## 2 The Case for Maltese Web-Mapping
### The CRISOLA Model  
A conceptual model was created by [PERSON] [6] to enable an understanding of the complex Maltese data availability situation together with the spatial requirements for data within the different landscapes that are posited by the crime, social and physical worlds. The aim of the model is to identify the impacts of crime on social-capital and socio-cohesion, the impacts on security and safety and the impacts on spatial capital. The study highlighted the need to bring together each aspect and built a mindmap that helps set out a process to depict a basic and generic model on how crime, social and landuse issues interact together, which process also identified techniques and datasets that could be used in the identification and understanding of crime. The model lays the base for social scientists to identify the links between the various variables across the three domains, such as the correlations between location of offender residence and the areas of poverty (Figure 1d). Another aspect emanating from the model concerns the identification of policy changes that would be required should an area be depopulated and the subsequent dilapidation would result in an increase in offences as well as a migration of offenders to the area (Figure 1c). Figure 1: Spatial outputs from the CRISOLA model research (**a**) K-Means clustering by day (blue)-night(red) periods; (**b**) Offence Densities by Dwelling Type (dark higher rates); (**c**) NNH Offence Hotspots (red high-blue low); (**d**) NNH1 Poverty (social) ellipsoids (green) and Offender residence (blue ellipsoids). Figure 1(a-d) depicts outputs from the CRISOLA model research carried out in Malta, which can be employed by policy-makers for their planning functions as based on the urban ecology theory. Figure 1a describes the differences between day-time and night-time crimes and how they migrate towards those areas of recreation during the night, whilst Figure 1b identifies the relationship between dwelling type and offence structure, particularly the high rates of offences occurring in apartment areas. Figure 1c identifies offence hotspots as they occur in an urban area whilst Figure 1d identifies the spatial relationships between areas where offenders live and their direct overlay on areas of poverty. The CRISOLA model outlines criminal activity within the social and physical structures:  
* the crime characteristics through an analysis of offender and offence composition and the interactivity between them [10];
* the social characteristics of an area through an analysis of its poverty/deprivation [26];
* the physical characteristics of an area, particularly its land use, structural and zoning parameters [11]. In order to identify the above structures, a three-phase model was created, with a basic first phase enabling a generic focus on the targets and impacts of each theme on the other two, following which a more detailed second phase was required to identify the interactivity between the three parameters. This is accomplished through the identification of datasets that may be used for analysis. Phase 2 (Figure 2) sought to identify those literature-related issues and integrate them within the model. It reviewed the different theories, datasets, spatio-temporal aspects, predictors and the main tenets that can be used in such a study on crime. Figure 2 outlines those themes and datasets that were identified as potential candidates for the study as well as marking the links between the datasets through connecting lines. Taking the model one step further to Phase 3, a series of statistical measures are listed within each dataset as required to investigate the correlations between the variables. The model does not attempt to solve all CRISOLA issues but depicts the potential future studies that can be attempted within an integrated framework that brings together those isolated datasets used in traditional methods as well as new data inputs from other technological domains.