ISPRS International Journal of Geo-Information

2246 KiB

Open AccessArticle

Predicting Relevant Change in High Resolution Satellite Imagery

by Matthew Klaric

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1491-1511; https://doi.org/10.3390/ijgi3041491 - 22 Dec 2014

Cited by 1 | Viewed by 4412

With the ever increasing volume of remote sensing imagery collected by satellite constellations and aerial platforms, the use of automated techniques for change detection has grown in importance, such that changes in features can be quickly identified. However, the amount of data collected [...] Read more.

With the ever increasing volume of remote sensing imagery collected by satellite constellations and aerial platforms, the use of automated techniques for change detection has grown in importance, such that changes in features can be quickly identified. However, the amount of data collected surpasses the capacity of imagery analysts. In order to improve the effectiveness and efficiency of imagery analysts performing data maintenance activities, we propose a method to predict relevant changes in high resolution satellite imagery based on human annotations on selected regions of an image. We study a variety of classifiers in order to determine which is most accurate. Additionally, we experiment with a variety of ways in which a diverse set of training data can be constructed to improve the quality of predictions. The proposed method aids in the analysis of change detection results by using various classifiers to develop a relevant change model that can be used to predict the likelihood of other analyzed areas containing a relevant change or not. These predictions of relevant change are useful to analysts, because they speed the interrogation of automated change detection results by leveraging their observations of areas already analyzed. A comparison of four classifiers shows that the random forest technique slightly outperforms other approaches. Full article

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4634 KiB

Open AccessArticle

A Software Reference Architecture for Service-Oriented 3D Geovisualization Systems

by Dieter Hildebrandt

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1445-1490; https://doi.org/10.3390/ijgi3041445 - 19 Dec 2014

Cited by 7 | Viewed by 6652

Abstract

Modern 3D geovisualization systems (3DGeoVSs) are complex and evolving systems that are required to be adaptable and leverage distributed resources, including massive geodata. This article focuses on 3DGeoVSs built based on the principles of service-oriented architectures, standards and image-based representations (SSI) to address [...] Read more.

Modern 3D geovisualization systems (3DGeoVSs) are complex and evolving systems that are required to be adaptable and leverage distributed resources, including massive geodata. This article focuses on 3DGeoVSs built based on the principles of service-oriented architectures, standards and image-based representations (SSI) to address practically relevant challenges and potentials. Such systems facilitate resource sharing and agile and efficient system construction and change in an interoperable manner, while exploiting images as efficient, decoupled and interoperable representations. The software architecture of a 3DGeoVS and its underlying visualization model have strong effects on the system’s quality attributes and support various system life cycle activities. This article contributes a software reference architecture (SRA) for 3DGeoVSs based on SSI that can be used to design, describe and analyze concrete software architectures with the intended primary benefit of an increase in effectiveness and efficiency in such activities. The SRA integrates existing, proven technology and novel contributions in a unique manner. As the foundation for the SRA, we propose the generalized visualization pipeline model that generalizes and overcomes expressiveness limitations of the prevalent visualization pipeline model. To facilitate exploiting image-based representations (IReps), the SRA integrates approaches for the representation, provisioning and styling of and interaction with IReps. Five applications of the SRA provide proofs of concept for the general applicability and utility of the SRA. A qualitative evaluation indicates the overall suitability of the SRA, its applications and the general approach of building 3DGeoVSs based on SSI. Full article

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17890 KiB

Open AccessArticle

Urban Morphological Change Analysis of Dhaka City, Bangladesh, Using Space Syntax

by Bayes Ahmed, Rakibul Hasan and K. M. Maniruzzaman

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1412-1444; https://doi.org/10.3390/ijgi3041412 - 18 Dec 2014

Cited by 34 | Viewed by 26185

Abstract

This article is based on a study of the morphological changes of Dhaka City, the capital of Bangladesh. The main objective of the research is to study the transformation of urban morphology in Dhaka City from 1947 to 2007. Three sample wards (18, [...] Read more.

