Integrating attribute and space characteristics in choropleth display and spatial data mining

Murray, Alan T.; Shyy, Tung‐Kai

doi:10.1080/136588100424954

Cited by 88 publications

(41 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This corroborated the previous study by Murray and Shyy (2000) on the integration of attribute and spatial data for identifying patterns in spatial information. The previous investigation by Andrienko and Andrienko (2001) also reported on the technique of analysis of numerical data associated with area geographical objects.…”

Section: Discussionsupporting

confidence: 81%

A precision nutrient variability study of an experimental plot in Mukono Agricultural Research and Development Institute, Mukono, Uganda

Adekayode¹,

Lutaaya²,

Ogunkoya³

et al. 2014

Afr. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Section: Discussionsupporting

confidence: 81%

A precision nutrient variability study of an experimental plot in Mukono Agricultural Research and Development Institute, Mukono, Uganda

Adekayode¹,

Lutaaya²,

Ogunkoya³

et al. 2014

Afr. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…However, neither of these approaches was ideal for our purposes. The natural breaks approach uses Jenk's method of optimisation which minimises the sum of the variance within each of the categories (Murray and Shyy 2000), thus making each group within a distribution as homogenous as possible. Unfortunately, while natural breaks take into account the distribution of the entire dataset, they also suffer from some problems as discussed in Conolly and Lake (2006).…”

Section: Sei=09493mentioning

confidence: 99%

Poverty at the Local Level: National and Small Area Poverty Estimates by Family Type for Australia in 2006

Miranti

McNamara

Tanton

et al. 2010

Appl. Spatial Analysis

View full text Add to dashboard Cite

There is a substantial literature within Australia examining poverty rates for different family types at the national level. This study presents the first Australian estimates of poverty rates for different types of families at a local level. This paper builds upon the SpatialMSM/08B model, which fuses together data from the 2006 Australian Bureau of Statistics Census of Population and Housing and the 2002-03 and 2003-04 Surveys of Income and Housing. We examine differences in rates of income poverty for lone persons, sole parents, couples and couples with children. The results show that people living by themselves and sole parents have the highest poverty rates. In addition, there are pronounced spatial differences in the poverty rates of people living in different family situations, although the highest poverty rates for all family types tend to be in Australia's rural areas, with poverty clusters in most of the capital cities.

show abstract

“…We utilize raw crime and geospatial feature datasets recorded in the year of 1996 and 1998 by the Queensland Police Service around 217 central urban suburbs of Brisbane as our study region. Brisbane is continuously experiencing steady population and crime growth [21,22]. Building crime related concepts in this region provides a valuable resource to city planners, policing agencies and criminologists.…”

Section: A Case Study With Geospatial Conceptsmentioning

confidence: 99%

Higher Order Voronoi Diagrams for Concept Boundaries and Tessellations

Lee

2007

6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007)

View full text Add to dashboard Cite

Representing concepts and their boundaries is of great importance in concept learning and formation. We introduce a flexible framework for representing concept boundaries and tessellations through the higher order Voronoi diagrams. The framework provides different levels of concept granularity modelling vagueness and indeterminacy of concept boundaries. The proposed approach robustly supports concept learning and formation.

show abstract

Integrating attribute and space characteristics in choropleth display and spatial data mining

Cited by 88 publications

References 22 publications

A precision nutrient variability study of an experimental plot in Mukono Agricultural Research and Development Institute, Mukono, Uganda

A precision nutrient variability study of an experimental plot in Mukono Agricultural Research and Development Institute, Mukono, Uganda

Poverty at the Local Level: National and Small Area Poverty Estimates by Family Type for Australia in 2006

Higher Order Voronoi Diagrams for Concept Boundaries and Tessellations

Contact Info

Product

Resources

About