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.

Perennial grasses for recovery of the aggregation capacity of a reconstructed soil in a coal mining area in southern Brazil

The construction of a soil after surface coal mining involves heavy machinery traffic during the topographic regeneration of the area, resulting in compaction of the relocated soil layers. This leads to problems with water infiltration and redistribution along the new profile, causing water erosion and consequently hampering the revegetation of the reconstructed soil. The planting of species useful in the process of soil decompaction is a promising strategy for the recovery of the soil structural quality. This study investigated the influence of different perennial grasses on the recovery of reconstructed soil aggregation in a coal mining area of the Companhia Riograndense de Mineração, located in Candiota-RS, which were planted in September/October 2007. The treatments consisted of planting: T1- Cynodon dactylon cv vaquero; T2 - Urochloa brizantha; T3 - Panicum maximun; T4 - Urochloa humidicola; T5 - Hemarthria altissima; T6 - Cynodon dactylon cv tifton 85. Bare reconstructed soil, adjacent to the experimental area, was used as control treatment (T7) and natural soil adjacent to the mining area covered with native vegetation was used as reference area (T8). Disturbed and undisturbed soil samples were collected in October/2009 (layers 0.00-0.05 and 0.10-0.15 m) to determine the percentage of macro- and microaggregates, mean weight diameter (MWD) of aggregates, organic matter content, bulk density, and macro- and microporosity. The lower values of macroaggregates and MWD in the surface than in the subsurface layer of the reconstructed soil resulted from the high degree of compaction caused by the traffic of heavy machinery on the clay material. After 24 months, all experimental grass treatments showed improvements in soil aggregation compared to the bare reconstructed soil (control), mainly in the 0.00-0.05 m layer, particularly in the two Urochloa treatments (T2 and T4) and Hemarthria altissima (T5). However, the great differences between the treatments with grasses and natural soil (reference) indicate that the recovery of the pre-mining soil structure could take decades.

Organic Matter Fractions and Quality of the Surface Layer of a Constructed and Vegetated Soil After Coal Mining. II - Physical Compartments and Carbon Management Index

Soils constructed after mining often have low carbon (C) stocks and low quality of organic matter (OM). Cover crops are decisive for the recovery process of these stocks, improving the quality of constructed soils. Therefore, the goal of this study was to evaluate the effect of cover crops on total organic C (TOC) stocks, C distribution in physical fractions of OM and the C management index (CMI) of a soil constructed after coal mining. The experiment was initiated in 2003 with six treatments: Hemarthria altissima (T1), Paspalum notatum (T2), Cynodon dactylon (T3), Urochloa brizantha (T4), bare constructed soil (T5), and natural soil (T6). Soil samples were collected in 2009 from the 0.00-0.03 m layer, and the TOC and C stocks in the physical particle size fractions (carbon in the coarse fraction - CCF, and mineral-associated carbon - MAC) and density fractions (free light fraction - FLF; occluded light fraction - OLF, and heavy fraction - HF) of OM were determined. The CMI components: carbon pool index (CPI), lability (L) and lability index (LI) were estimated by both fractionation methods. No differences were observed between TOC, CCF and MAC stocks. The lowest C stocks in FLF and OLF fractions were presented by T2, 0.86 and 0.61 Mg ha-1, respectively. The values of TOC stock, C stock in physical fractions and CMI were intermediate, greater than T5 and lower than T6 in all treatments, indicating the partial recovery of soil quality. As a result of the better adaptation of the species Hemarthria and Brizantha, resulting in greater accumulation of labile organic material, the CPI, L, LI and CMI values were higher in these treatments, suggesting a greater potential of these species for recovery of constructed soils.

Venture capital financing : screening and contracting under assymetric information

Structure of the Thesis This thesis consists of 5 sections. Section 1 starts with the problem definition and the presentation of the objectives of this thesis. Section 2 introduces a presentation of the theoretical foundations of Venture financing and a review of the main theories developed on Venture investing. It includes a taxonomy of contracting clauses relevant in venture contracting, the conflicts they address, and presents some general observations on contractual clauses. Section 3 presents the research findings on the analysis of a European VC's deal flow and investment screening linked to the prevailing market conditions. Section 4 focuses an empirical study of a European VC's investment process, the criteria it uses to make its investments. It presents empirical findings on the investment criteria over time, business cycles, and investment types. It also links these criteria to the VC's subsequent performance. Finally, section 5 presents an empirical research on the comparison of the legal contracts signed between European and United States Venture Capitalists and the companies they finance. This research highlights some of the contracting practices in Europe and the United States.

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/.

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.

