The case for inclusive area profiling applied in Geographic Information Systems

Memorandum Surabaya merupakan perusahaan koran mingguan yang berdiri pada 10 November 1969 dan telah sekian tahun menjadi koran populer dibidang media informasi. Sumber daya manusia merupakan salah satu sumber keunggulan kompetitif dan elemen kunci yang penting untuk meraih kesuksesan dalam bersaing mencapai tujuan. Sebuah peningkatan pencatatan data karyawan akan mendorong sumber daya manusia secara keseluruhan dalam kenaikan produktivitas. Berdasarkan hasil survey dan wawancara dengan HRD Manager, Divisi HRD mengalami kesulitan dalam melakukan pencarian data karyawan, serta pembuatan laporan riwayat karyawan sehingga belum bisa membantu HRD Manager dalam mengambil keputusan. Selain itu, pencarian data karyawan selama ini masih membutuhkan waktu yang cukup lama karena pihak HRD harus mencari data karyawan satu per satu pada Microsoft Office Excel. Aplikasi pencatatan data karyawan merupakan sebuah pengembangan yang digunakan untuk mempercepat proses pengolahan data pada Divisi HRD. Kemudian diuji coba dan diimplementasikan kepada pemakai di HRD PT. Memorandum Sejahtera, aplikasi pencatatan data karyawan pada HRD dapat membantu pekerjaan Staf Personalia dan HRD Manager. Hal ini terbukti dari aplikasi dapat mempercepat pelayanan terhadap pencatatan data karyawan, pencarian data karyawan, data pribadi, riwayat pendidikan, riwayat pekerjaan, riwayat jabatan serta mempermudah pembuatan laporan yang dibutuhkan oleh HRD Manager untuk membantu dalam pengambilan keputusan

Open Data for Crime and Place Research: A Practical Guide in R

Access to data in crime and place research has traditionally been reserved for those who have the means to collect fresh data themselves, pay for access, or obtain data through formal data sharing agreements. Even when access is granted, the usage of these data often comes with conditions that circumscribe how the data can be used through licensing or policy (Kitchin, 2014). Even the public dissemination of findings which emerge from analysis might be subject to restrictions. This can lead to unequal access, controlled usage and curb the diffusion of findings, severely limiting the insight that can be obtained from data

Extending geodemographics using data primitives: a review and a methodological proposal

This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same properties as the cluster centre) and the failure of the static label to describe anything about the underlying neighbourhood processes and dynamics. To address these limitations, this paper proposed a data primitives approach. Data primitives are the fundamental dimensions or measurements that capture the processes of interest. They can be used to describe the current state of an area in a multivariate feature space, and states can be compared over multiple time periods for which data are available, through for example a change vector approach. In this way, emergent social processes, which may be too weak to result in a change in a cluster label, but are nonetheless important signals, can be captured. As states are updated (for example, as new data become available), inferences about different social processes can be made, as well as classification updates if required. State changes can also be used to determine neighbourhood trajectories and to predict or infer future states. A list of data primitives was suggested from a review of the mechanisms driving a number of neighbourhood-level social processes, with the aim of improving the wider understanding of the interaction of complex neighbourhood processes and their effects. A small case study was provided to illustrate the approach. In this way, the methods outlined in this paper suggest a more nuanced approach to geodemographic research, away from a focus on classifications and static data, towards approaches that capture the social dynamics experienced by neighbourhoods

Local and Application-Specific Geodemographics for Data-Led Urban Decision Making

This work seeks to introduce improvements to the traditional variable selection procedures employed in the development of geodemographic classifications. It presents a proposal for shifting from a traditional approach for generating general-purpose one-size-fits-all geodemographic classifications to application-specific classifications. This proposal addresses the recent scepticism towards the utility of general-purpose applications by employing supervised machine learning techniques in order to identify contextually relevant input variables from which to develop geodemographic classifications with increased discriminatory power. A framework introducing such techniques in the variable selection phase of geodemographic classification development is presented via a practical use-case that is focused on generating a geodemographic classification with an increased capacity for discriminating the propensity for Library use in the UK city of Leeds. Two local classifications are generated for the city, one a general-purpose classification, and the other, an application-specific classification incorporating supervised Feature Selection methods in the selection of input variables. The discriminatory power of each classification is evaluated and compared, with the result successfully demonstrating the capacity for the application-specific approach to generate a more contextually relevant result, and thus underpins increasingly targeted public policy decision making, particularly in the context of urban planning

Identifying and Predicting Neighbourhood Level Gentrification: A Data Primitive Approach

