Ocean & Coastal Management, Volume 103, January 2015, Pages 14–24
By Jinliang Huang, Yaling Huang,Robert Gilmore Pontius Jr., and Zhenyu Zhang
- GWR reveals spatial variation in water pollution-land use linkages.
- Water pollution is associated more with built-up than with cropland or forest.
- More built-up is associated with more pollution for less urbanized sub-watersheds.
- Forest has a stronger negative association with pollution in urban sub-watersheds.
- Cropland has a weak association with water pollution among 21 sub-watersheds.
“Land use can influence river pollution and such relationships might or might not vary spatially. Conventional global statistics assume one relationship for the entire study extent, and are not designed to consider whether a relationship varies across space. We used geographically weighted regression to consider whether relationships between land use and water pollution vary spatially across a subtropical coastal watershed of Southeast China. Surface water samples of baseflow for seven pollutants were collected twelve times during 2010–2013 from headwater sub-watersheds. We computed 21 univariate regressions, which consisted of three regressions for each of the seven pollutants. Each of the three regressions considered one of three independent variables, i.e. the percent of the sub-watershed that was cropland, built-up, or forest.
Local R2 values and local parameter estimates for GWR cropland models among three types of sub-watershed.
“Cropland had a local R2 less than 0.2 for most pollutants, while it had a positive association with water pollution in the agricultural sub-watersheds and a negative association with water pollution in the non-agricultural sub-watersheds. Built-up had a positive association with all pollutants consistently across space, while the increase in pollution per increase in built-up density was largest in the sub-watersheds with low built-up density. The local R2 values were stronger with built-up than with cropland and forest. The local R2 values for built-up varied spatially, and the pattern of the spatial variation was not consistent among the seven pollutants. Forest had a negative association with most pollutants across space. Forest had a stronger negative association with water pollution in the urban sub-watersheds than in the agricultural sub-watersheds. This research provides an insight into land-water linkages, which we discuss with respect to other watersheds in the literature.”
Transactions in GIS, Volume 18, Issue 3, pages 324–350, June 2014
By Dorothy M. Dick, Shaun Walbridge, Dawn J. Wright, John Calambokidis, Erin A. Falcone, Debbie Steel, Tomas Follett, Jason Holmberg, and C. Scott Baker
“To improve understanding of population structure, ecosystem relationships and predictive models of human impact in cetaceans and other marine megafauna, we developed geneGIS, a suite of GIS tools and a customized Arc Marine data model to facilitate visual exploration and spatial analyses of individual-based records from DNA profiles and photo-identification records. We used the open source programming language Python 2.7 and ArcGIS 10.1 software to create a user-friendly, menu-driven toolbar linked to a Python Toolbox containing customized geoprocessing scripts. For ease of sharing and installation, we compiled the geneGIS program into an ArcGIS Python Add-In, freely available for download from the website http://genegis.org. We used the Lord-Castillo et al. (2009) Arc Marine data model customization as the starting point for our work and retained nine key base Arc Marine classes. We demonstrate the utility of geneGIS using an integrated database of more than 18,000 records of humpback whales (Megaptera novaeangliae) in the North Pacific collected during the Structure of Populations, Levels of Abundance and Status of Humpback Whales in the North Pacific (SPLASH) program. These records represent more than 8,000 naturally marked individuals and 2,700 associated DNA profiles, including 10 biparentally inherited microsatellite loci, maternally inherited mitochondrial DNA, and genetic sex.”
A new netCDF Standards Working Group (SWG) is being chartered to further extend the existing netCDF standard with extension modules for additional data models, encodings, and conventions. Initiators of the new SWG seek comments from the public on the draft new charter. The comment period closes on 2014-07-11.
NetCDF has already been established as an adopted OGC standard, encompassing a core standard along with extensions for specific data models and encodings and for the Climate and Forecast (CF) metadata conventions. The additional extensions to be addressed by the new netCDF SWG include, but are not limited to, those currently under consideration by the currently existing CF-netCDF 1.0 SWG, which will be disbanded and replaced by this NetCDF SWG.
NetCDF (network Common Data Form) is a data model for multidimensional array-oriented scientific data, a freely distributed collection of access libraries implementing support for that data model, and a machine-independent storage format. Together, the interfaces, libraries, and format support the creation, access, and sharing of scientific data.
Having already established netCDF as an OGC standard for binary encoding has made it possible to incorporate standard delivery of data in binary form via several OGC protocols, including the OGC Web Coverage Service (WCS), Web Feature Service (WFS), and Sensor Observation Service (SOS) Interface Standards. Work is already underway on an extension to GML and OWS for delivery of data encoded in netCDF. Additional netCDF conventions extensions will improve the effectiveness and usability of netCDF datasets by a wider community. One example is the recently released OGC NetCDF Uncertainty Conventions Discussion Paper.
