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 firstname.lastname@example.org.
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, email@example.com
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.”