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ISPRS Int. J. Geo-Inf., Volume 1, Issue 3 (December 2012) – 7 articles , Pages 228-350

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383 KiB  
Article
Spatial Relations Using High Level Concepts
by Padraig Corcoran, Peter Mooney and Michela Bertolotto
ISPRS Int. J. Geo-Inf. 2012, 1(3), 333-350; https://doi.org/10.3390/ijgi1030333 - 13 Dec 2012
Cited by 13 | Viewed by 8794
Abstract
Existing models of spatial relations do not consider that different concepts exist on different levels in a hierarchy and in turn that the spatial relations in a given scene are a function of the specific concepts considered. One approach to determining the existence [...] Read more.
Existing models of spatial relations do not consider that different concepts exist on different levels in a hierarchy and in turn that the spatial relations in a given scene are a function of the specific concepts considered. One approach to determining the existence of a particular spatial relation is to compute the corresponding high level concepts explicitly using map generalization before inferring the existence of the spatial relation in question. We explore this idea through the development of a model of the spatial relation “enters” that may exist between a road and a housing estate. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>A set of polygons corresponding to houses in a housing estate and a number of lines corresponding to roads are represented. The grey line represents the access road for the housing estate in question. Data taken from OpenStreetMap.</p> Full article ">Figure 2
<p>In (<b>a</b>) the object <span class="html-fig-inline" id="ijgi-01-00333-i005"> <img alt="Ijgi 01 00333 i005" src="/ijgi/ijgi-01-00333/article_deploy/html/images/ijgi-01-00333-i005.png"/></span> is <span class="html-italic">nearly completely</span> contained inside the object <span class="html-fig-inline" id="ijgi-01-00333-i006"> <img alt="Ijgi 01 00333 i006" src="/ijgi/ijgi-01-00333/article_deploy/html/images/ijgi-01-00333-i006.png"/></span>; In (<b>b</b>) the object <span class="html-fig-inline" id="ijgi-01-00333-i007"> <img alt="Ijgi 01 00333 i007" src="/ijgi/ijgi-01-00333/article_deploy/html/images/ijgi-01-00333-i007.png"/></span> is <span class="html-italic">between</span> the objects <span class="html-fig-inline" id="ijgi-01-00333-i006"> <img alt="Ijgi 01 00333 i006" src="/ijgi/ijgi-01-00333/article_deploy/html/images/ijgi-01-00333-i006.png"/></span> and <span class="html-fig-inline" id="ijgi-01-00333-i005"> <img alt="Ijgi 01 00333 i005" src="/ijgi/ijgi-01-00333/article_deploy/html/images/ijgi-01-00333-i005.png"/></span>.</p> Full article ">Figure 3
<p>The two polygons in (<b>a</b>) are merged using the connected in (<b>b</b>) to give the result in (<b>c</b>).</p> Full article ">Figure 4
<p>The three polygon in (<b>a</b>) are merged, through steps (<b>b</b>) to (<b>e</b>), to give the result in (<b>f</b>).</p> Full article ">Figure 5
<p>The merging of the polygons in (<b>a</b>) introduces a geometrical intersection with the line as illustrated in (<b>b</b>).</p> Full article ">Figure 6
<p>In each figure the set of 8 rays corresponding to a point <span class="html-fig-inline" id="ijgi-01-00333-i023"> <img alt="Ijgi 01 00333 i023" src="/ijgi/ijgi-01-00333/article_deploy/html/images/ijgi-01-00333-i023.png"/></span> are represented by arrows. <span class="html-fig-inline" id="ijgi-01-00333-i037"> <img alt="Ijgi 01 00333 i037" src="/ijgi/ijgi-01-00333/article_deploy/html/images/ijgi-01-00333-i037.png"/></span> represents the centroid of each polygon.</p> Full article ">Figure 7
<p>A set of polygons corresponding to houses in a housing estate and a number of lines corresponding to roads are represented. The grey lines represent the access roads for the housing estate in question. Data taken from OpenStreetMap.</p> Full article ">Figure 8
<p>The results of merging the polygons in (<b>a</b>) and (<b>b</b>) are displayed in (<b>c</b>) and (<b>d</b>) respectively.</p> Full article ">Figure 9
<p>OSM test scenes with the corresponding <span class="html-fig-inline" id="ijgi-01-00333-i019"> <img alt="Ijgi 01 00333 i019" src="/ijgi/ijgi-01-00333/article_deploy/html/images/ijgi-01-00333-i019.png"/></span> values.</p> Full article ">
866 KiB  
Article
Towards Automatic Vandalism Detection in OpenStreetMap
by Pascal Neis, Marcus Goetz and Alexander Zipf
ISPRS Int. J. Geo-Inf. 2012, 1(3), 315-332; https://doi.org/10.3390/ijgi1030315 - 22 Nov 2012
Cited by 80 | Viewed by 19452
Abstract
The OpenStreetMap (OSM) project, a well-known source of freely available worldwide geodata collected by volunteers, has experienced a consistent increase in popularity in recent years. One of the main caveats that is closely related to this popularity increase is different types of vandalism [...] Read more.
