A generic test for the similarity of spatial data

Two spatial data sets are considered to be similar if they originate from the same stochastic process in terms of their spatial structure. Many tests have been developed over recent years to test the similarity of certain types of spatial data, such as spatial point patterns, geostatistical data and images. This research proposes a generic spatial similarity test able to handle various types of spatial data, for example images (modelled spatially), point patterns, marked point patterns, geostatistical data and lattice patterns. A simulation study is done in order to test the method for each spatial data set. After the simulation study, it was concluded that the proposed spatial similarity test is not sensitive to the user-defined resolution of the pixel image representation. From the simulation study, the proposed spatial similarity test performs well on lattice data, some of the unmarked point patterns and the marked point patterns with discrete marks. We illustrate this test on property prices in the City of Cape Town and the City of Johannesburg, South Africa.

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CHANGES AFFECTING GENERALIZATION OF LAND COVER FEATURES IN A SMALLER SCALE

Geodesy and Cartography ◽

10.3846/20296991.2012.728045 ◽

2012 ◽

Vol 38 (3) ◽

pp. 98-105 ◽

Cited By ~ 2

Author(s):

Lina Papšienė ◽

Kęstutis Papšys

Keyword(s):

Land Cover ◽

Spatial Data ◽

Data Sets ◽

Data Set ◽

The Creation ◽

Spatial Data Sets

Reference spatial data sets represent the least changing natural and anthropogenic features of terrine. As a rule, such data are stored in different scales and most frequently updated consequently starting with a spatial data set of a larger scale (usually base scale) thus later performing an update of data in smaller scales. The generalization of features in a larger scale is one of the major processes employed in the creation and update of spatial data of a smaller scale. In order to effectively carry out works, it is recommended to use automatic procedures and generalization only in those cases when changes in features are significant, i.e. affect the update of features in a smaller scale. The article discusses the relation between changes in polygon features (identify land cover territories in a base spatial data set) and different generalization processes as well as the evaluation of significance of likely changes.

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On the MDBSCAN Algorithm in a Spatial Data Mining Context

Geographic Information Analysis for Sustainable Development and Economic Planning - Advances in Geospatial Technologies ◽

10.4018/978-1-4666-1924-1.ch018 ◽

2013 ◽

pp. 263-273

Author(s):

Gabriella Schoier

Keyword(s):

Spatial Data ◽

Spatial Clustering ◽

Clustering Algorithms ◽

Synthetic Data ◽

Spatial Data Mining ◽

Data Sets ◽

Data Set ◽

Spatial Variables ◽

Spatial Objects ◽

Spatial Data Sets

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this chapter is to present a density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images

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On the MDBSCAN Algorithm in a Spatial Data Mining Context

Data Mining ◽

10.4018/978-1-4666-2455-9.ch021 ◽

2013 ◽

pp. 435-444

Author(s):

Gabriella Schoier

Keyword(s):

Spatial Data ◽

Spatial Clustering ◽

Clustering Algorithms ◽

Synthetic Data ◽

Spatial Data Mining ◽

Data Sets ◽

Data Set ◽

Spatial Variables ◽

Spatial Objects ◽

Spatial Data Sets

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

Energies ◽

10.3390/en14041029 ◽

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.

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Spatial data-sets for geographical referencing

10.3403/00334343 ◽

1994 ◽

Keyword(s):

Spatial Data ◽

Data Sets ◽

Spatial Data Sets

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Spatial data-sets for geographical referencing

10.3403/02124652 ◽

2000 ◽

Keyword(s):

Spatial Data ◽

Data Sets ◽

Spatial Data Sets

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Spatial data-sets for geographical referencing

10.3403/02503338 ◽

2002 ◽

Keyword(s):

Spatial Data ◽

Data Sets ◽

Spatial Data Sets

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

Open Geosciences ◽

10.1515/geo-2020-0159 ◽

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.

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