The placement and configuration of the projector is important because it affects the coverage, distortion, and visibility of the projected data. For example, in some setups the 3D scanner may be caught in the projection and would thus cast a shadow over the model. Therefore it can be practical to use a short-throw projector because it can be placed to the side of the physical model at a height similar to the 3D scan- ner (Fig. 2.1b and 2.3). Since the projection is cast from the side, the projection beam does not cross the 3D scanning device and no shadow is cast. However, with a short-throw projector there is a certain level of visible distortion when project- ing on a physical model that has substantial relief. The distortion occurs because the light rays reach the model at an acute angle. The horizontal position at which the projected light intersects the model is shifted from the position at which it would intersect with a flat surface. Larger differences in height increase the distortion. The- oretically, we can remove the distortion by either warping the projected data itself or using the projector to automatically warp the projected image. The first solution would require an undistorted dataset for geospatial modeling and a warped copy of that data for projecting. That is impractical, especially when working with many different raster and vector layers. The other solution requires the projector itself to warp the image; while this technology exists, it is only offered by a few projector manufacturers and such projectors are typically more expensive. Moreover, as the landscape is modified, the warping pattern should change as well. Currently it is not possible to find this feature in the off-the-shelf projectors.
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The presented coupling of a tangible physical model and Grass GIS represents a promising first step in an effort to build tangible geospatial modeling environ- ments that will allow users to interact with 3D land- scape data using the natural human ability to work with their hands. We need to further research several areas to better understand the usability and potential for applications. In particular, we need to investigate the range of scales that manual manipulation of a physical landscape model can support and to assess the feasibil- ity of adding zoom-in capabilities at least for the cou- pling with the computer-controlled physical model. A related issue is the merging of the model data with the real-world data and a tradeoff between real-time response and accuracy.
The site suitability modeling for locating the groundwater poten- tial zones using GIS analysis has an added advantage over conven- tional survey. The mapping of groundwater resources has assumed importance in recent years because of increased demand for water. Utilization of remote sensing and GIS is a powerful tool for water resources management. It plays an important role in integrating all the data to generate various thematic maps for preparing ground- water potential map. The geomorphic units’ buried pediment and water bodies are good prospective zones for groundwater explora- tion. Presence of high lineament density, low drainage density and low slope indicate the occurrence of groundwater. The resultant maps of the study revealed that about sixty six percent of the Kal- lar watershed comes under the category of good to moderate groundwater prospective zones, while, the remaining area falls in the poor zone. The integrated groundwater potential map, thus, could be useful for development of sustainable scheme for groundwater development in the area.
Geospatial analysis of the wine grape quality parameter, anthocyanin in Twin Creeks and Merjan vineyards in San Joaquin Valley California region evaluated the potential of optimum field sampling in differential harvesting. An average of 3, 5, and 7 SPA anthocyanin predictions using geo-statistical ordinary kriging analysis were compared against reference of ~10 SPA (9.7, 8.3, and 8.9 for Twin Creeks, Merjan and Merjan South block vineyards, respectively) to capture vineyard spatial variability. Two strategies were pursued for geospatial analysis, strategy I based on random sampling of sampling sites in a vineyard block amounting to 3, 5, and 7 SPA while strategy II is based on random sampling of 3, 5 and 7 SPA from each 1 acre block segments in the vineyards.