Electric Motor and Frequency Converter Markets in Latin America and Especially in the Mining Industry of Chile

Latinalaisen Amerikan osuus maailmantaloudesta on pieni verrattuna sen maantieteelliseen kokoon, väkilukuun ja luonnonvaroihin. Aluetta pidetään kuitenkin yhtenä tulevaisuuden merkittävistä kasvumarkkinoista. Useissa Latinalaisen Amerikan maissa on teollisuutta, joka hyödyntää luonnonvaroja ja tuottaa raaka-aineita sekä kotimaan että ulkomaiden markkinoille. Tällaisia tyypillisiä teollisuudenaloja Latinalaisessa Amerikassa ovat kaivos- ja metsäteollisuus sekä öljyn ja maakaasun tuotanto. Näiden teollisuudenalojen tuotantolaitteiden ja koneiden valmistusta ei Latinalaisessa Amerikassa juurikaan ole. Ne tuodaan yleensä Pohjois-Amerikasta ja Euroopasta. Tässä diplomityössä tutkitaan sähkömoottorien ja taajuusmuuttajien markkinapotentiaalia Latinalaisessa Amerikassa. Tutkimuksessa perehdytään Latinalaisen Amerikan maiden kansantalouksien tilaan sekä arvioidaan sähkömoottorien ja taajuusmuuttajien markkinoiden kokoa tullitilastojen avulla. Chilen kaivosteollisuudessa arvioidaan olevan erityistä potentiaalia. Diplomityössä selvitetään ostoprosessin kulkua Chilen kaivosteollisuudessa ja eri asiakastyyppien roolia siinä sekä tärkeimpiä päätöskriteerejä toimittaja- ja teknologiavalinnoissa.

Educational Data Mining: cerrando el círculo del proceso de aprendizaje en entornos virtuales

Development of methods to explore data from educational settings, to understand better the learning process.

NATURAL REGENERATION STRATUM AS AN INDICATOR OF RESTORATION IN AREA OF ENVIRONMENTAL COMPENSATION FOR MINING LIMESTONE, MUNICIPALITY OF BARROSO, MG, BRAZIL

ABSTRACT This study was conducted in a forest under restoration process, which belongs to the company Holcim Brasil S/A, in the municipality of Barroso, state of Minas Gerais (21º00'to 22º00'S and 43º00' to 44º00'W), where 40 plots (2 x 2 m) were set, spaced at 10 m, forming eight strata parallel to the watercourse present in the area. Floristic composition and natural regeneration stratum were characterized, and the formed strata allowed evaluating whether the riparian vegetation and watercourse influence on the local regeneration. It was found 162 individuals of 13 families, 18 genera and 22 species, and 10,125 individuals/ha were estimated. Successional classes from pioneer and early secondary and zoochory dispersion syndrome prevailed among species and individuals. The watercourse and riparian vegetation did not exercise significant influence (p> 0.05) on the number of species and regenerating individuals among the different strata of the forest. The diversity index of Shannon-Wiener (H') and equability of Pielou (J') were 2.691 and 0.870, respectively. The species Psidium guajava and Myrtaceae families presented the highest VI (value of importance). Natural regeneration analysis showed the low floristic diversity in the area, suggesting that corrective management actions should be adopted.

Assessing the Environmental Value of Water Treatment Solutions in the Mining Industry

Environmental accountability has become a major source of competitive advantage for industrial companies, because customers consider it as relevant buying criterion. However, in order to leverage their environmental responsibility, industrial suppliers have to be able to demonstrate the environmental value of their products and services, which is also the aim of Kemira, a global water chemistry company considered in this study. The aim of this thesis is to develop a tool which Kemira can use to assess the environmental value of their solutions for the customer companies in mining industry. This study answers to questions on what kinds of methods to assess environmental impacts exist, and what kind of tool could be used to assess the environmental value of Kemira’s water treatment solutions. The environmental impacts of mining activities vary greatly between different mines. Generally the major impacts include the water related issues and wastes. Energy consumption is also a significant environmental aspect. Water related issues include water consumption and impacts in water quality. There are several methods to assess environmental impacts, for example life cycle assessment, eco-efficiency tools, footprint calculations and process simulation. In addition the corresponding financial value may be estimated utilizing monetary assessment methods. Some of the industrial companies considered in the analysis of industry best practices use environmental and sustainability assessments. Based on the theoretical research and conducted interviews, an Excel based tool utilizing reference data on previous customer cases and customer specific test results was considered to be most suitable to assess the environmental value of Kemira’s solutions. The tool can be used to demonstrate the functionality of Kemira’s solutions in customers’ processes, their impacts in other process parameters and their environmental and financial aspects. In the future, the tool may be applied to fit also Kemira’s other segments, not only mining industry.

Developing Service-based Solutions for Water Treatment in Finnish Mining Industry

The aim of this master’s thesis is to analyze the mining industry customers' current and future needs for the water treatment services and discover new business development opportunities in the context of mine water treatment. In addition, the study focuses on specifying service offerings needed and evaluate suitable revenue generation models for them. The main research question of the study is: What kind of service needs related to water treatment can be identified in the Finnish mining industry? The literature examined in the study focused on industrial service classification and new service development process as well as the revenue generation of services. A qualitative research approach employing a case study method was chosen for the study. The present study uses customer and expert interviews as primary data source, complemented by archival data. The primary data was gathered by organizing total of 13 interviews, and the interviews were analyzed by using qualitative content analysis. The abductive-logic was chosen as the way of conducting scientific reasoning in this study. As a result, new service proposals were developed for Finnish mine industry suppliers. The main areas of development were on asset efficiency services and process support services. The service needs were strongly associated with suppliers’ know-how of water treatment process optimization, cost-effectiveness as well as on alternative technologies. The study provides an insight for managers that wish to pursue a water treatment services as a part of their business offering.