The OGC® is an international consortium of more than 475 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. Visit the OGC website at http://www.opengeospatial.org/.
[Source: OGC press release]
GIScience Research Track
Esri International User Conference
14-18 July, 2014
San Diego, California
Call for Papers, Transactions in GIS special issue
GI Science researchers are invited to present original manuscripts for a peer-reviewed journal and presentation in the GIScience Research Track of the 2014 Esri International User Conference.
Papers in this special track must focus on cutting-edge research in GIScience and need not be Esri software related. Full papers will be included in a special issue of the journal Transactions in GIS to be distributed at the 2014 Conference. Abstracts (500 words) must be submitted to Dr. John Wilson, University of Southern California, by 15th December, 2013.
The Transactions in GIS editorial team will review abstracts based on their GIScience content and select a maximum of nine abstracts to become full papers. Notice of acceptance will occur by end of December, 2013. Full papers (maximum 6,000 words plus figures, tables, and references in appropriate format for publication) must be submitted to Dr. Wilson for independent review by 15th February, 2014. Reviewed papers will be returned to authors by 15th March, 2014 and final manuscripts must be returned by 8th April, 2014, to be included in the special issue of Transactions in GIS.
A listing of the 2013 accepted papers can be found at the journal website: http://onlinelibrary.wiley.com/doi/10.1111/tgis.2013.17.issue-3/issuetoc
For questions or guidelines on this GIScience Research Track, please contact Michael Gould at email@example.com.
Abstracts should be submitted via email with a subject line “Esri GIScience Abstract, Authors Last Name” no later than 15th December, 2013 to:
Dr. John Wilson, firstname.lastname@example.org
Computers & Geosciences, published online 22 October 2012
Tom Kwasnitschka, Thor H. Hansteen, Colin W. Devey, and Steffen Kutterolf
- A new technology for deep-sea micro scale mapping is demonstrated.
- Photogrammetry based on ROV video yields 3D models.
- Quantitative data extraction yields geoscientific insights.
- The workflow is readily replicable and based on industrial software.
“Remotely Operated Vehicles (ROVs) have proven to be highly effective in recovering well localized samples and observations from the seafloor. In the course of ROV deployments, however, huge amounts of video and photographic data are gathered which present tremendous potential for data mining. We present a new workflow based on industrial software to derive fundamental field geology information such as quantitative stratigraphy and tectonic structures from ROV-based photo and video material.
Warping effects due to missing lens distortion parameters (a) superimposed on the correct reconstruction (b). Both models have been aligned at the first camera pose (c), where deviations in the model geometry and position are already apparent. The largest dislocation (gray arrow) in position and camera angle is found between the last images, (d) showing the warped path and (e) the correct path, deviating 29° in pitch, 8° in roll and 1.8° in heading. Crosses mark the location of a corresponding feature referenced in the text. Measurements of a corresponding bedding plane (white planes) indicate a strong deviation in strike (67°, lines) and dip (12°, arrows). The light transparent model (f) and camera planes (g) illustrate the model, which has been aligned to the track coordinates, resulting in positioning and also scaling errors. The white grid represents the true horizontal plane.
“We demonstrate proof of principle tests for this workflow on video data collected during dives with the ROV Kiel6000 on a new hot spot volcanic field that was recently identified southwest of the island of Santo Antão in the Cape Verdes. Our workflow allows us to derive three-dimensional models of outcrops facilitating quantitative measurements of joint orientation, bedding structure, grain size comparison and photo mosaicking within a georeferenced framework. The compiled data facilitate volcanological and tectonic interpretations from hand specimen to outcrop scales based on the quantified optical data. The demonstrated procedure is readily replicable and opens up possibilities for post-cruise “virtual fieldwork” on the seafloor.”
Computers & Geosciences, Volume 42, May 2012, Pages 64-70
Igor Rychkov, James Brasington, and Damià Vericat
- We present a software toolkit for processing terrestrial point clouds.
- The toolkit can be applied to TLS surveys of gravel river beds.
- Improved DEM differencing is one outcome.
- Estimating surface roughness and grain size distribution is possible now with point-based, statistical metrics.
- Other applications and extensions are enabled by the library being freely available and open source.
