Autocorrelation-Based Regioclassification – a self-calibrating classification approach for choropleth maps explicitly considering spatial autocorrelation

Traun, Christoph; Loidl, Martin

doi:10.1080/13658816.2011.614246

Cited by 12 publications

(13 citation statements)

References 31 publications

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“…Almost all geographical data possesses the nature of autocorrelation while it is commonly ignored as the classification for choropleth maps is generally based on nonspatial attribute values (Traun and Loidl, 2012). According to Tobler's first law of geography (Tobler, 1970), attribute values that are close in space tend to have similar values and are more likely to fall into the same class interval (Mak and Coulson, 1991).…”

Section: The Spatial Autocorrelation Of Geographical Datamentioning

confidence: 99%

“…The results differ a lot from each other, especially in the low level where the difference in area percentage reach up to 12.2% (Fig 4, Table 2). (Jenks and Caspall, 1971;Slocum et al, 2005;Traun and Loidl, 2012;Smith et al, 2015). As to the land suitability assessment in planning, there is no uniform standard for the numbers of class.…”

Section: The Applicability Of the Natural Breaks (Jenks)mentioning

confidence: 99%

“…Consequently, the final result of the assessment might end up being too trivial in some areas (Fig 5), leaving erratic "islands" within a certain class just because the grid value falls slightly above or below the class interval. Traun and Loidl (2012) concluded the current spatially aware classification methods and their possibility of improvement. They also presented a new approach named "Regioclassification", which adapts to the degree of spatial autocorrelation in data through the combination of the Moran's I scatter plot with the FisherJenks algorithm.…”

Section: The Spatial Autocorrelation Of Geographical Datamentioning

confidence: 99%

“…Harvey (1970) considers classification as "perhaps the basic procedure by which we impose some sort of order and coherence upon the vast inflow of information from the real world". However, important information might risk being lost during the communication if the map is badly prepared (Jenks and Caspall, 1971;Traun and The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017 ISPRS Geospatial Week 2017, 18-22 September 2017, Wuhan, China…”

Section: The Uncertainty Of Choropleth Map Classificationmentioning

confidence: 99%

“…Multiple factors influence the certainty of the classification, such as data type, class numbers, intervals and the autocorrelation of the spatial data (Slocum et al, 2005;Traun and Loidl, 2012;Smith et al, 2015). Planners ought to avoid the pure application of the software and select the most suitable classification method according to the data feature and the actual demand of planning.…”

Section: The Uncertainty Of Choropleth Map Classificationmentioning

confidence: 99%

See 4 more Smart Citations

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

Liu

Zhan

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 KEY WORDS: Uncertainty, Land suitability assessment, GIS, Planning ABSTRACT:The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS) based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the "Nature Breaks" method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.

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Section: The Spatial Autocorrelation Of Geographical Datamentioning

confidence: 99%

Section: The Applicability Of the Natural Breaks (Jenks)mentioning

confidence: 99%

Section: The Spatial Autocorrelation Of Geographical Datamentioning

confidence: 99%

Section: The Uncertainty Of Choropleth Map Classificationmentioning

confidence: 99%

Section: The Uncertainty Of Choropleth Map Classificationmentioning

confidence: 99%

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The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

Liu

Zhan

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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An open source toolkit for identifying comparative space-time research questions

She

et al. 2014

Chin. Geogr. Sci.

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Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluation of public policies in the comparative context. At the same time, social scientists have been slow to adopt and implement new spatiotemporally explicit methods of data analysis due to the lack of extensible software packages, which becomes a major impediment to the promotion of spatiotemporal thinking. The proposed framework will address this need by developing a set of research questions based on space-time-distributional features of socioeconomic datasets. The authors aim to develop, evaluate, and implement this framework in an open source toolkit to comprehensively quantify the changes and level of hidden variation of space-time datasets across scales and dimensions. Free access to the source code allows a broader community to incorporate additional advances in perspectives and methods, thus facilitating interdisciplinary collaboration. Being written in Python, it is entirely cross-platform, lowering transmission costs in research and education.

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An integrated classification scheme for mapping estimates and errors of estimation from the American Community Survey

Wei

Tong

Phillips

2017

Computers, Environment and Urban Systems

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Autocorrelation-Based Regioclassification – a self-calibrating classification approach for choropleth maps explicitly considering spatial autocorrelation

Cited by 12 publications

References 31 publications

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

An open source toolkit for identifying comparative space-time research questions

An integrated classification scheme for mapping estimates and errors of estimation from the American Community Survey

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