Identifying and analysing neighbourhood change is a critical task for urban planners and policy makers and is an active academic field. However, traditional approaches to neighbourhood change often rely on temporally static data and methods that reduce complex processes to one cluster label, or one score for example. This leads to a fragmented understanding of neighbourhood dynamics, on a temporal scale that does not align with the processes, resulting in the failure to capture their complex and multifaceted nature. These limitations highlight the importance of adopting new and innovative methods to provide more accurate and dynamic insights into neighbourhood dynamics. This research subsequently proposes a new approach, data primitives, and a methodological framework for their application. Data primitives are measurements of the fundamental components that capture the driving characteristics of clearly conceptualised neighbourhood processes. Their utility is explored in a regional analysis, identifying 123 cycles of gentrification and their respective temporal properties, which are exhaustively validated via Google Earth and Google Street View. This demonstrates the effectiveness of data primitives at capturing processes, and quantifying their changes over time, to provide a more comprehensive picture of neighbourhood change. These validated cycles of gentrification are used as a training dataset for training three machine learning algorithms for predicting gentrification in England. Three models were created to predict the presence of gentrification, the type of gentrification, and the temporal properties of the predicted types of gentrification in England. These predicted cycles of gentrification are explored, generating novel insights for the neighbourhood change and gentrification communities. Overall, the results of this research have important implications for urban planning and policy making, as they can provide a framework for informing decisions on where to invest resources and how to mitigate the potential negative effects of gentrification, in an appropriately scheduled timetable of interventions. They also provide a framework for uncovering novel insights into the complexities of neighbourhood processes, and their impacts upon neighbourhood change, thus developing upon knowledge in suitable academic fields

Evaluación de la actividad científica en ciencia de la información a partir de indicadores bibliométricos y altmétricos

La presente investigación es un análisis de la producción científica en Ciencia de la Informacion (CI), fundamentada en el contexto epistemológico e histórico de la disciplina, para identificar las tendencias de uso de la información en plataformas de publicación formales e informales. A partir de la implementación de indicadores bibliométricos e indicadores alternativos, se pretende establecer. ¿Como la integración de indicadores altimétricos en la evaluación científica, posibilita la identificación de tendencias en la investigación disciplinar? Y si es valido afirmar, que la altmetría es una herramienta confiable y útil para la evaluación de los dominios científicos. Se toma como referente la producción visible en Web of Science durante el periodo 2012- 2016, para identificar las dinámicas científicas de investigación en la CI, a partir de una muestra de 1224 registros en los cuales se utilizan indicadores bibliometricos de producción, citación o impacto e indicadores altimétricos recuperados de las plataformas ResearchGate (RG) y Plum Analytics (PlumX). Los resultados evidencian que los indicadores alternativos aun están en periodo de desarrollo y necesitan normalización; de lo cual se concluye, que la evaluación científica requiere la complementación de modelos métricos clásicos junto a métricas alternativas que permitan identificar las dinámicas sociales y de comunicación que se generan en la comunidad científica más allá del impacto y la citación.This research is an analysis of the scientific activity in Information Science (CI), based on the epistemological and historical context of the discipline, to identify trends in the use of information in formal and informal publishing platforms. Based on the implementation of bibliometric iand alternative indicators, it is intended to establish: How does the integration of altmetric indicators in scientific evaluation make it possible to identify trends in disciplinary research? And, if it is valid to say that altmetrics is a reliable and useful tool for the scientific evaluation of scientific domains. Visible production in Web of Science during the 2012-2016 period is taken as a reference to identify the scientific dynamics of research in the CI, from a sample of 1224 records in which bibliometric indicators of production, citation or impact and altmetric indicators recovered from the ResearchGate (RG) and Plum Analytics (PlumX) platforms are used. The results show that the alternative indicators are still under development and need to be standardized; from which it is concluded that scientific evaluation requires the complementing of classical metric models with alternative metrics that allow identifying the social and communication dynamics generated in the scientific community beyond the impact and citation.Profesional en Ciencia de la Información - Bibliotecólogo (a)Pregrad

Exploring spatial and temporal variation in perception of crime and place using crowdsourced data

To advance and widen the scope of research into the perception of crime and place, innovations in technology for data collection can be utilized as research tools. To date, there has been little exploration into these new methods of data capture. This thesis presents the possibilities of using crowdsourced data collection methods for application to research in environmental criminology. The lack of detailed data on people's experiences and movements at a micro geographical and temporal resolution have impeded the exploration of many of the subtleties of the relationship between crime and place, but this data-gap can be filled by creatively applying new technologies for data collection. The core chapters in this thesis give empirical examples, which demonstrate that spatiotemporal data on people's experiences with crime and disorder during their routine activities can be collected and used to study perception of crime and place. By exploring such crowdsourced data from an environmental criminology framework, I demonstrate how fear of crime varies in place and time, dynamically within individuals, which is not reflected in current measurement approaches. I also propose crowdsourced collection of volunteered geographic information as a proxy measurement for within-day fluctuations for active guardianship, possibly highlighting areas of temporarily increased crime risk. Such information also shows promise in identifying when people are likely to encounter signal disorders as part of their everyday routine activities, leading to possible experiences of fear of crime. These findings provide novel insight into fear of crime, signal disorders, and active guardianship, which allows for the exploration of these concepts as situation-dependent, dynamic experiences. Theoretical development of this thesis is the application of the framework of environmental criminology to the study of subjective perceptions, and the possibility to gather empirical data to support this approach is made possible by the methodological developments presented within. This approach serves as a guideline for studying perception in a way that allows for situational prevention measures to be introduced. Making use of new insight into dynamic variation in context allows for identification of areas with temporarily increased risk of crime, disorder or fear of crime. This thesis contributes to theoretical and methodological growth in the study of perception of crime and place by applying crowdsourcing theory and practice to its measurement