“Processing of high-resolution terrestrial laser scanning (TLS) point clouds presents methodological and computational challenges before a geomorphological analysis can be carried out. We present a software library that effectively deals with billions of points and implements a simple methodology to study the surface profile and roughness. Adequate performance and scalability were achieved through the use of 64-bit memory mapped files, regular 2D grid sorting, and parallel processing. The plethora of the spatial scales found in a TLS dataset were grouped into the “ground” model at the grid scale and per cell, sub-grid surface roughness. We used centroid-thinning to build a piecewise linear ground model, and studied “detrended” standard deviation of relative elevations as a measure of surface roughness. Two applications to the point clouds from gravel river bed surveys are described. Linking empirically the standard deviation to the grain size allowed us to retrieve morphological and sedimentological models of channel topology evolution and movement of the gravel with richer quantitative results and deeper insights than the previous survey techniques.”
GIScience 2012, Columbus, Ohio, 18-21 September 2012
Jared Aldstadt, Michael Widener, and Neal Crago
“In the age of “Big Data” GIScientists are faced with the challenging task of processing and interpreting increasingly large spatial datasets. To fully exploit these large and high-resolution data, researchers must adapt existing algorithms and utilize new computational technologies, such as high performance computers (HPCs) (Armstrong 2000). This research concentrates on improving the performance of local irregular cluster detection algorithms, focusing on one flexible, but computationally expensive, algorithm: AMOEBA (Aldstadt and Getis 2006). The ability to locally detect clusters within a geographic region is a powerful tool that can be applied in many settings. For example, in a health context, it is important to explicitly locate the clustering of disease incidents so an appropriate medical intervention can occur. AMOEBA is one of a growing number of cluster detection techniques that do not restrict the search to regular geometric shapes (Duczmal and Assuncao 2004, Tango and Takahashi 2005, Assuncao et al. 2006, Yiannakoulias et al. 2007). These methods have the potential to more accurately delineate clusters, thereby improving the value of clustering as an exploratory data analysis technique.
“While the ability to detect irregularly shaped clusters is useful, the underlying process of AMOEBA involves organically growing the cluster from every spatial data point, known as seed locations, in a predefined study area. This research builds on previous work (Duque et al. 2011a, Widener et al. 2012) by developing a heuristic and decomposition strategies for parallel computing platforms to improve the runtime of the algorithm. The heuristic is designed to intelligently sample seed locations in sub-regions of a large dataset in order to eliminate redundant cluster discovery. Current AMOEBA algorithms spend valuable computation time rediscovering the same irregular cluster, as all seed locations apart of a particular cluster will grow approximately or exactly the same hotspot (or coldspot). With the heuristic reducing the number of computations necessary in some sub-regions but not in others, new decomposition strategies are necessary to equitably distribute the computational load of growing clusters from those seed locations that are tested. In addition to providing researchers with a faster tool for detecting irregularly shaped clusters in large or detailed datasets, this research also provides general insights into how efficient local spatial cluster detection can be achieved on parallel computing platforms.”
GIScience 2012, Columbus, Ohio, 18-21 September 2012
Thomas Blaschke and Clemens Eisank
“GIScientists find themselves sometimes in a somewhat defending role when to position Geographic Information Science. Kemp et al. (2012) state that researchers in this field often find it difficult to argue in established disciplines like Geography, Statistics, or Computer Science. Kemp et al. diagnose reasons for this to include problems of a narrow focus on indices like Thomson-Reuters’ for use in assessment metrics, the relative importance of conferences versus journals, or different criteria used in geography and computer science (as well as other fields, such as statistics or economics), or the highly variable meaning of “strong impact factors” across fields, and so on (Kemp et al. 2012: 268).
“Reitsma (2012) argued that GIScience may be considered as a science if using similar criteria as Stamos (2007), namely simplicity, predictive accuracy, coherence with known facts and testability and pleads not for just a yes or no classification, but for some kind of degree classification. What distinguishes GIScience from man other sciences is the fact that GIScientists study the representation of the world and not the world in concrete. For this knowledge and principles from other disciplines are needed and used (Reitsma 2012:9). Following this line of arguments one may reason that GIScience exists in symbiosis with other disciplines and can hardly exist without them. This could be one reason why it is still in the process of self-justification. Nevertheless, in this short article, we will avoid to discuss whether or not GIScience is a science or a discipline and if it is scientific at all. Rather, we try to analyse in an unbiased and neutral way how well GIScience is reflected in the literate. We are well aware of the limitations of this approach. We of course reduce a positive hit as a “citations” if a search term is found in the title or keywords of a particular database. This will produce quite a high number of errors of omission and – less often – errors of commission: we – as many other studies which analyse publications and the number of their citations in other publications – hypothesize that an entry is a confirmation of its contents.”