This article is based on a study of the morphological changes of Dhaka City, the capital of Bangladesh. The main objective of the research is to study the transformation of urban morphology in Dhaka City from 1947 to 2007. Three sample wards (18, 19 and 72) of Dhaka City Corporation are strategically selected as the study areas. Ward 72 has an indigenous type of organic settlement, whereas ward 19 is a planned area, and ward 18 represents a mixed (both planned and informal) type of settlement. In this research, the transformation of urban settlement pattern is examined through space syntax. The results show that the organic settlements (ward 72) are highly integrated both in terms of the local and global syntactic measures (lowest standard deviation for local and global integration, with the highest intelligibility values), and are more connectivity. The scenario is opposite in the case of planned settlements. The characteristics of mixed areas (ward 18) lie in between the organic and planned settlements. Therefore, in summary, it can be stated that the integration, connectivity and intelligibility measures of Dhaka City are found to be high, medium and low for the indigenous, mixed and planned settlement types; respectively. Full article

(This article belongs to the Special Issue Geographic Information Science and Remote Sensing Techniques for Sustainable Urban and Regional Planning)

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Graphical abstract

383 KiB

Open AccessArticle

Targeting: Logistic Regression, Special Cases and Extensions

by Helmut Schaeben

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1387-1411; https://doi.org/10.3390/ijgi3041387 - 11 Dec 2014

Cited by 10 | Viewed by 5161

Abstract

Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters [...] Read more.

Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology, i.e., its control, is sufficiently versatile to mimic interaction terms, artificial neural nets are able to account for violations of conditional independence and yield very similar results. Weights-of-evidence cannot reasonably include interaction terms; subsequent modifications of the weights, as often suggested, cannot emulate the effect of interaction terms. Full article

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Spatial distribution of two indicator predictor variables B1, B2 and the indicator target variable T of the dataset RANKIT and two uni-directional semi-variograms (left); and the spatial distribution of two indicator predictor variables B1, B2 and the indicator target variable T of the dataset RANKITMIX and two uni-directional semi-variograms (right), revealing different spatial distributions and different geostatistical characteristics than rankit. The red lines indicate the values of the classical sample variances. Full article ">
Spatial distribution of predicted conditional probabilities <math display="inline"> <mrow> <mover accent="true"> <mi>P</mi> <mo>^</mo></mover> <mrow> <mo>(</mo> <mrow> <mi mathvariant="normal">T</mi> <mo>=</mo> <mn>1</mn> <mo stretchy="false">|</mo> <msub> <mi mathvariant="normal">B</mi> <mn>1</mn></msub> <msub> <mi mathvariant="normal">B</mi> <mn>2</mn></msub></mrow> <mo>)</mo></mrow></mrow></math> for the training dataset rankit according to: elementary estimation (top left); logistic regression with interaction term (top center); artificial neural net annga of R (top right); weights-of-evidence (bottom left); logistic regression without interaction (bottom right). Full article ">
Spatial distribution of predicted conditional probabilities <math display="inline"> <mrow> <mover accent="true"> <mi>P</mi> <mo>^</mo></mover> <mrow> <mo>(</mo> <mrow> <mi mathvariant="normal">T</mi> <mo>=</mo> <mn>1</mn> <mo stretchy="false">|</mo> <msub> <mi mathvariant="normal">B</mi> <mn>1</mn></msub> <msub> <mi mathvariant="normal">B</mi> <mn>2</mn></msub></mrow> <mo>)</mo></mrow></mrow></math> for the training dataset rankitmix according to: elementary estimation (top left); logistic regression with interaction term (top center); artificial neural net annga of R (top right); weights-of-evidence (bottom left); logistic regression without interaction (bottom right). Full article ">
Commutation of targeting and simultaneous random rearrangement of all digital map images. Full article ">
Spatial distribution of two indicator predictor variables B1, B2 and the indicator target variable T of dataset DFQR. Full article ">
Spatial distribution of predicted conditional probabilities <math display="inline"> <mrow> <mover accent="true"> <mi>P</mi> <mo>^</mo></mover> <mrow> <mo>(</mo> <mrow> <mi>T</mi> <mo>=</mo> <mn>1</mn> <mo stretchy="false">|</mo> <msub> <mi>B</mi> <mn>1</mn></msub> <msub> <mi>B</mi> <mn>2</mn></msub></mrow> <mo>)</mo></mrow></mrow></math> for the training dataset DFQR according to: elementary estimation (top left); weights-of-evidence (top center); artificial neural net ANNGA of R (top right), ordinary logistic regression(bottom left), logistic regression with interaction term (bottom right). Full article ">

1444 KiB

Open AccessCommunication

Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection

by James Acker, Radina Soebiyanto, Richard Kiang and Steve Kempler

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1372-1386; https://doi.org/10.3390/ijgi3041372 - 10 Dec 2014

Cited by 19 | Viewed by 9881

Abstract

The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, [...] Read more.

