scholarly journals Methodological Issues in Cross-Border Analyses of European Small-Area Data: A Case Study

10.1068/a3278 ◽  
2000 ◽  
Vol 32 (2) ◽  
pp. 361-376 ◽  
Author(s):  
Sally Cook ◽  
Michael A Poole ◽  
Adrian J Moore ◽  
Dennis G Pringle
Keyword(s):  
Spatial Data ◽  
Small Area ◽  
Census Data ◽  
Spatial Scales ◽  
Data Sets ◽  
Socioeconomic Indicators ◽  
The European Union ◽  
National Data ◽  
Cross Border ◽  
Spatial Data Sets

The growing trends for political and economic integration within the European Union signal a need for improved cohesion of national data sets to allow for comparative cross-national research on socioeconomic topics. Difficulties in achieving comparability of variables and spatial bases can apply whether analysis is undertaken at the most macro level (that of entire nations) or at more micro spatial scales. In this paper we will discuss the methodological problems which can arise from comparative cross-border analysis by using small-area census data within Ireland (North and South) as illustration. As this analysis demonstrates, limited overlap between the specific variables in national data sets, different national contexts for socioeconomic indicators, and variations between data sets in the size of spatial units can all cause analytical problems at this spatial scale. In particular, scale mixing can potentially result in very misleading interpretations. Possible solutions to these problems are discussed, and policy recommendations are made in relation to microscale spatial data sets at the national, as well as at the pan-European, level.

scholarly journals Time-dependent taphonomic site loss leads to spatial averaging: implications for archaeological cultures

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Emily Coco ◽  
Radu Iovita
Keyword(s):  
Material Culture ◽  
Spatial Data ◽  
Spatial Scales ◽  
Negative Relationship ◽  
Time Dependent ◽  
Data Sets ◽  
Naturally Occurring ◽  
Spatially Distributed ◽  
Spatial Data Sets ◽  
Archaeological Cultures

Abstract Archaeologists typically define cultural areas on the basis of similarities between the types of material culture present in sites. The similarity is assessed in order of discovery, with newer sites being evaluated against older ones. Despite evidence for time-dependent site loss due to taphonomy, little attention has been paid to how this impacts archaeological interpretations about the spatial extents of material culture similarity. This paper tests the hypothesis that spatially incomplete data sets result in detection of larger regions of similarity. To avoid assumptions of cultural processes, we apply subsampling algorithms to a naturally occurring, spatially distributed dataset of soil types. We show that there is a negative relationship between the percentage of points used to evaluate similarity across space and the absolute distances to the first minimum in similarity for soil classifications at multiple spatial scales. This negative relationship indicates that incomplete spatial data sets lead to an overestimation of the area over which things are similar. Moreover, the location of the point from which the calculation begins can determine the size of the region of similarity. This has important implications for how we interpret the spatial extent of similarity in material culture over large distances in prehistory.

MULTIVARIATE SPATIAL OUTLIER DETECTION

2004 ◽  
Vol 13 (04) ◽  
pp. 801-811 ◽  
Author(s):  
CHANG-TIEN LU ◽  
DECHANG CHEN ◽  
YUFENG KOU
Keyword(s):  
Outlier Detection ◽  
Spatial Data ◽  
Census Data ◽  
Data Sets ◽  
Computation Complexity ◽  
Spatial Outlier ◽  
Detection Algorithms ◽  
Single Attribute ◽  
Spatial Data Sets ◽  
Spatial Outliers

A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective detection algorithms, and analyze their computation complexity. In addition, using a real-world census data, we demonstrate that our approaches can effectively identify local abnormality in large spatial data sets.

scholarly journals Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1029
Author(s):  
Malte Schwanebeck ◽  
Marcus Krüger ◽  
Rainer Duttmann
Keyword(s):  
Spatial Data ◽  
Census Data ◽  
Residential Buildings ◽  
Local Heating ◽  
Federal State ◽  
Data Sets ◽  
Grid Cells ◽  
Data Set ◽  
Heat Demand ◽  
Spatial Data Sets

Heat demand of buildings and related CO2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age.

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

scholarly journals Mining Co-Location Patterns with Rare Events from Spatial Data Sets

2006 ◽  
Vol 10 (3) ◽  
pp. 239-260 ◽  
Author(s):  
Yan Huang ◽  
Jian Pei ◽  
Hui Xiong
Keyword(s):  
Spatial Data ◽  
Rare Events ◽  
Data Sets ◽  
Location Patterns ◽  
Spatial Data Sets

scholarly journals Refinement Algoritm of Hypotheses Testing Research Based on Geochronological Tracking

2020 ◽  
Vol 6 (1) ◽  
pp. 86-93
Author(s):  
R. Ivakin ◽  
Y. Ivakin ◽  
S. Potapichev
Keyword(s):  
Spatial Data ◽  
Optimization Problem ◽  
Retrospective Studies ◽  
Subject Area ◽  
Statistical Testing ◽  
Time Interval ◽  
Data Sets ◽  
Hypotheses Testing ◽  
Practical Application ◽  
Spatial Data Sets

Geochronological tracking is an effective information technology for digital cartographic spatial data sets processing. It is widely known in retrospective patterns research about geographic relocation of figures, or any other units for a given time interval. Software component of geochronological tracking is becoming one the most popular GIS-integrated applications. The article presents the basic provisions for the algorithmization of the geochronological tracking procedure for statistical testing of retrospective studies hypotheses. We can observe the results of solving this optimization problem in a general form and in a number of the most typical variants. The obtained results of solving the optimization problem are interpreted in terms of the retrospective studies subject area. There are shown the ways of further practical application of the optimized algorithm in the tasks of modern logistics, data mining and formalized knowledge.

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