The OpenStreetMap (OSM) project, a well-known source of freely available worldwide geodata collected by volunteers, has experienced a consistent increase in popularity in recent years. One of the main caveats that is closely related to this popularity increase is different types of vandalism that occur in the projects database. Since the applicability and reliability of crowd-sourced geodata, as well as the success of the whole community, are heavily affected by such cases of vandalism, it is essential to counteract those occurrences. The question, however, is: How can the OSM project protect itself against data vandalism? To be able to give a sophisticated answer to this question, different cases of vandalism in the OSM project have been analyzed in detail. Furthermore, the current OSM database and its contributions have been investigated by applying a variety of tests based on other Web 2.0 vandalism detection tools. The results gathered from these prior steps were used to develop a rule-based system for the automated detection of vandalism in OSM. The developed prototype provides useful information about the vandalism types and their impact on the OSM project data. Full article
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Graphical abstract
Full article ">Figure 1
<p>The OpenStreetMap Infrastructure/Geostack (simplified).</p> Full article ">Figure 2
<p>Example of“Graffiti” vandalism in OSM in Zwijndrecht (The Netherlands) [<a href="#B34-ijgi-01-00315" class="html-bibr">34</a>].</p> Full article ">Figure 3
<p>OSM &amp; <span class="html-italic">OSMPatrol</span> Architecture.</p> Full article ">Figure 4
<p>UML sequence diagram of the vandalism detection tool (<span class="html-italic">OSMPatrol</span>).</p> Full article ">Figure 5
<p>UML activity diagram to detect the types of vandalism of an OSM edit sequence diagram of the vandalism detection tool (<span class="html-italic">OSMPatrol</span>).</p> Full article ">Figure 6
<p>Distribution of objects and edit-types in the detected vandalism (14–21 August 2012).</p> Full article ">Figure 7
<p>Distribution of vandalism users and vandalism edits based on the user reputation (14–21 August 2012).</p> Full article ">
1088 KiB  
Article
Geoprocessing Journey-to-Work Data: Delineating Commuting Regions in Dalarna, Sweden
by Martin Landré
ISPRS Int. J. Geo-Inf. 2012, 1(3), 294-314; https://doi.org/10.3390/ijgi1030294 - 14 Nov 2012
Cited by 9 | Viewed by 7446
Abstract
Delineation of commuting regions has always been based on statistical units, often municipalities or wards. However, using these units has certain disadvantages as their land areas differ considerably. Much information is lost in the larger spatial base units and distortions in self-containment values, [...] Read more.