The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, and air quality research. The use of Giovanni for researching connections between public health issues and Earth’s environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated (p < 0.05) with influenza activity in both temperate and tropical climate. In the two temperate locations studied, specific humidity was negatively correlated with influenza; conversely, in the three tropical locations, specific humidity was positively correlated with influenza. Influenza prediction using the regression models showed good agreement with the observed data (correlation coefficient of 0.5–0.83). Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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7023 KiB

Open AccessArticle

Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data

by Vanessa Machault, André Yébakima, Manuel Etienne, Cécile Vignolles, Philippe Palany, Yves M. Tourre, Marine Guérécheau and Jean-Pierre Lacaux

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1352-1371; https://doi.org/10.3390/ijgi3041352 - 10 Dec 2014

Cited by 24 | Viewed by 7813

Abstract

Controlling dengue virus transmission mainly involves integrated vector management. Risk maps at appropriate scales can provide valuable information for assessing entomological risk levels. Here, results from a spatio-temporal model of dwellings potentially harboring Aedes aegypti larvae from 2009 to 2011 in Tartane (Martinique, [...] Read more.

Controlling dengue virus transmission mainly involves integrated vector management. Risk maps at appropriate scales can provide valuable information for assessing entomological risk levels. Here, results from a spatio-temporal model of dwellings potentially harboring Aedes aegypti larvae from 2009 to 2011 in Tartane (Martinique, French Antilles) using high spatial resolution remote-sensing environmental data and field entomological and meteorological information are presented. This tele-epidemiology methodology allows monitoring the dynamics of diseases closely related to weather/climate and environment variability. A Geoeye-1 image was processed to extract landscape elements that could surrogate societal or biological information related to the life cycle of Aedes vectors. These elements were subsequently included into statistical models with random effect. Various environmental and meteorological conditions have indeed been identified as risk/protective factors for the presence of Aedes aegypti immature stages in dwellings at a given date. These conditions were used to produce dynamic high spatio-temporal resolution maps from the presence of most containers harboring larvae. The produced risk maps are examples of modeled entomological maps at the housing level with daily temporal resolution. This finding is an important contribution to the development of targeted operational control systems for dengue and other vector-borne diseases, such as chikungunya, which is also present in Martinique. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Graphical abstract

1238 KiB

Open AccessArticle

The RichWPS Environment for Orchestration

by Felix Bensmann, Dorian Alcacer-Labrador, Dennis Ziegenhagen and Rainer Roosmann

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1334-1351; https://doi.org/10.3390/ijgi3041334 - 5 Dec 2014

Cited by 7 | Viewed by 6892

Abstract

Web service (WS) orchestration can be considered as a fundamental concept in service-oriented architectures (SOA), as well as in spatial data infrastructures (SDI). In recent years in SOA, advanced solutions were developed, such as realizing orchestrated web services on the basis of already [...] Read more.

Web service (WS) orchestration can be considered as a fundamental concept in service-oriented architectures (SOA), as well as in spatial data infrastructures (SDI). In recent years in SOA, advanced solutions were developed, such as realizing orchestrated web services on the basis of already existing more fine-granular web services by using standardized notations and existing orchestration engines. Even if the concepts can be mapped to the field of SDI, on a conceptual level the implementations target different goals. As a specialized form of a common web service, an Open Geospatial Consortium (OGC) web service (OWS) is optimized for a specific purpose. On the technological level, web services depend on standards like the Web Service Description Language (WSDL) or the Simple Object Access Protocol (SOAP). However OWS are different. Consequently, a new concept for OWS orchestration is needed that works on the interface provided by OWS. Such a concept is presented in this work. The major component is an orchestration engine integrated in a Web Processing Service (WPS) server that uses a domain specific language (DSL) for workflow description. The developed concept is the base for the realization of new functionality, such as workflow testing, and workflow optimization. Full article

(This article belongs to the Special Issue 20 Years of OGC: Open Geo-Data, Software, and Standards)

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Graphical abstract

2214 KiB

Open AccessArticle

Accuracy and Effort of Interpolation and Sampling: Can GIS Help Lower Field Costs?

by Greg Simpson and Yi Hwa Wu

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1317-1333; https://doi.org/10.3390/ijgi3041317 - 5 Dec 2014

Cited by 25 | Viewed by 7801

Abstract

Sedimentation is a problem for all reservoirs in the Black Hills of South Dakota. Before working on sediment removal, a survey on the extent and distribution of the sediment is needed. Two sample lakes were used to determine which of three interpolation methods [...] Read more.