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Geospatial data can be presented either in raster or vector form. We have chosen to develop the sinkhole geo-hazard model as a raster model, because they are easier to manipulate and process. The calculation and determination of the sinkhole hazard map depend on the geospatial data presentation model (raster or vector) of its factors. The difference of these factors data model requires unification. Between the raster model and vector model, the raster data model are easy to manipulate and process compared with vector models. Thus, the output data model of the sinkhole geohazard map. The calcula- tion unit of the raster data model is pixel (P); where the pixel presents, in reality, a metric square. Each contrib- uted hazard factor (F) is expressed in matrix of pixels that present the raster model of that factor. Depending on the number of the factors (n), the total contributed factors in geo-hazards map are F n ; for each pixel of the
Previous groundwater assessment involves various elemental analyses which are subjected to different statistical computations either aimed to check for variance or trend. This method, though still in use produces numerous results that are sometimes difficult to interpret and inadequate for spatial analysis. Based on this, the Water Quality Index Computation and the use of GIS (Geospatial techniques) have been introduced to provide an easy assessment of spatial distribution of water quality in different areas (Kavita, 2010). Geospatial techniques for water quality index have not been carried out in Nigeria especially in the Jos Plateau area which has a peculiar geology in Nigeria. Surface water sources are generally seasonal in this area for which reason most residents depend on groundwater.
Researchers use social media and geospatial analysis for forecasting political opinions on the web (Sobkowicz et al., 2012), identifying and mapping global virtual communities (Stefanidis et al., 2013), making meteorological observations (Hyvärinen and Saltikoff, 2010), studying structure and dynamics of natural cities (Jiang and Miao, 2015), tracking infectious diseases (Padmanabhan et al., 2013), managing crisis situations (MacEachren et al., 2011a), capturing human movement patterns across political borders (Blanford et al., 2015), discovering significant events and patterns (Andrienko et al. 2010b), understanding protest movements (Gleason, 2013), finding geographic patterns of communication networks (Conover et al., 2013), and answering many other questions related to human movements and communication. The general trend is the following: researchers use maps 1) to report their findings, 2) to verify whether social media is more reliable than other techniques, 3) to discover new patterns and insights about phenomena, 4) to generate hypotheses about phenomena, and 5) to understand laws that can explain how networks work. For example, in a recent paper, Krings et al. (2009) investigated the network of mobile phone customers and analyzed the geographical patterns of the customers. After
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where it is attempted to substitute the provided three- dimensional points with a surface (function). For training several preference models are used of as expressed through a variety of fuzzy membership functions (FMFs). The approach is simple yet effective: gradually increase the complexity of the underlying FMF until an accept- able solution is reached. The process begins by interpo- lating a set of planes to the training dataset . We examine the resulting accuracy and if it is within the predefined specifications we end the process. These pre- defined specifications are in essence thresholds describ- ing the maximum acceptable error between the interpo- lated functions and the training points. They can be pre- set by the database designer or adjusted in real-time by the user. If the results are not within these thresholds, we examine the obtained plane parameters. This analysis leads to a decision whether similarity is dependent on the query value, their difference metric or the actual database and query values. We continue by interpolating two sig- moidal functions whose initial approximations are calcu- lated from the plane properties. If required accuracy is not achieved, we provide further modeling capabilities by parameterizing further the FMFs parameters. At the last stage we obtain the best possible set of FMFs that express user preference as presented through the training set. If accuracy is not yet achieved, we trigger a neural network process to correct local errors. More information on the training mechanism and the corresponding mod- eling capabilities can be found in .
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Time and financial resources did not allow for testing the scalability of the application. However, the study has shown that cloud computing has the potential to facilitate scalable web services in the cloud, thus increasing uptake of SDIs. Cloud services can also provide higher quality services, improved performance, reliability of geoservices, and improved accessibility to geospatial data and services. Several risks and benefits of cloud computing and other trends, which practitioners should be aware of, have been discussed.
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Heat maps are first used to plot zip-code level claims count data for 2016 through 2018. Subsequently, heat maps illustrate county-level opioid-related inpatient claim per 10,000 population for the same years. For rates, county-level data were selected rather than zip-code level data, as zip-code level data resulted in outliers that influenced interpretation. (Some zip codes have very small populations, yet still have admissions.) The heat maps illustrate the intensity of opioid admissions and rates by color-coding map areas. When used properly, they can highlight geographic variation . The use of heat maps in healthcare is ubiquitous, as they have been used for improving minority health surveillance , examining birth outcomes , and many other applications. The value in geospatial-temporal analysis is the graphical depiction of change in demand over time. The significance of changes for 2016 to 2017 claims and claim rates, years with complete data, are evaluated by a non-parametric t-test, the Wilcoxon matched pairs signed rank test, since parametric assumptions such as normality, homogeneity of variance, and independence of do not hold . Matching is performed to account for the geographic unit (zip codes for claims and counties for claim rates).