Automated Purchase to Pay Process Value Modeling and Comparative Process Speeds

The importance of efficient supply chain management has increased due to globalization and the blurring of organizational boundaries. Various supply chain management technologies have been identified to drive organizational profitability and financial performance. Organizations have historically been concentrating heavily on the flow of goods and services, while less attention has been dedicated to the flow of money. While supply chains are becoming more transparent and automated, new opportunities for financial supply chain management have emerged through information technology solutions and comprehensive financial supply chain management strategies. This research concentrates on the end part of the purchasing process which is the handling of invoices. Efficient invoice processing can have an impact on organizations working capital management and thus provide companies with better readiness to face the challenges related to cash management. Leveraging a process mining solution the aim of this research was to examine the automated invoice handling process of four different organizations. The invoice data was collected from each organizations invoice processing system. The sample included all the invoices organizations had processed during the year 2012. The main objective was to find out whether e-invoices are faster to process in an automated invoice processing solution than scanned invoices (post entry into invoice processing solution). Other objectives included looking into the longest lead times between process steps and the impact of manual process steps on cycle time. Processing of invoices from maverick purchases was also examined. Based on the results of the research and previous literature on the subject, suggestions for improving the process were proposed. The results of the research indicate that scanned invoices were processed faster than e-invoices. This is mostly due to the more complex processing of e-invoices. It should be noted however that the manual tasks related to turning a paper invoice into electronic format through scanning are ignored in this research. The transitions with the longest lead times in the invoice handling process included both pre-automated steps as well as manual steps performed by humans. When the most common manual steps were examined in more detail, it was clear that these steps had a prolonging impact on the process. Regarding invoices from maverick purchases the evidence shows that these invoices were slower to process than invoices from purchases conducted through e-procurement systems and from preferred suppliers. Suggestions on how to improve the process included: increasing invoice matching, reducing of manual steps and leveraging of different value added services such as invoice validation service, mobile solutions and supply chain financing services. For companies that have already reaped all the process efficiencies the next step is to engage in collaborative financial supply chain management strategies that can benefit the whole supply chain.

Maximizing of water recovery in nanofiltration of mine process waters by using antiscalants

Reverse osmosis and nanofiltration are among the most effective and widely used desalination and water softening technologies. They can also be used to treat mining wastewaters and are capable of producing water of extremely high purity, regardless of the high concentrations of toxic heavy metals and extreme pH and salinity. However, challenges with recovering the salts and metals from mining wastewaters in exploitable form, as well as problems with scaling still limit the process efficiency and the ratio of purified water recoverable from process waters. To address the problem of membrane scaling caused by calcium sulfate, batch filtration experiments with the Desal-5 DL nanofiltration membrane, three commercial antiscalants and actual mine process water from a copper mine were performed. The aim of these experiments was to find process conditions where maximum water recovery would be achieved before significant scaling or irreversible membrane fouling would occur and to further improve water recovery by addition of antiscalants. Water recovery of 70 % was reached with the experimental setups by optimizing process conditions. PC-504T antiscaling agent was determined to be the most effective of the three antiscalants used and the addition of 5 ppm of PC-504T allowed the water recovery to be further increased from 70 % to 85 % before major scaling was observed. In these conditions 92 % calcium rejection was achieved.

Heat Demand Forecasting Models’ Development: Use of Data Mining Tools in SQL Server Analysis Services

This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.

Investigating the potential of thecamoebians (testate amoebae) as bio-indicators of impact of oil sands mining operations on freshwater environments in Northeastern Alberta, Canada

Thecamoebian (testate amoeba) species diversity and assemblages in reclamation wetlands and lakes in northeastern Alberta respond to chemical and physical parameters associated with oil sands extraction. Ecosystems more impacted by OSPM (oil sands process-affected material) contain sparse, low-diversity populations dominated by centropyxid taxa and Arcella vulgaris. More abundant and diverse thecamoebian populations rich in difflugiid species characterize environments with lower OSPM concentrations. These shelled protists respond quickly to environmental change, allowing year-to-year variations in OSPM impact to be recorded. Their fossil record thus provides corporations with interests in the Athabasca Oil Sands with a potential means of measuring the progression of highlyimpacted aquatic environments to more natural wetlands. Development of this metric required investigation of controls on their fossil assemblage (e.g. seasonal variability, fossilization potential) and their biogeographic distribution, not only in the constructed lakes and wetlands on the oil sands leases, but also in natural environments across Alberta.