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Item Environmental risk factors influencing bicycle theft:(PubMed Central, 2016) Mburu, Lucy W.; Helbich, MarcoAbstract Urban authorities are continuously drawing up policies to promote cycling among commuters. However, these initiatives are counterproductive for the targeted objectives because they increase opportunities for bicycle theft. This paper explores Inner London as a case study to address place-specific risk factors for bicycle theft at the street-segment level while controlling for seasonal variation. The presence of certain public amenities (e.g., bicycle stands, railway stations, pawnshops) was evaluated against locations of bicycle theft between 2013 and 2016 and risk effects were estimated using negative binomial regression models. Results showed that a greater level of risk stemmed from land-use facilities than from area-based socioeconomic status. The presence of facilities such as train stations, vacant houses, pawnbrokers and payday lenders increased bicycle theft, but no evidence was found that linked police stations with crime levels. The findings have significant implications for urban crime prevention with respect to non-residential land use.Item Modeling spatial interactions between areas to assess the burglary risk(MDPI, 2016) Mburu, Lucy W.; Bakillah, MohamedAbstract It is generally acknowledged that the urban environment presents different types of risk factors, but how the structural effects of areas influence the risk levels in neighboring areas has been less widely investigated. This research assesses the local effects of burglary contributory factors on burglary over small areas in a large metropolitan region. A comparative framework is developed for analyzing the effects of geographic dependence on burglary rates, and for assessing how such dependence conditions the community context and the urban land use. A local indicators spatial autocorrelation analysis assesses burglaries over five years (2011–2015) to identify risk clusters. Thereafter, effects of different variables (e.g., unemployment, building density) on burglary frequency are estimated in a series of regression models while controlling for changes in the risk levels of nearby surrounding areas. Results uncover strong evidence that the configuration of the surroundings influences risk. After controlling for area-based interaction, patterns are identified that contrast with the previous literature, such as lower burglary frequency in areas with higher tenancy in social housing units. Together the findings demonstrate that the spatial arrangement of areas is as crucial as contextual crime factors, particularly when assessing the risk for small areas.Item A knowledge-as-a-service support framework for ambient learning in Kenya(13th IADIS International Conference Information Systems, 2020) Mburu, Lucy W.; Karanja, Richard; Nyaga, Simon M.Knowledge as a Service (KaaS) is a relatively new model, albeit one that is rapidly gaining popularity within cloud computing environments. Over the recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. This need stems from the knowledge that users will be able to access applications and the information therein on demand, without the restrictions that are usually imposed by time and space. The KaaS model terms knowledge as the understanding of information based on its relevance to a specific context and problem area, thus forming a valuable resource for the human decision-making process. As motivated by the global sustainable development goal of ensuring inclusive and equitable quality education to promote learning opportunities for all, this research has developed a framework that is hinged on KaaS and utilizes knowledge from ambient learning systems. The main aim is to provide a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve the quality of education on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge. The research outcomes will benefit knowledge consumers such as the developers of ambient learning systems.Item A contextualized and personalized model to predict user interest using location-based social networks(Elsevier Ltd., 2016) Mburu, Lucy W.; Li, Ming; Sagl, Günther; Fan, HongchaoThe accurate determination of user interest in terms of geographic information is essential to numerous mobile applications, such as recommender systems and mobile advertising. User interest is greatly influenced by the usage context and varies across individuals; therefore, a user interest model should incorporate these individual needs and propensities. In this paper, we present an approach to model user interest in a contextualized and personalized manner based on location-based social networks. Multinomial logistic regression is employed to quantify the relationship between user interest and usage context at both the aggregate and individual levels. The proposed approach is tested in a real-world application using Foursquare check-ins issued between February and June 2014 in the three major cities of Chicago, Los Angeles and New York. Results demonstrate the capability of the contextualization process for capturing contextual influences on user interest, and that such influences can be observed at a fine-grained scale at the individual level through the personalization process. The proposed approach therefore enables contextualized and personalized estimation of user interest, thereby contributing useful information to follow-up mobile applications.Item Crime risk estimation with a commuter- harmonized ambient population(Taylor & Francis Group, 2016) Mburu, Lucy W.; Helbich, MarcoResidential population data are frequently employed to link the crime incidence of an area with the number of residents to estimate the underlying risk. Human mobility patterns cause shifts in the baseline population, however, that can potentially influence the crime statistics. This study therefore employed an ambient population that combined residential population data with data depicting the commuting activity in small administrative areas. The effects of the commuter-harmonized ambient population on crime were then evaluated in a series of negative binomial regression models. The models