law enforcement officers quickly and enables them to allocate their resources optimally.

The purpose of this paper is to address the urgent need for guiding the construction of information systems for disease surveillance and early warning, given the latest efforts of the report system of public health information over China.

A system framework for disease surveillance and early warning, based on disease clustering test and cluster detection techniques, geographical information system, network and communication is conceived. Through geographical surveillance analysis of severe acute respiratory syndrome occurring in Beijing in 2003, an application example of the framework is illustrated.

Through approaches such as integrating spatial‐time clustering test and cluster detection algorithms, spatial visualization, computer network, wireless communication, it is feasible to build a systematic, automatic, real‐time surveillance and early warning system for prevention and control of disease.

The present study provides an underlying framework for the development of disease surveillance and early warning system enabling data acquisition, data analysis and alarm publishing.

The framework integrates report system of public health information, GIS and disease clustering test and cluster detection techniques into an application, which will significantly enhance the resilience of healthcare facilities. It is supposed to be implemented in near future and provides fundamental support for nation‐wide disease surveillance and early warning.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.