Sedimentation is a problem for all reservoirs in the Black Hills of South Dakota. Before working on sediment removal, a survey on the extent and distribution of the sediment is needed. Two sample lakes were used to determine which of three interpolation methods gave the most accurate volume results. A secondary goal was to see if fewer samples could be taken while still providing similar results. The smaller samples would mean less field time and thus lower costs. Subsamples of 50%, 33% and 25% were taken from the total samples and evaluated for the lowest Root Mean Squared Error values. Throughout the trials, the larger sample sizes generally showed better accuracy than smaller samples. Graphing the sediment volume estimates of the full sample, 50%, 33% and 25% showed little improvement after a sample of approximately 40%–50% when comparing the asymptote of the separate samples. When we used smaller subsamples the predicted sediment volumes were normally greater than the full sample volumes. It is suggested that when planning future sediment surveys, workers plan on gathering data at approximately every 5.21 meters. These sample sizes can be cut in half and still retain relative accuracy if time savings are needed. Volume estimates may slightly suffer with these reduced samples sizes, but the field work savings can be of benefit. Results from these surveys are used in prioritization of available funds for reclamation efforts. Full article

(This article belongs to the Special Issue Spatial Analysis for Environmental Applications)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Semivariograms for total sample (A and E), 50% subsample (B and F), 33% subsample (C and G) and 25% subsample (D and H) of sediment data from Dalton Lake and Major Lake, South Dakota. Crosses are average measures of the empirical values of the semivariogram cloud. Full article ">Figure 2
Map of sample area in the Black Hills of South Dakota. Triangle is the location within the Elk Creek Watershed where Dalton Lake is located. The circle is the location where Major Lake is located within the Spring Creek Watershed. Full article ">Figure 3
Mean sediment depths of all data interpolated from two Black Hills lakes. Error bars are 95% confidence intervals. Full article ">Figure 4
Sediment maps of Dalton Lake within the Black Hills of South Dakota using IDW interpolation to depict extent and distribution of sediment within the lake. Inverse Distance Weighted is identified with the subset letter (A), Spline is identified with the subset letter (B) and kriging is identified with the subset letter (C). Full article ">Figure 5
Sediment maps of Major Lake within the Black Hills of South Dakota using IDW interpolation to depict extent and distribution of sediment within the lake. Inverse Distance Weighted is identified with the subset letter (A), Spline is identified with the subset letter (B) and kriging is identified with the subset letter (C). Full article ">Figure 6
Plot of sediment predicted by differing sampling amounts from two sample lakes in the Black Hills of South Dakota. Full article ">

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Open AccessArticle

A GIS Approach to Urban History: Rome in the 18th Century

by Keti Lelo

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1293-1316; https://doi.org/10.3390/ijgi3041293 - 5 Dec 2014

Cited by 12 | Viewed by 14499

Abstract

This article explores the integration of GIS technology with urban historical studies, focusing on one case study from the 18th century, the project Historical atlas of the modern Rome. The methodology employed in this project allows for effectiveness and accuracy in historical data [...] Read more.

This article explores the integration of GIS technology with urban historical studies, focusing on one case study from the 18th century, the project Historical atlas of the modern Rome. The methodology employed in this project allows for effectiveness and accuracy in historical data acquisition and integration, which enables refined analyses of socioeconomic and environmental phenomena. The approach outlined in this article allowed researchers from different disciplines—city historians, archaeologists, demographists, economists, and so on—to interpret urban phenomenologies according to different thematic keys. These interpretations were derived from archival sources that complement each other and offer diversified insights into the urban context. The techniques described in the article are based on methods of data acquisition and spatial analysis developed in a GIS environment by exploiting the effectiveness of this technology in the quantitative treatment of cartographic and documentary sources. Full article