Delineation of commuting regions has always been based on statistical units, often municipalities or wards. However, using these units has certain disadvantages as their land areas differ considerably. Much information is lost in the larger spatial base units and distortions in self-containment values, the main criterion in rule-based delineation procedures, occur. Alternatively, one can start from relatively small standard size units such as hexagons. In this way, much greater detail in spatial patterns is obtained. In this paper, regions are built by means of intrazonal maximization (Intramax) on the basis of hexagons. The use of geoprocessing tools, specifically developed for the processing of commuting data, speeds up processing time considerably. The results of the Intramax analysis are evaluated with travel-to-work area constraints, and comparisons are made with commuting fields, accessibility to employment, commuting flow density and network commuting flow size. From selected steps in the regionalization process, a hierarchy of nested commuting regions emerges, revealing the complexity of commuting patterns. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The location of Dalarna.</p> Full article ">Figure 2
<p>Number of jobs and major employment centers and external flows.</p> Full article ">Figure 3
<p>Residence-based and workplace-based self-containment.</p> Full article ">Figure 4
<p>Intramax region builder tool.</p> Full article ">Figure 5
<p>Intramax analysis results at steps 661 (11 regions) and 670 (six regions).</p> Full article ">Figure 6
<p>Residence-based self-containment at steps 661 (11 regions) and 670 (six regions).</p> Full article ">Figure 7
<p>Commuting field builder tool.</p> Full article ">Figure 8
<p>Commuting fields of Borlänge and Falun.</p> Full article ">Figure 9
<p>Commuting fields and 30 min travel time areas.</p> Full article ">Figure 10
<p>Network accessibility to employment (30 min travel time on road network).</p> Full article ">Figure 11
<p>Voronoi drive time isochrones for Leksand and Rättvik.</p> Full article ">Figure 12
<p>Commuter flows on road network and line density.</p> Full article ">Figure 13
<p>Intramax analysis result at step 671 (five regions) compared with local labor market areas.</p> Full article ">
792 KiB  
Article
A Spatial Multi-Criteria Model for the Evaluation of Land Redistribution Plans
by Demetris Demetriou, Linda See and John Stillwell
ISPRS Int. J. Geo-Inf. 2012, 1(3), 272-293; https://doi.org/10.3390/ijgi1030272 - 9 Nov 2012
Cited by 22 | Viewed by 8069
Abstract
A planning support system for land consolidation has been developed that has, at its heart, an expert system called LandSpaCES (Land Spatial Consolidation Expert System) which contains a “design module” that generates alternative land redistributions under different scenarios and an “evaluation module” which [...] Read more.
A planning support system for land consolidation has been developed that has, at its heart, an expert system called LandSpaCES (Land Spatial Consolidation Expert System) which contains a “design module” that generates alternative land redistributions under different scenarios and an “evaluation module” which integrates GIS with multi-criteria decision making for assessing these alternatives. This paper introduces the structural framework of the latter module which has been applied using a case study in Cyprus. Two new indices are introduced: the “parcel concentration coefficient” for measuring the dispersion of parcels; and the “landowner satisfaction rate” for predicting the acceptance of the land redistribution plan by the landowners in terms of the location of their new parcels. These two indices are used as criteria for the evaluation of the land redistribution alternatives and are transferable to any land consolidation project. Moreover, a modified version of the ratio estimation procedure, referred to as the “qualitative rating method” for assigning weights to the evaluation criteria, is presented, along with a set of non-linear value functions for standardizing the performance scores of the alternatives and incorporating expert knowledge for five evaluation criteria. The application of the module showed that it is a powerful new tool for the evaluation of alternative land redistribution plans that could be implemented in other countries after appropriate adjustments. A broader contribution has also been made to spatial planning processes, which might follow the methodology and innovations presented in this paper. Full article
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Figure 1

Figure 1
<p>General model of multi-attribute decision-making method (MADM) (adapted from Sharifi <span class="html-italic">et al.</span> [<a href="#B31-ijgi-01-00272" class="html-bibr">31</a>]).</p> Full article ">Figure 2
<p>The objective tree for the land redistribution problem.</p> Full article ">Figure 3
<p>The value functions for the evaluation criteria.</p> Full article ">Figure 4
<p>Ranking of alternatives for four different criteria weighting cases.</p> Full article ">Figure 5
<p>Performance of alternatives for all criteria in four cases.</p> Full article ">Figure 6
<p>Variability of the sensitivity coefficient for each criterion for four cases.</p> Full article ">Figure 7
<p>Ranking of alternatives for the two cases.</p> Full article ">Figure 8
<p>Variability of the sensitivity coefficient for each criterion for the two cases.</p> Full article ">
3667 KiB  
Article
A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic
by Günther Sagl, Martin Loidl and Euro Beinat
ISPRS Int. J. Geo-Inf. 2012, 1(3), 256-271; https://doi.org/10.3390/ijgi1030256 - 2 Nov 2012
Cited by 44 | Viewed by 10800
Abstract
In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. More than 88 million movements between pairs of radio cells—so-called handovers—served as a proxy for more than two [...] Read more.
