Constructing geographic areas for homicide research : a case study of New Orleans

Because homicides are rare events, criminologists must often deal with the Small Population Problem, which creates unreliable homicide rates based on arbitrarily delineated census tracts of low population. These rates lead to violations in several assumptions required in statistical analysis. This study proposes the Regionally Constrained Agglomerative Clustering and Partitioning (REDCAP) method to mitigate the Modifiable Areal Unit Problem and solve the Small Population Problem by constructing new, larger regions with sufficient minimum populations for homicide rate calculation. This method is used for a case study of New Orleans, Louisiana, to test the relationship between concentrated disadvantage and homicide. Ordinary Least Squares and Geographically Weighted Regressions are conducted with the data both before and after the REDCAP operation. Results for the standard census tract layer show a weak and insignificant relationship between concentrated disadvantage and homicide because of extremely unreliable rate estimates. After the REDCAP operation, variables show a more normal distribution and reduced variability; moreover, regression results confirm a strong and positive relationship between concentrated disadvantage and homicide. This study shows viability for REDCAP as a regionalization method for further studies on violent crime, namely its ability to provide more stable data for improved reliability in crime rate calculations. Additionally, this study provides implications for public policy, specifically social cohesion and efficacy policies, including community-oriented policing

Knowledge discovery process for description of spatially referenced clusters

Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.Facultad de Informátic

Knowledge discovery process for description of spatially referenced clusters

Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.Facultad de Informátic

Системные основы интеллектуального анализа геопространственных данных

Здійснено оглядове дослідження наукового напряму інтелектуального аналізу геопросторових даних (ІАГД). Виявлено основні передумови формування цього напряму і його зв’язок із геоінформатикою, системним аналізом та інтелектуальним аналізом даних. Проведено бібліографічне дослідження зарубіжних і вітчизняних публікацій в галузі ІАГД. У ході дослідження подано визначення ІАГД, виявлено основні завдання, функції та етапи його проведення, визначено коло перспективних напрямків розвитку та його зв’язок з підтримкою ухвалення рішень у регіональному управлінні. Із використанням ІАГД методів кластеризації гарячих точок проведено дослідження перевищення гранично допустимих концентрацій урану в підземних водах на території України на основі даних геологічних зйомок і виявлено зони обмеженнями використання підземних вод.A survey of geospatial data mining (GSDM) research was conducted. The basic prerequisites for the emergence of this research area and its relation to geoinformatics, systems analysis, and data mining were discovered. A bibliographic study of foreign and Ukrainian publications in the field of GSDM was conducted. During this study, a definition for GSDM was provided. The main tasks, functions and stages of GSDM were identified, range of promising directions of development GSDM and its relationship to support decision-making in the regional administration were determined. The study of exceeding the maximum permissible concentrations of uranium in groundwater in the territory of Ukraine on the basis of geological survey was conducted using GSDM clustering hotspots analysis methods and areas with limited use of groundwater were detected.Проведено обзорное исследование научного направления интеллектуального анализа геопространственных данных (ИАГД). Выявлены основные предпосылки формирования этого направления и его связь с геоинформатикой, системным анализом и интеллектуальным анализом данных. Проведено библиографическое исследование зарубежных и отечественных публикаций в области ИАГД. В ходе исследования было дано определение ИАГД, выявлены основные задачи, функции и этапы его проведения, определен круг перспективных направлений развития и его связь с поддержкой принятия решений в региональном управлении. С использованием ИАГД методов кластеризации горячих точек проведено исследование превышения предельно допустимых концентраций урана в подземных водах на территории Украины на основе данных геологической съемки и выявлено зоны ограничениями использования подземных вод

Spatially-constrained clustering of ecological networks

Spatial ecological networks are widely used to model interactions between georeferenced biological entities (e.g., populations or communities). The analysis of such data often leads to a two-step approach where groups containing similar biological entities are firstly identified and the spatial information is used afterwards to improve the ecological interpretation. We develop an integrative approach to retrieve groups of nodes that are geographically close and ecologically similar. Our model-based spatially-constrained method embeds the geographical information within a regularization framework by adding some constraints to the maximum likelihood estimation of parameters. A simulation study and the analysis of real data demonstrate that our approach is able to detect complex spatial patterns that are ecologically meaningful. The model-based framework allows us to consider external information (e.g., geographic proximities, covariates) in the analysis of ecological networks and appears to be an appealing alternative to consider such data

Proceso de descubrimiento de reglas de caracterización de grupos espacialmente referenciados

El descubrimiento de grupos sobre información espacialmente referenciada es un tema de interés en minería de datos espaciales. Los algoritmos existentes se orientan a descubrir tres tipos de grupos: regiones, grupos propiamente dichos, y zonas calientes. Sin embargo, no dan una caracterización de dichos grupos. El presente trabajo propone un proceso de descubrimiento de reglas de caracterización de grupos espacialmente referenciados que utiliza algoritmos TDIDT para obtener las características que fueron elegidas automáticamente para la generación de los mismos. Se presentan pruebas de concepto en las que se utilizan distintos algoritmos de agrupamiento espacial.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

Proceso de descubrimiento de reglas de caracterización de grupos espacialmente referenciados

El descubrimiento de grupos sobre información espacialmente referenciada es un tema de interés en minería de datos espaciales. Los algoritmos existentes se orientan a descubrir tres tipos de grupos: regiones, grupos propiamente dichos, y zonas calientes. Sin embargo, no dan una caracterización de dichos grupos. El presente trabajo propone un proceso de descubrimiento de reglas de caracterización de grupos espacialmente referenciados que utiliza algoritmos TDIDT para obtener las características que fueron elegidas automáticamente para la generación de los mismos. Se presentan pruebas de concepto en las que se utilizan distintos algoritmos de agrupamiento espacial.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

Proceso de descubrimiento de reglas de caracterización de grupos espacialmente referenciados

El descubrimiento de grupos sobre información espacialmente referenciada es un tema de interés en minería de datos espaciales. Los algoritmos existentes se orientan a descubrir tres tipos de grupos: regiones, grupos propiamente dichos, y zonas calientes. Sin embargo, no dan una caracterización de dichos grupos. El presente trabajo propone un proceso de descubrimiento de reglas de caracterización de grupos espacialmente referenciados que utiliza algoritmos TDIDT para obtener las características que fueron elegidas automáticamente para la generación de los mismos. Se presentan pruebas de concepto en las que se utilizan distintos algoritmos de agrupamiento espacial.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

Knowledge discovery process for description of spatially referenced clusters

Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.Facultad de Informátic