(This article belongs to the Special Issue Recent Developments in Cartography and Display Technologies)

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802 KiB

Open AccessArticle

A Systems Perspective on Volunteered Geographic Information

by Victoria Fast and Claus Rinner

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1278-1292; https://doi.org/10.3390/ijgi3041278 - 4 Dec 2014

Cited by 43 | Viewed by 11218

Abstract

Volunteered geographic information (VGI) is geographic information collected by way of crowdsourcing. However, the distinction between VGI as an information product and the processes that create VGI is blurred. Clearly, the environment that influences the creation of VGI is different than the information [...] Read more.

Volunteered geographic information (VGI) is geographic information collected by way of crowdsourcing. However, the distinction between VGI as an information product and the processes that create VGI is blurred. Clearly, the environment that influences the creation of VGI is different than the information product itself, yet most literature treats them as one and the same. Thus, this research is motivated by the need to formalize and standardize the systems that support the creation of VGI. To this end, we propose a conceptual framework for VGI systems, the main components of which—project, participants, and technical infrastructure—form an environment conducive to the creation of VGI. Drawing on examples from OpenStreetMap, Ushahidi, and RinkWatch, we illustrate the pragmatic relevance of these components. Applying a system perspective to VGI allows us to better understand the components and functionality needed to effectively create VGI. Full article

(This article belongs to the Special Issue Geoweb 2.0)

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4022 KiB

Open AccessArticle

Coupling Land Use Change Modeling with Climate Projections to Estimate Seasonal Variability in Runoff from an Urbanizing Catchment Near Cincinnati, Ohio

by Diana Mitsova

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1256-1277; https://doi.org/10.3390/ijgi3041256 - 4 Dec 2014

Cited by 30 | Viewed by 8563

Abstract

This research examines the impact of climate and land use change on watershed hydrology. Seasonal variability in mean streamflow discharge, 100-year flood, and 7Q10 low-flow of the East Fork Little Miami River watershed, Ohio was analyzed using simulated land cover change and climate [...] Read more.

This research examines the impact of climate and land use change on watershed hydrology. Seasonal variability in mean streamflow discharge, 100-year flood, and 7Q10 low-flow of the East Fork Little Miami River watershed, Ohio was analyzed using simulated land cover change and climate projections for 2030. Future urban growth in the Greater Cincinnati area, Ohio, by the year 2030 was projected using cellular automata. Projected land cover was incorporated into a calibrated BASINS-HSPF model. Downscaled climate projections of seven GCMs based on the assumptions of two IPCC greenhouse gas emissions scenarios were integrated through the BASINS Climate Assessment Tool (CAT). The discrete CAT output was used to specify a seed for a Monte Carlo simulation and derive probability density functions of anticipated seasonal hydrologic responses to account for uncertainty. Sensitivity analysis was conducted for a small catchment in the watershed using the Storm Water Management Model (SWMM) developed U.S. Environmental Protection Agency. The results indicated higher probability of exceeding the 100-year flood over the fall and winter months, and a likelihood of decreasing summer low flows. Full article

(This article belongs to the Special Issue Spatial Analysis for Environmental Applications)

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4355 KiB

Open AccessArticle

Improving Inland Water Quality Monitoring through Remote Sensing Techniques

by Igor Ogashawara and Max J. Moreno-Madriñán

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1234-1255; https://doi.org/10.3390/ijgi3041234 - 14 Nov 2014

Cited by 14 | Viewed by 8539

Abstract

Chlorophyll-a (chl-a) levels in lake water could indicate the presence of cyanobacteria, which can be a concern for public health due to their potential to produce toxins. Monitoring of chl-a has been an important practice in aquatic systems, especially [...] Read more.

