Error‐sensitive historical GIS: Identifying areal interpolation errors in time‐series data

Gregory, Ian; Ell, Paul S.

doi:10.1080/13658810500399589

Cited by 37 publications

(33 citation statements)

References 22 publications

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“…Areal interpolation has been one of key techniques in historical GIS to establish a consistent spatial framework through which to analyze demographic change over time (Gregory 2000;Gregory and Ell 2006). It has been far less commonly used by historians to construct microhistories of how populations are positioned within the built environment.…”

Section: Methods and Challenges In The Creation Of "Meaningful Boundamentioning

confidence: 99%

“…So dramatic were Areal interpolation enables us to reconcile these transformations of administrative units in order to facilitate time-series analysis of population change. To standardize divergent boundary data sets over time we calculated the difference between two areal units and then reallocated the data relative to the areal proportions of each unit (Gregory 2000;Gregory and Ell 2006). This technique within GIS is a simple and effective means of redistricting data onto consistent spatial units by calculating the degree of overlap between "source" and "target" geographies.…”

Section: Methods and Challenges In The Creation Of "Meaningful Boundamentioning

confidence: 99%

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The Inner Geographies of a Migrant Gateway: Mapping the Built Environment and the Dynamics of Caribbean Mobility in Manchester, 1951–2011

Brown¹,

Cunningham²

2016

Social Science History

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Section: Methods and Challenges In The Creation Of "Meaningful Boundamentioning

confidence: 99%

Section: Methods and Challenges In The Creation Of "Meaningful Boundamentioning

confidence: 99%

The Inner Geographies of a Migrant Gateway: Mapping the Built Environment and the Dynamics of Caribbean Mobility in Manchester, 1951–2011

Brown¹,

Cunningham²

2016

Social Science History

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“…Areal interpolation is then applied to create a standardized set of administrative boundaries. The results offer great potential, but the approach requires dealing with a great number of errors (Gregory and Ell, 2006). Furthermore, the information required to reconstruct boundary changes is not available for any other territories than England and Wales.…”

Section: Data Sources and Homogenizationmentioning

confidence: 99%

The Use of Digital Tools for Spatial Analysis in Population Geography

Martí-Henneberg

Franch-Auladell

Solanas-Jiménez

2016

Front. Digit. Humanit.

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Digital tools, and in particular GIS, have enormously increased the possibilities for analysis in historical geography. In this article, we shall explain how these tools can be used to study the evolution of population density over a significant period. The territorial units used will be municipalities, as they allow detailed territorial analysis. However, research projects that take municipalities as their points of reference tend to be complex because their territorial boundaries have often undergone numerous changes over the course of modern history. The same has occurred, to a greater or lesser degree, in all of the countries in Europe (Bennett, 1989). The countries that have had the most stable municipal boundaries over the past 150 years include France, Italy, and Spain, though the modifications to their boundaries have also been notable. However, like all relevant challenges, these changes also offer us new opportunities, if we are able to cope with them. In this particular case, the challenge will be to achieve the territorial homogenization of the historical municipal series. In other words, when the municipal limits have changed, it will be necessary to adapt the data from the old municipal territories to the new ones. This exercise will have a number of applications. In this article, we present just one of these: the possibility of detecting areas and periods in which, over the course of history, there has been population growth, decline, or stagnation. This will serve as a relevant indicator, or proxy, for organizing research in other fields. For example, in the case of economic history, it is clear that variations in the density of population provide clues for interpreting the territorial distribution of economic activity. We also understand that it will be possible to apply our research about Spain to other countries and that this will make it possible to evaluate the interest and results that we can expect from the homogenized work. We think that, despite its interest, this type of study has, until now, been very rare on account of the methodological difficulties involved. However, these new digital tools in the field of historical GIS, as spatial aggregation and Moran I techniques, have helped to provide solutions to assume this challenge.

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“…Beard and Chrisman proposed the Zipper algorithm, which finds the closest point pairs within a distance threshold and determines that these pairs are the corresponding point pairs [1]. This algorithm is simple and efficient; thus, it has been adopted in many studies [2][3][4][5]. However, one problem with this algorithm lies in estimating an appropriate threshold because even though the threshold is related to the positional accuracies of the spatial datasets involved, it is not clear how the accuracies are translated into a threshold value [6].…”

Section: Open Accessmentioning

confidence: 99%

“…Edge matching between adjacent spatial datasets finds and removes the geometric differences of common boundary edges between adjacent spatial datasets [1][2][3][4]. There are two components in this task: feature matching to identify the corresponding point or edge pairs and map alignment to remove the differences in the identified pairs.…”

Section: Introductionmentioning

confidence: 99%

Local Edge Matching for Seamless Adjacent Spatial Datasets with Sequence Alignment

Huh¹

2015

IJGI

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This study proposes a local edge matching method with a sequence alignment technique for adjacent spatial datasets. By assuming that the common boundary edges of the datasets are point strings, the proposed method obtains the sequence for point edit operations to align the edges by using the string matching algorithm with the following operations: (1) snapping two points from each string to their average position, (2) removing a point from one string and (3) removing a point from the other string. The costs for these operations are derived from the deformation of the involved line segments in terms of the angle and length changes. The corresponding point pairs are then considered point pairs for which the snapping operation is chosen in a sequence. Based on these pairs, a border area of adjacent spatial datasets can be partitioned into sub-border areas where distinctive matching and alignment processes can be performed.

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Error‐sensitive historical GIS: Identifying areal interpolation errors in time‐series data

Cited by 37 publications

References 22 publications

The Inner Geographies of a Migrant Gateway: Mapping the Built Environment and the Dynamics of Caribbean Mobility in Manchester, 1951–2011

The Inner Geographies of a Migrant Gateway: Mapping the Built Environment and the Dynamics of Caribbean Mobility in Manchester, 1951–2011

The Use of Digital Tools for Spatial Analysis in Population Geography

Local Edge Matching for Seamless Adjacent Spatial Datasets with Sequence Alignment

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