GIScience 2012, Columbus, Ohio, 18-21 September 2012
M.W. Horner and J. A. Downs
“Mobile object analysis continues to be well-studied in GIScience (Hornsby and Egenhofer 2002; Laube et al. 2005; Neutens et al. 2011). Time geography remains the key theoretical framework for understanding mobile objects’ movement possibilities (Miller 2005). Within time geography, recent efforts have sought to enhance its ‘probabilistic’ potential through exploring questions of data uncertainty, spatial representation, and limitations of classical approaches (Kuijpers et al. 2010; Neutens et al. 2011; Winter and Lin 2011). Along these lines, Downs (2010) fused time geography and kernel density estimation in developing timegeographic density estimation (TGDE), which may be used to estimate mobile objects’ probable locations in continuous space, given a time budget between control points (Downs 2010). Downs and Horner (2012) extend TGDE to discrete network space, demonstrating its application with GPS-based vehicle tracking data (Downs and Horner 2012) and using it in searches for travellers’ destinations missing in travel surveys (Horner et al. 2012).
Intensity Values for Traveller 1 (eq. 1).
“The present paper explores a new direction for TGDE, namely the creation of a densitybased accessibility measure for mobile objects. Related to time geography, accessibility measures have also garnered widespread attention in the literature (Kwan 1998; Miller 1999; O’Sullivan et al. 2000; Yu and Shaw 2008; Delafontaine et al. 2012). Our new metrics gauge how accessible a moving object is to particular opportunities of interest, given the constraints inherent to its movement plan. Thus, we are able not only visualize where the object most likely could have been (Downs and Horner 2012), but we also capture the configuration and magnitude of activities relative to its travel path from both a visual and analytic perspective.”
The application deadline is October 15, 2012
The Association of American Geographer’s Marble Fund for Geographic Science is pleased to announce the new Marble-Boyle Undergraduate Achievement Awards. These awards aim to recognize excellence in academic performance by undergraduate students from the United States and Canada who are putting forth a strong effort to bridge geographic science and computer science as well as to encourage other students to embark upon similar programs. These awards, together with the William L. Garrison Award for Best Dissertation in Computational Geography, are activities of the Marble Fund and are supported by donations to the Fund. In the case of the current awards, the support of Mr. Jack Dangermond is gratefully acknowledged.
Duane Marble and Ray Boyle in Honolulu at the Joint U.S.–Australia GIS Meeting in 1982
The award is named for Dr. Duane Marble, creator of the Marble Fund, and for the late Dr. A. R. (Ray) Boyle who was a major contributor to the early development of both computer cartography and geographic information systems. Ray Boyle was born in England in 1920 and served with the British Admiralty during World War II. After the war, he developed and patented many graphic systems for plotting and digitizing and was the inventor of the “free cursor” digitizing system that was the basis for the digital entry of much early spatial data. After moving to Canada in 1965 he was appointed Professor of Electrical Engineering at the University of Saskatchewan where, among many other achievements, he developed the first digital system for nautical chart production for the Canadian Hydrographic Service. He worked for many years with Dr. Roger Tomlinson on the technical evaluation of GIS software and was also an active member of the IGU Commission on Geographical Data Sensing & Processing for many years.
At any time during the stated application period applicants may submit an application using our online application form. This form will ask applicants to provide:
- Personal information (name, address, email, phone number, etc.) together with a statement of their intention to apply for the award.
- An essay of no more than 800 words, formatted in 12 pt Times New Roman, double-spaced with 1” margins, identifying the individual’s areas of interest within geographic science and computer science, a description of his or her career goals, and a statement as to how his or her joint background in geographic science and computer science will help to meet these goals.
- A letter of recommendation from the student’s faculty advisor, department chair or program director. The letter should clearly address both the applicant’s academic performance as well as their potential for a career or further education that utilizes their geographic science and computer science background. Recommendation letters must be signed and written on institutional letterhead. They must be sent in PDF form directly by the referee to the Chair of the Review Committee to email@example.com .
- An official or unofficial copy of the applicant’s transcript(s) with all relevant courses in geographic science and computer science, and grades received in these courses, highlighted.
- For each highlighted course, the applicant must provide a one or two sentence description of the course content, the text book(s) used (if applicable), and the name(s) of the instructor(s).
All elements of the application must be received at firstname.lastname@example.org no later than the close of business in Washington, D.C. on October 15, 2012.
Full information on the awards and the application process may be found at http://www.aag.org/cs/marble-boyle
[Source: AAG press release]