Chlorophyll-a (chl-a) levels in lake water could indicate the presence of cyanobacteria, which can be a concern for public health due to their potential to produce toxins. Monitoring of chl-a has been an important practice in aquatic systems, especially in those used for human services, as they imply an increased risk of exposure. Remote sensing technology is being increasingly used to monitor water quality, although its application in cases of small urban lakes is limited by the spatial resolution of the sensors. Lake Thonotosassa, FL, USA, a 3.45-km² suburban lake with several uses for the local population, is being monitored monthly by traditional methods. We developed an empirical bio-optical algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) daily surface reflectance product to monitor daily chl-a. We applied the same algorithm to four different periods of the year using 11 years of water quality data. Normalized root mean squared errors were lower during the first (0.27) and second (0.34) trimester and increased during the third (0.54) and fourth (1.85) trimesters of the year. Overall results showed that Earth-observing technologies and, particularly, MODIS products can also be applied to improve environmental health management through water quality monitoring of small lakes. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Figure 1
Location of Lake Thonotosassa in the State of Florida, USA, and the location of sampling points from the Environmental Protection Commission of Hillsborough County. Full article ">Figure 2
Flow chart of the methodology used to develop an empirical model for chl-a estimation in Lake Thonotosassa. SeaDAS, normalized bias; ICE, Interactive Correlation Environment. Full article ">Figure 3
Time series of cyanobacteria biomass (CBB) estimations from (A) TN and (B) TP at the three sampling points, (C) data on measured chl-a concentrations. Full article ">Figure 4
OC3M application using SeaDAS 7.02 on a MODIS-Aqua L0 product. Full article ">Figure 5
Average reflectance spectra of MOD09GA for each seasonal period. Full article ">Figure 6
2D correlation plots of MOD09GA spectral bands using ICE [<a href="#B37-ijgi-03-01234" class="html-bibr">37</a>]. (A) JFM; (B) AMJ; (C) JAS; (D) OND. Full article ">Figure 7
Linear regression plots of the calibration between model values and chl-a concentration for each period of the year. (A) JFM; (B) AMJ; (C) JAS; (D) OND. Full article ">

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Open AccessArticle

Areal Delineation of Home Regions from Contribution and Editing Patterns in OpenStreetMap

by Dennis Zielstra, Hartwig H. Hochmair, Pascal Neis and Francesco Tonini

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1211-1233; https://doi.org/10.3390/ijgi3041211 - 3 Nov 2014

Cited by 22 | Viewed by 10950

Abstract

The type of data an individual contributor adds to OpenStreetMap (OSM) varies by region. The local knowledge of a data contributor allows for the collection and editing of detailed features such as small trails, park benches or fire hydrants, as well as adding [...] Read more.

The type of data an individual contributor adds to OpenStreetMap (OSM) varies by region. The local knowledge of a data contributor allows for the collection and editing of detailed features such as small trails, park benches or fire hydrants, as well as adding attribute information that can only be accessed locally. As opposed to this, satellite imagery that is provided as background images in OSM data editors, such as ID, Potlatch or JOSM, facilitates the contribution of less detailed data through on-screen digitizing, oftentimes for areas the contributor is less familiar with. Knowing whether an area is part of a contributor’s home region or not can therefore be a useful predictor of OSM data quality for a geographic region. This research explores the editing history of nodes and ways for 13 highly active OSM members within a two-tiered clustering process to delineate an individual mapper’s home region from remotely mapped areas. The findings are evaluated against those found with a previously introduced method which determines a contributor’s home region solely based on spatial clustering of created nodes. The comparison shows that both methods are able to delineate similar home regions for the 13 contributors with some differences. Full article

(This article belongs to the Special Issue Geoweb 2.0)

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1105 KiB

Open AccessArticle

Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

by Jay Lee, Mohammad Alnasrallah, David Wong, Heather Beaird and Everett Logue

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1198-1210; https://doi.org/10.3390/ijgi3041198 - 24 Oct 2014

Cited by 11 | Viewed by 7774

Abstract

The prevalence of obesity has increased dramatically in recent decades. It is an important public health issue as it causes many other chronic health conditions, such as hypertension, cardiovascular diseases, and type II diabetics. Obesity affects life expectancy and even the quality of [...] Read more.

