Community Policing in Switzerland's Major Urban Areas: An Observational Study of the Implementation and Impact Using Geospatial Data Mining

La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.

Application of text-mining for updating protein post-translational modification annotation in UniProtKB.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

Mining the human phenome using allelic scores that index biological intermediates.

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

Tapping the innovation potential of corporate engagement with stakeholders

Summary This dissertation explores how stakeholder dialogue influences corporate processes, and speculates about the potential of this phenomenon - particularly with actors, like non-governmental organizations (NGOs) and other representatives of civil society, which have received growing attention against a backdrop of increasing globalisation and which have often been cast in an adversarial light by firms - as a source of teaming and a spark for innovation in the firm. The study is set within the context of the introduction of genetically-modified organisms (GMOs) in Europe. Its significance lies in the fact that scientific developments and new technologies are being generated at an unprecedented rate in an era where civil society is becoming more informed, more reflexive, and more active in facilitating or blocking such new developments, which could have the potential to trigger widespread changes in economies, attitudes, and lifestyles, and address global problems like poverty, hunger, climate change, and environmental degradation. In the 1990s, companies using biotechnology to develop and offer novel products began to experience increasing pressure from civil society to disclose information about the risks associated with the use of biotechnology and GMOs, in particular. Although no harmful effects for humans or the environment have been factually demonstrated even to date (2008), this technology remains highly-contested and its introduction in Europe catalysed major companies to invest significant financial and human resources in stakeholder dialogue. A relatively new phenomenon at the time, with little theoretical backing, dialogue was seen to reflect a move towards greater engagement with stakeholders, commonly defined as those "individuals or groups with which. business interacts who have a 'stake', or vested interest in the firm" (Carroll, 1993:22) with whom firms are seen to be inextricably embedded (Andriof & Waddock, 2002). Regarding the organisation of this dissertation, Chapter 1 (Introduction) describes the context of the study, elaborates its significance for academics and business practitioners as an empirical work embedded in a sector at the heart of the debate on corporate social responsibility (CSR). Chapter 2 (Literature Review) traces the roots and evolution of CSR, drawing on Stakeholder Theory, Institutional Theory, Resource Dependence Theory, and Organisational Learning to establish what has already been developed in the literature regarding the stakeholder concept, motivations for engagement with stakeholders, the corporate response to external constituencies, and outcomes for the firm in terms of organisational learning and change. I used this review of the literature to guide my inquiry and to develop the key constructs through which I viewed the empirical data that was gathered. In this respect, concepts related to how the firm views itself (as a victim, follower, leader), how stakeholders are viewed (as a source of pressure and/or threat; as an asset: current and future), corporate responses (in the form of buffering, bridging, boundary redefinition), and types of organisational teaming (single-loop, double-loop, triple-loop) and change (first order, second order, third order) were particularly important in building the key constructs of the conceptual model that emerged from the analysis of the data. Chapter 3 (Methodology) describes the methodology that was used to conduct the study, affirms the appropriateness of the case study method in addressing the research question, and describes the procedures for collecting and analysing the data. Data collection took place in two phases -extending from August 1999 to October 2000, and from May to December 2001, which functioned as `snapshots' in time of the three companies under study. The data was systematically analysed and coded using ATLAS/ti, a qualitative data analysis tool, which enabled me to sort, organise, and reduce the data into a manageable form. Chapter 4 (Data Analysis) contains the three cases that were developed (anonymised as Pioneer, Helvetica, and Viking). Each case is presented in its entirety (constituting a `within case' analysis), followed by a 'cross-case' analysis, backed up by extensive verbatim evidence. Chapter 5 presents the research findings, outlines the study's limitations, describes managerial implications, and offers suggestions for where more research could elaborate the conceptual model developed through this study, as well as suggestions for additional research in areas where managerial implications were outlined. References and Appendices are included at the end. This dissertation results in the construction and description of a conceptual model, grounded in the empirical data and tied to existing literature, which portrays a set of elements and relationships deemed important for understanding the impact of stakeholder engagement for firms in terms of organisational learning and change. This model suggests that corporate perceptions about the nature of stakeholder influence the perceived value of stakeholder contributions. When stakeholders are primarily viewed as a source of pressure or threat, firms tend to adopt a reactive/defensive posture in an effort to manage stakeholders and protect the firm from sources of outside pressure -behaviour consistent with Resource Dependence Theory, which suggests that firms try to get control over extemal threats by focussing on the relevant stakeholders on whom they depend for critical resources, and try to reverse