{'fraction_non_alphanumeric': 0.02280130293159609, 'fraction_numerical': 0.0, 'mean_word_length': 5.982954545454546, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Spatially Clustered Associations in Health GIS "mashups"

Developments in standards of the Open Geospatial Consortium (OGC) and International Organisation for Standardisation (ISO) along with the server-side and client-side software to allow the implementation of geospatial web services, enable GIS “mashups” to be seamlessly assembled by combining datasets from various sources and semantics. These geospatial “mashups” have huge potential in the health and epidemiological context, to derive intelligent outcomes, such as disease mapping or clustering, environmental risk factor analysis, exposure analysis, orforecasting and modelling of epidemics. However, practical application of these techniques requires efficient geoprocessing services that use pertinent statistical methods or algorithms; and there is frequently a dilemma in balancing the pertinence of the spatial methodology and the efficiency of the web service in terms of performance. This tradeoff between methodological complexity and real-time performance is amplified by the many and complex data sources which are available to be added to a ‘mashup’, and emphasises the need for simple exploratory methods which allow multivariate analysis of spatial data.