The prevalence of obesity has increased dramatically in recent decades. It is an important public health issue as it causes many other chronic health conditions, such as hypertension, cardiovascular diseases, and type II diabetics. Obesity affects life expectancy and even the quality of lives. Eventually, it increases social costs in many ways due to increasing costs of health care and workplace absenteeism. Using the spatial patterns of obesity prevalence as an example; we show how different geographic units can reveal different degrees of detail in results of analysis. We used both census tracts and census block groups as units of geographic analysis. In addition; to reveal how different geographic scales may impact on the analytic results; we applied geographically weighted regression to model the relationships between obesity rates (dependent variable) and three independent variables; including education attainment; unemployment rates; and median family income. Though not including an exhaustive list of explanatory variables; this regression model provides an example for revealing the impacts of geographic scales on analysis of health data. With obesity data based on reported heights and weights on driver’s licenses in Summit County, Ohio, we demonstrated that geographically weighted regression reveals varying spatial trends between dependent and independent variables that conventional regression models such as ordinary least squares regression cannot. Most importantly, analyses carried out with different geographic scales do show very different results. With these findings, we suggest that, while possible, smaller geographic units be used to allow better understanding of the studies phenomena. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Open AccessArticle

Uncertainty in Geographic Data on Bivariate Maps: An Examination of Visualization Preference and Decision Making

by Ruojing W. Scholz and Yongmei Lu

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1180-1197; https://doi.org/10.3390/ijgi3041180 - 24 Oct 2014

Cited by 11 | Viewed by 6636

Abstract

Uncertainty exists widely in geographic data. However, it is often disregarded during data analysis and decision making. Proper visualization of uncertainty can help map users understand uncertainty in geographic data and make informed decisions. The study reported in this paper examines map users’ [...] Read more.

Uncertainty exists widely in geographic data. However, it is often disregarded during data analysis and decision making. Proper visualization of uncertainty can help map users understand uncertainty in geographic data and make informed decisions. The study reported in this paper examines map users’ perception of and preferences for different visual variables to report uncertainty on bivariate maps. It also explores the possible impact that knowledge and training in Geographic Information Sciences and Systems (GIS) may have on map users’ decision making with uncertainty information. A survey was conducted among college students with and without GIS training. The results showed that boundary fuzziness and color lightness were the most preferred visual variables for representing uncertainty using bivariate maps. GIS knowledge and training was found helpful for some survey participants in their decision making using bivariate uncertainty maps. The results from this case study provide guidance for reporting uncertainty on bivariate maps, aiming at encouraging informed decision making. Full article

(This article belongs to the Special Issue Recent Developments in Cartography and Display Technologies)

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12225 KiB

Open AccessArticle

Mapping VHR Water Depth, Seabed and Land Cover Using Google Earth Data

by Antoine Collin, Kazuo Nadaoka and Takashi Nakamura

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1157-1179; https://doi.org/10.3390/ijgi3041157 - 23 Oct 2014

Cited by 31 | Viewed by 10201

Abstract

Google Earth (GE) provides very high resolution (VHR) natural-colored (red-green-blue, RGB) images based on commercial spaceborne sensors over worldwide coastal areas. GE is rarely used as a direct data source to address coastal issues despite the tremendous potential of data transferability. This paper [...] Read more.

Google Earth (GE) provides very high resolution (VHR) natural-colored (red-green-blue, RGB) images based on commercial spaceborne sensors over worldwide coastal areas. GE is rarely used as a direct data source to address coastal issues despite the tremendous potential of data transferability. This paper describes an inexpensive and easy-to-implement methodology to construct a GE natural-colored dataset with a submeter pixel size over 44 km² to accurately map the water depth, seabed and land cover along a seamless coastal area in subtropical Japan (Shiraho, Ishigaki Island). The valuation of the GE images for the three mapping types was quantified by comparison with directly-purchased images. We found that both RGB GE-derived mosaic and pansharpened QuickBird (QB) imagery yielded satisfactory results for mapping water depth (R²_GE = 0.71 and R²_QB = 0.69), seabed cover (OA_GE = 89.70% and OA_QB = 80.40%, n = 15 classes) and land cover (OA_GE = 95.32% and OA_QB = 88.71%, n = 11 classes); however, the GE dataset significantly outperformed the QB dataset for all three mappings (Z_{Water depth} = 6.29, Z_Seabed = 4.10, Z_Land = 3.28, α_two-tailed < 0.002). The integration of freely available elevation data into both RGB datasets significantly improved the land cover classification accuracy (OA_GE = 99.17% and OA_QB = 97.80%). Implications and limitations of our findings provide insights for the use of GE VHR data by stakeholders tasked with integrated coastal zone management. Full article

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ISPRS Int. J. Geo-Inf., Volume 3, Issue 4 (December 2014) – 16 articles , Pages 1157-1511

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