the control potentially exerted by extemal constituencies by trying to influence and manipulate these valuable stakeholders. In situations where stakeholders are viewed as a current strategic asset, firms tend to adopt a proactive/offensive posture in an effort to tap stakeholder contributions and connect the organisation to its environment - behaviour consistent with Institutional Theory, which suggests that firms try to ensure the continuing license to operate by internalising external expectations. In instances where stakeholders are viewed as a source of future value, firms tend to adopt an interactive/innovative posture in an effort to reduce or widen the embedded system and bring stakeholders into systems of innovation and feedback -behaviour consistent with the literature on Organisational Learning, which suggests that firms can learn how to optimize their performance as they develop systems and structures that are more adaptable and responsive to change The conceptual model moreover suggests that the perceived value of stakeholder contribution drives corporate aims for engagement, which can be usefully categorised as dialogue intentions spanning a continuum running from low-level to high-level to very-high level. This study suggests that activities aimed at disarming critical stakeholders (`manipulation') providing guidance and correcting misinformation (`education'), being transparent about corporate activities and policies (`information'), alleviating stakeholder concerns (`placation'), and accessing stakeholder opinion ('consultation') represent low-level dialogue intentions and are experienced by stakeholders as asymmetrical, persuasive, compliance-gaining activities that are not in line with `true' dialogue. This study also finds evidence that activities aimed at redistributing power ('partnership'), involving stakeholders in internal corporate processes (`participation'), and demonstrating corporate responsibility (`stewardship') reflect high-level dialogue intentions. This study additionally finds evidence that building and sustaining high-quality, trusted relationships which can meaningfully influence organisational policies incline a firm towards the type of interactive, proactive processes that underpin the development of sustainable corporate strategies. Dialogue intentions are related to type of corporate response: low-level intentions can lead to buffering strategies; high-level intentions can underpin bridging strategies; very high-level intentions can incline a firm towards boundary redefinition. The nature of corporate response (which encapsulates a firm's posture towards stakeholders, demonstrated by the level of dialogue intention and the firm's strategy for dealing with stakeholders) favours the type of learning and change experienced by the organisation. This study indicates that buffering strategies, where the firm attempts to protect itself against external influences and cant' out its existing strategy, typically lead to single-loop learning, whereby the firm teams how to perform better within its existing paradigm and at most, improves the performance of the established system - an outcome associated with first-order change. Bridging responses, where the firm adapts organisational activities to meet external expectations, typically leads a firm to acquire new behavioural capacities characteristic of double-loop learning, whereby insights and understanding are uncovered that are fundamentally different from existing knowledge and where stakeholders are brought into problem-solving conversations that enable them to influence corporate decision-making to address shortcomings in the system - an outcome associated with second-order change. Boundary redefinition suggests that the firm engages in triple-loop learning, where the firm changes relations with stakeholders in profound ways, considers problems from a whole-system perspective, examining the deep structures that sustain the system, producing innovation to address chronic problems and develop new opportunities - an outcome associated with third-order change. This study supports earlier theoretical and empirical studies {e.g. Weick's (1979, 1985) work on self-enactment; Maitlis & Lawrence's (2007) and Maitlis' (2005) work and Weick et al's (2005) work on sensegiving and sensemaking in organisations; Brickson's (2005, 2007) and Scott & Lane's (2000) work on organisational identity orientation}, which indicate that corporate self-perception is a key underlying factor driving the dynamics of organisational teaming and change. Such theorizing has important implications for managerial practice; namely, that a company which perceives itself as a 'victim' may be highly inclined to view stakeholders as a source of negative influence, and would therefore be potentially unable to benefit from the positive influence of engagement. Such a selfperception can blind the firm from seeing stakeholders in a more positive, contributing light, which suggests that such firms may not be inclined to embrace external sources of innovation and teaming, as they are focussed on protecting the firm against disturbing environmental influences (through buffering), and remain more likely to perform better within an existing paradigm (single-loop teaming). By contrast, a company that perceives itself as a 'leader' may be highly inclined to view stakeholders as a source of positive influence. On the downside, such a firm might have difficulty distinguishing when stakeholder contributions are less pertinent as it is deliberately more open to elements in operating environment (including stakeholders) as potential sources of learning and change, as the firm is oriented towards creating space for fundamental change (through boundary redefinition), opening issues to entirely new ways of thinking and addressing issues from whole-system perspective. A significant implication of this study is that potentially only those companies who see themselves as a leader are ultimately able to tap the innovation potential of stakeholder dialogue.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.