In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. More than 88 million movements between pairs of radio cells—so-called handovers—served as a proxy for more than two months of mobility within four urban test areas in Northern Italy. In contrast to previous work, our approach relies entirely on visualization and mapping techniques, implemented in several software applications. We purposefully avoid statistical or probabilistic modeling and, nonetheless, reveal characteristic and exceptional mobility patterns. The results show, for example, surprising similarities and symmetries amongst the total mobility and people flows between the test areas. Moreover, the exceptional patterns detected can be associated to real-world events such as soccer matches. We conclude that the visual analytics approach presented can shed new light on large-scale collective urban mobility behavior and thus helps to better understand the “pulse” of dynamic urban systems. Full article
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Figure 1

Figure 1
<p>Case study areas: four urban environments in the Friuli Venetia Giulia region including their close periphery.</p> Full article ">Figure 2
<p>The main principle of a handover (blue arrow) between two service cells along a path (grey line); the orange locations represent the centroid of the origin and the destination cell, respectively (grey areas).</p> Full article ">Figure 3
<p>Overview of the four-step analysis approach.</p> Full article ">Figure 4
<p>Diurnal variations of the total mobility and absolute net migration flow of the four urban environments per day of the week (colored per cell-link); blue arrows indicate mobility gateways and red arrows indicate mobility hotspots (compare <a href="#ijgi-01-00256-f005" class="html-fig">Figure 5</a>).</p> Full article ">Figure 5
<p>Twenty-four hours of total mobility <span class="html-italic">versus</span> 24 h absolute net migration flow of the four urban environments per day of the week (colored per cell-link); mobility gateways are indicated by blue arrows and mobility hotspots are indicated by red arrows (compare <a href="#ijgi-01-00256-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 6
<p>Spatial mobility patterns of the four urban environments: geographic locations of mobility gateways and mobility hotspots identified in <a href="#ijgi-01-00256-f004" class="html-fig">Figure 4</a> and <a href="#ijgi-01-00256-f005" class="html-fig">Figure 5</a>, and spatial density estimation of the total mobility (line density of handovers per cell-link).</p> Full article ">Figure 7
<p>Similarity and symmetry of total mobility and absolute net migration flow among the four urban test areas (values have been normalized between 0 and 1).</p> Full article ">Figure 8
<p>Bruce Springsteen Concert in Stadio Friuli, Udine, on Thursday, 23 July 2009; the outstanding orange pattern refers to the Stadium’s location as shown in the aerial photo to the right (the white lines indicate other cell-links).</p> Full article ">Figure 9
<p>Soccer game Udinese <span class="html-italic">versus</span> AC Milan (Stadio Friuli, Udine) on Sunday, 23 August 2009.</p> Full article ">Figure 10
<p>Celebration of the 60th anniversary of mountain infantry in Udine on Sunday, 13 September 2009 (the color of the arrows correspond with the color in the map).</p> Full article ">Figure 11
<p>Unknown event in the Udine city center on Saturday night, 19 September 2009 (the color of the arrows correspond with the color in the map).</p> Full article ">
516 KiB  
Article
Detecting Changes in Forest Structure over Time with Bi-Temporal Terrestrial Laser Scanning Data
by Xinlian Liang, Juha Hyyppä, Harri Kaartinen, Markus Holopainen and Timo Melkas
ISPRS Int. J. Geo-Inf. 2012, 1(3), 242-255; https://doi.org/10.3390/ijgi1030242 - 26 Oct 2012
Cited by 52 | Viewed by 10468
Abstract
Changes to stems caused by natural forces and timber harvesting constitute an essential input for many forestry-related applications and ecological studies, especially forestry inventories based on the use of permanent sample plots. Conventional field measurement is widely acknowledged as being time-consuming and labor-intensive. [...] Read more.