also controlled for criminogenic factors and incorporated eigenvector spatial filtering to adjust for spatial effects. The results show significant effects of commuting patterns on crime outcomes. For certain crimes, such as violence, theft, and disorder, the inbound commuters are significantly associated with high risk. It was further discovered that an offset variable comprising the commuter-harmonized ambient population data models the crime outcomes more reliably than when residential population data are used. Spatial filtering was found to effectively eradicate residual spatial autocorrelation after accounting for effects of the predictor variables. We conclude that calculating crime rates using the residential population does not constitute an accurate risk measure and that the ambient population has crucial implications for realistic and reliable target representation and crime modeling.Item Analysis of community properties and node properties to understand the structure of the bus transport network(ScienceDirect, 2016) Mburu, Lucy W.; Sun, Yeran; Wang, ShaohuaAkin to most infrastructures, intraurban bus networks are large and highly complex. Understanding the composition of such networks requires an intricate decomposition of the network into modules, taking into account the manner in which network links are distributed among the nodes. There exists for each set of highly interlinked nodes little connectivity with the next set of highly interlinked nodes. This inherent property of nodes makes community detection a popular approach for analyzing the structure of complex networks. In this study, we attempt to understand the structure of the intraurban bus network of Ireland’s capital city, Dublin in a two-step approach. We first analyze the modular structure of the network by identifying potential communities. Secondly, we assess the prominence of each network node by examining the module-based topological properties of the nodes. Results of this empirical study reveal a clear pattern of independent communities, indicating thus, an implicit multi-community structure of the intraurban bus network. Examination of the geographic characteristics of the identified communities shows a degree of socio-economic divisions of the Dublin city. Furthermore, a large majority of the important nodes (vital transportation hubs) are located at the city center, implying that most of the bus lines in Dublin city tend to intersect the city’s core.Item Communities as neighborhood guardians: A spatio-temporal analysis of community policing in Nairobi's suburbs(Springer, 2017) Mburu, Lucy W.; Helbich, MarcoThe efficacy of citizens to participate in neighborhood-watch activities and report signs of trouble is important for safeguarding communities against crime. Community policing is a key policing strategy for utilizing the capability of residents to solve local crime-related problems. However, variability in social cohesion among communities profoundly affects the contribution of individuals towards policing. After 7 years of a community policing intervention in suburban Nairobi, Kenya, this study assesses the program as a state-initiated and community-sustained security venture. We compare micro-scaled concentrations of different property and violent crimes to identify geographic variations over time using kernel density estimates and spatio-temporal scan statistics. Multi-level regression models assess the direct and conditioned perceptions of individuals and their neighbors, and how these perceptions influenced crime variation during the pre- and post-intervention periods of community policing. Both the density estimates and the scan statistics pinpoint a disproportionate crime reduction across neighborhoods. The research findings also depict an interaction between the communal willingness to participate in neighborhood-watch activities and the relative crime decline. In particular, those communities that have good relations with the police are more inclined to involve themselves in community policing. The findings of this study are discussed in terms of their implications for policy.Item Evaluating the accuracy and effectiveness of criminal geographic profiling methods: the case of Dandora, Kenya(Taylor & Francis Group, 2014) Mburu, Lucy W.; Helbich, MarcoCriminal geographic profiling (CGP) prioritizes offender search, extensively reducing the resources expended in criminal investigations. The utility of CGP has, however, remained unclear when variations in environmental characteristics and offense type are introduced. This study evaluates several CGP strategies with data from Dandora, a small but densely populated suburb of Nairobi, Kenya. The research employs error distance and search-cost measures to determine CGP accuracy. Characterized by much shorter journeys to crime than those observed in Western cities, this study discovers significant applicability of CGP strategies in prioritizing offender searches. The negative exponential CGP strategy is identified to generate the most accurate geo-profiles.Item Modeling and mapping crime in Eastern Nairobi, Kenya(AGILE PhD School, 2013) Mburu, Lucy W.This working paper provides a description of the phases of my PhD study. Drawing on assumptions from various theories of environmental criminology, this study applies various crime mapping methodologies to observe geographic and temporal patterns of crime in the eastern part of the Kenyan capital city, Nairobi. This paper outlines the first completed phase which employs criminal geographic profiling to predict offender abodes, and also briefly identifies the next two phases of the study that involve spatio-temporal analysis and a regression modeling respectively. Results from the completed study have potential implications on the prediction and ultimate reduction of criminality, both within the Nairobi capital and also in other cities with similar spatial patterns.Item Applying Data Mining in Graduates’ Employability : A Systematic Literature Review(International Journal of Engineering Pedagogy, 2023) Mburu, Lucy W.; Mwendia, Simon N.; Mpia, Héritier N.Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research. Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.