ISPRS Int. J. Geo-Inf.2015, 4(2), 418-446; doi:10.3390/ijgi4020418 (registering DOI) - published 27 March 2015 Show/Hide Abstract
Abstract: Readability is a major issue with all maps. In this study, we evaluated whether we can predict map readability using analytical measures, both single measures and composites of measures. A user test was conducted regarding the perceived readability of a number of test map samples. Evaluations were then performed to determine how well single measures and composites of measures could describe the map readability. The evaluation of single measures showed that the amount of information was most important, followed by the spatial distribution of information. The measures of object complexity and graphical resolution were not useful for explaining the map readability of our test data. The evaluations of composites of measures included three methods: threshold evaluation, multiple linear regression and support vector machine. We found that the use of composites of measures was better for describing map readability than single measures, but we could not identify any major differences in the results of the three composite methods. The results of this study can be used to recommend readability measures for triggering and controlling the map generalization process of online maps.
ISPRS Int. J. Geo-Inf.2015, 4(1), 400-417; doi:10.3390/ijgi4010400 - published 19 March 2015 Show/Hide Abstract
Abstract: Even though fewer people die as a result of fire than other natural disasters, such as earthquake, flood, landslide, etc., the average loss of property due to fire is high. Kathmandu Metropolitan City is becoming more vulnerable to fire due to haphazard urbanization and increase in population. To control problems due to fire, systematic studies are necessary, including fire potential mapping and risk assessment. This study applies an Analytic Hierarchy Process (AHP) method in Kathmandu Metropolitan City, Nepal for generation of fire potential zonation map. The fire potential zonation map is prepared on the basis of available data of land use, fuel stations, and population density. This map shows that 58.04% of the study area falls under low fire potential zone, 32.92% falls under moderate fire potential zone and 9.04% falls under high fire potential zone. The map is also validated through major past fire incidents. The results show that the predicted fire potential zones are found to be in good agreement with past fire incidents, and, hence, the map can be used for future land-use planning.
ISPRS Int. J. Geo-Inf.2015, 4(1), 385-399; doi:10.3390/ijgi4010385 - published 18 March 2015 Show/Hide Abstract
Abstract: The use of sensor technologies is standard practice in the domain of precision farming. The variety of vendor-specific sensor systems, control units and processing software has led to increasing efforts in establishing interoperable sensor networks and standardized sensor data infrastructures. This study utilizes open source software and adapts the standards of the Open Geospatial Consortium to introduce a method for the realization of a sensor data infrastructure for precision farming applications. The infrastructure covers the control of sensor systems, the access to sensor data, the transmission of sensor data to web services and the standardized storage of sensor data in a sensor web-enabled server. It permits end users and computer systems to access the sensor data in a well-defined way and to build applications on top of the sensor web services. The infrastructure is scalable to large scenarios, where a multitude of sensor systems and sensor web services are involved. A real-world field trial was set-up to prove the applicability of the infrastructure.
ISPRS Int. J. Geo-Inf.2015, 4(1), 367-384; doi:10.3390/ijgi4010367 - published 6 March 2015 Show/Hide Abstract
Abstract: In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact). The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED) presentations by children and youth (age < 18 years) because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.
ISPRS Int. J. Geo-Inf.2015, 4(1), 337-366; doi:10.3390/ijgi4010337 - published 5 March 2015 Show/Hide Abstract
Abstract: We introduce spatial patterns of Tweets visualization (SPoTvis), a web-based geovisual analytics tool for exploring messages on Twitter (or “tweets”) collected about political discourse, and illustrate the potential of the approach with a case study focused on a set of linked political events in the United States. In October 2013, the U.S. Congressional debate over the allocation of funds to the Patient Protection and Affordable Care Act (commonly known as the ACA or “Obamacare”) culminated in a 16-day government shutdown. Meanwhile the online health insurance marketplace related to the ACA was making a public debut hampered by performance and functionality problems. Messages on Twitter during this time period included sharply divided opinions about these events, with many people angry about the shutdown and others supporting the delay of the ACA implementation. SPoTvis supports the analysis of these events using an interactive map connected dynamically to a term polarity plot; through the SPoTvis interface, users can compare the dominant subthemes of Tweets in any two states or congressional districts. Demographic attributes and political information on the display, coupled with functionality to show (dis)similar features, enrich users’ understandings of the units being compared. Relationships among places, politics and discourse on Twitter are quantified using statistical analyses and explored visually using SPoTvis. A two-part user study evaluates SPoTvis’ ability to enable insight discovery, as well as the tool’s design, functionality and applicability to other contexts.
ISPRS Int. J. Geo-Inf.2015, 4(1), 320-336; doi:10.3390/ijgi4010320 - published 25 February 2015 Show/Hide Abstract
Abstract: Digital Earth frameworks provide a tool to receive, send and interact with large location-based datasets, organized usually according to Discrete Global Grid Systems (DGGS). In DGGS, an indexing method is used to assign a unique index to each cell of a global grid, and the datasets corresponding to these cells are retrieved or allocated using this unique index. There exist many methods to index cells of DGGS. Toward facility, interoperability and also defining a “standard” for DGGS, a conversion is needed to translate a dataset from one DGGS to another. In this paper, we first propose a categorization of indexing methods of DGGS and then define a general conversion method from one indexing to another. Several examples are presented to describe the method.