Changes to stems caused by natural forces and timber harvesting constitute an essential input for many forestry-related applications and ecological studies, especially forestry inventories based on the use of permanent sample plots. Conventional field measurement is widely acknowledged as being time-consuming and labor-intensive. More automated and efficient alternatives or supportive methods are needed. Terrestrial laser scanning (TLS) has been demonstrated to be a promising method in forestry field inventories. Nevertheless, the applicability of TLS in recording changes in the structure of forest plots has not been studied in detail. This paper presents a fully automated method for detecting changes in forest structure over time using bi-temporal TLS data. The developed method was tested on five densely populated forest plots including 137 trees and 50 harvested trees in point clouds. The present study demonstrated that 90 percent of tree stem changes could be automatically located from single-scan TLS data. These changes accounted for 92 percent of the changed basal area. The results indicate that the processing of TLS data collected at different times to detect tree stem changes can be fully automated. Full article
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Figure 1

Figure 1
<p>The Leica HDS6000 terrestrial laser scanner.</p> Full article ">Figure 2
<p>The terrestrial laser scanning (TLS) data for the plot (<b>a</b>) before and (<b>b</b>) after the harvest operation.</p> Full article ">Figure 3
<p>The steps in stem change detection: (<b>a</b>) the point cloud for a tree before the change; (<b>b</b>) the point cloud in the space corresponding to (a) after the change; (<b>c</b>) the differences found in the data-orientated analysis; (<b>d</b>) the detected stem points; (<b>e</b>) the reconstructed stem model.</p> Full article ">Figure 4
<p>A point p on a cylinder surface, its local coordinate system X<sub>1</sub>Y<sub>1</sub>Z<sub>1</sub>, and the real world coordinate system X<sub>g</sub>Y<sub>g</sub>Z<sub>g</sub>.</p> Full article ">Figure 5
<p>The detection in forest structure over time: (<b>a</b>) the TLS data at Time I; (<b>b</b>) the TLS data at Time II; (<b>c</b>) the laser points from the changed stems; (<b>d</b>) the stem models.</p> Full article ">
2174 KiB  
Article
Satellite Image Pansharpening Using a Hybrid Approach for Object-Based Image Analysis
by Brian Alan Johnson, Ryutaro Tateishi and Nguyen Thanh Hoan
ISPRS Int. J. Geo-Inf. 2012, 1(3), 228-241; https://doi.org/10.3390/ijgi1030228 - 16 Oct 2012
Cited by 33 | Viewed by 10270
Abstract
Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Smoothing-Filter-Based-Intensity Modulation (SFIM) algorithms were used to pansharpen GeoEye-1 imagery. The pansharpened images were then segmented in Berkeley Image Seg using a wide range of segmentation parameters, and the spatial and spectral accuracy of image segments was [...] Read more.
Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Smoothing-Filter-Based-Intensity Modulation (SFIM) algorithms were used to pansharpen GeoEye-1 imagery. The pansharpened images were then segmented in Berkeley Image Seg using a wide range of segmentation parameters, and the spatial and spectral accuracy of image segments was measured. We found that pansharpening algorithms that preserve more of the spatial information of the higher resolution panchromatic image band (i.e., IHS and BT) led to more spatially-accurate segmentations, while pansharpening algorithms that minimize the distortion of spectral information of the lower resolution multispectral image bands (i.e., SFIM) led to more spectrally-accurate image segments. Based on these findings, we developed a new IHS-SFIM combination approach, specifically for object-based image analysis (OBIA), which combined the better spatial information of IHS and the more accurate spectral information of SFIM to produce image segments with very high spatial and spectral accuracy. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>GeoEye-1 image (NIR, R, G bands) of (<b>a</b>) the residential study area. (<b>b</b>) Intensity-Hue-Saturation (IHS), (<b>c</b>) Brovey Transform (BT), and (<b>d</b>) Smoothing-Filter-Based-Intensity Modulation (SFIM) pansharpened images. Yellow polygons in (a) delineate reference tree polygons and black polygons delineate reference building polygons. A standard deviation contrast stretch of 2.0 was used for display purposes.</p> Full article ">Figure 2
<p>GeoEye-1 image (NIR, R, G bands) of (<b>a</b>).the forested study area, (<b>b</b>) IHS, (<b>c</b>) BT, and (<b>d</b>) SFIM pansharpened images. Yellow polygons in (a) delineate reference polygons of damaged or killed trees. A standard deviation contrast stretch of 3.0 was used for display purposes.</p> Full article ">Figure 3
<p>True color IHS pansharpened image of: (<b>a</b>) a subset of the forested study area, (<b>b</b>) the most spatially-accurate segmentation of the IHS image, and (c) image segments from (b) overlaid on the SFIM pansharpened image to extract more accurate mean spectral values for the segments. Brown areas in the images show trees severely damaged or killed by <span class="html-italic">Raffaelea quercivora.</span> False color imagery (NIR, R, G) used in place of the true color imagery in (<b>d</b>–<b>f</b>) for visualization purposes. White-colored areas show stressed, damaged, or dead trees, which are clearer in (f).</p> Full article ">
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