ISPRS Int. J. Geo-Inf.2015, 4(2), 607-625; doi:10.3390/ijgi4020607 - published 16 April 2015 Show/Hide Abstract
Abstract: In an effort to reforest school sites with limited resources, communities and non-profits have implemented projects on Los Angeles Unified School District (LAUSD) school sites, often without thought for the best location, long-term maintenance, or appropriateness of the tree type. Although studies exist related to sun safety policies in schools, there has been little emphasis on the environmental public health benefits of trees in public schools. The LAUSD School Shade Tree Canopy Study was a response to this issue in which data was analyzed (a total of 33,729 trees in the LAUSD were mapped) regarding tree canopy coverage, pervious/impervious areas, and buildings for 509 elementary schools to assess urban forestry management issues and environmental injustices within communities of the district. The results of these analyses indicate that there is a wide range of school site size, tree canopy coverage as a percentage of school site size, tree canopy coverage as a percentage of play area, and percentage of unpaved surface play areas (e.g., (~20%) of the schools have both (0.0%) tree canopy coverage play areas and 100% paved surfaces). This finding alone has implications in how the LAUSD may implement sun safe polices which would aid in preventing skin cancer and other adverse health outcomes for students within the school district.
ISPRS Int. J. Geo-Inf.2015, 4(2), 591-606; doi:10.3390/ijgi4020591 - published 15 April 2015 Show/Hide Abstract
Abstract: By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This possibility needs to be comprehensively investigated and tested in collaboration with transportation domain specialists.
ISPRS Int. J. Geo-Inf.2015, 4(2), 572-590; doi:10.3390/ijgi4020572 - published 13 April 2015 Show/Hide Abstract
Abstract: Geographical masks are a group of location protection methods for the dissemination and publication of confidential and sensitive information, such as health- and crime-related geo-referenced data. The use of such masks ensures that privacy is protected for the individuals involved in the datasets. Nevertheless, the protection process introduces spatial error to the masked dataset. This study quantifies the spatial error of masked datasets using two approaches. First, a perceptual survey was employed where participants ranked the similarity of a diverse sample of masked and original maps. Second, a spatial statistical analysis was performed that provided quantitative results for the same pairs of maps. Spatial statistical similarity is calculated with three divergence indices that employ different spatial clustering methods. All indices are significantly correlated with the perceptual similarity. Finally, the results of the spatial analysis are used as the explanatory variable to estimate the perceptual similarity. Three prediction models are created that indicate upper boundaries for the spatial statistical results upon which the masked data are perceived differently from the original data. The results of the study aim to help potential “maskers” to quantify and evaluate the error of confidential masked visualizations.
ISPRS Int. J. Geo-Inf.2015, 4(2), 551-571; doi:10.3390/ijgi4020551 - published 10 April 2015 Show/Hide Abstract
Abstract: Remotely sensed images are important sources of information for a range of spatial problems. Air photo interpretation emerged as a discipline in response to the need to develop a systematic method for analysis of reconnaissance photographs during World War I. Remote sensing research has focused on the development of automated methods of image analysis, shifting focus away from human interpretation processes. However, automated methods are far from perfect and human interpretation remains an important component of image analysis. One important source of information concerning human image interpretation process is textual guides written within the discipline. These early texts put more emphasis than more recent texts, on the details of the interpretation process, the role of the human in the process, and the cognitive skills involved. In the research reported here, we use content analysis to evaluate the discussion of air photo interpretation in historical texts published between 1922 and 1960. Results indicate that texts from this period emphasized the documentation of relationships between perceptual cues and images features of common interest while reasoning skill and knowledge were discussed less so. The results of this analysis provide a framework of expert image skills needed to perform image interpretation tasks. The framework is useful for informing the design of semi-automated tools for performing analysis.
ISPRS Int. J. Geo-Inf.2015, 4(2), 535-550; doi:10.3390/ijgi4020535 - published 8 April 2015 Show/Hide Abstract
Abstract: OpenStreetMap (OSM) constitutes an unprecedented, free, geographical information source contributed by millions of individuals, resulting in a database of great volume and heterogeneity. In this study, we characterize the heterogeneity of the entire OSM database and historical archive in the context of big data. We consider all users, geographic elements and user contributions from an eight-year data archive, at a size of 692 GB. We rely on some nonlinear methods such as power law statistics and head/tail breaks to uncover and illustrate the underlying scaling properties. All three aspects (users, elements, and contributions) demonstrate striking power laws or heavy-tailed distributions. The heavy-tailed distributions imply that there are far more small elements than large ones, far more inactive users than active ones, and far more lightly edited elements than heavy-edited ones. Furthermore, about 500 users in the core group of the OSM are highly networked in terms of collaboration.
ISPRS Int. J. Geo-Inf.2015, 4(2), 515-534; doi:10.3390/ijgi4020515 - published 7 April 2015 Show/Hide Abstract
Abstract: To improve decision making, real-time population density must be known. However, calculating the point density of a huge dataset in real time is impractical in terms of processing time. Accordingly, a fast algorithm for estimating the distribution of the density of moving points is proposed. The algorithm, which is based on variational Bayesian estimation, takes a parametric approach to speed up the estimation process. Although the parametric approach has a drawback, that is the processes to be carried out on the server are very slow, the proposed algorithm overcomes the drawback by using the result of an estimation of an adjacent past density distribution.