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Abstract- Geospatial data is becoming massive which leads to effective data management by compressing, updating and querying the data fields of the multidimensional spatial and temporal data. The explosion of both the data volumes and dimensionality of these geospatial field data makes the storage, management, query and processing a daunting challenge to existing solutions. In this work, hierarchical tensor decomposition based on the split-and-merge paradigm is developed for continuously compression and appending of multidimensional geospatial field data. the goal is to propose a hierarchical data structure to reformulate and store the large volume of geospatial field data and to develop methods for data storage, query and computation support using this data structure. The Proposed work is achieved through a prototype implementation. The prototype has five components: 1) the design of a buffered hierarchical data structure and data decomposition strategies; 2) a proposal for a blocked data separation mechanism for splitting the huge tensors into small blocks according to the spatial-temporal reference; 3) a proposed algorithm that allows for data appending which is free of arithmetical operations and also computationally adaptive with continuous compression; 4) the development of a hierarchical structure-preserving and dimensional-independent data query which needs only to reform the row of the matrix in the leaf node; 5) the provision of computational operators such as tensor addition and linear operations, as well as a hierarchical structure-preserving computational framework.
Over the past two decades, LiDAR mapping has been conducted along the U.S. east coast (including the Outer Banks, North Carolina) on a near annual basis – generating a rich time series of topographic data with unprecedented accuracy, resolution, and extent. This time series has captured the response of the landscape to episodic storms, daily forcing of wind and waves, and anthropogenic activities. This work presents raster-based geospatial techniques developed to gain new insights into coastal geomorphology from the time series of available LiDAR. Per-cell statistical techniques derive information that is typically not obtained through the techniques traditionally employed by coastal scientists and engineers. Application of these techniques to study sites along the Outer Banks, NC, revealed substantial spatial and temporal variations in terrain change. Additionally, they identify the foredunes as being the most geomorphologically dynamic coastal features.
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The research presented in this thesis reveals the level of rightness of the re- currence Prediction systems by correlated with geospatial effect. The Geospatial technology elements split up: Geographic Information System (GIS), Remote Sensing (RS) and Global Positioning System (GPS) consolidated into this tech- nique in light of the fact that the vast majority of the components in radio wave propagation are geographic highlights. In this exploration, ICEPAC remote ar- ranging programming is tried in a field test completed in Tigray and Afar dis- trict. The consequence show that, the Prediction programming doesn’t put, day by day, regular and month to month topographical marvels into thought. More- over, it doesn’t demonstrate the correct area of the radio stations. Furthermore, the new proposed ICEPAC Calibration algorithm anticipates a good Signal quali- ty for frequencies in the vicinity of 1.5 MHz up to 30 MHz. The total result showed that Geographical Information Systems (GIS) are getting to be noticeably val- uable apparatuses in accumulation, stockpiling, control and portrayal of Geo spatial information and also the RS and GIS situated Signal quality forecast can essentially enhance forecast quality contrasted with the hypothetical free space demonstration which does not consider any Geo spatial and neighborhood land- scape highlights impacts.
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changes in the uptake of skilled birth care across the policy periods. The mean and quartile distributions of the continuous covariates aggregated by women who received skilled birth care and those who received unskilled birth care were also examined. Bayesian Geoadditive Semiparametric (BGS) regression technique  was used to examine the extent of geospatial dependence in skilled birth care use and their associative relationships with ma- ternity fee paying policies focusing on the temporal trends when the policies were functional. To identify the inde- pendent effect of the policies, important predictors of skilled birth care use were accounted for based on the lit- erature and data availability. A key advantage of the BGS technique is that it allows for the simultaneous estimation of non-linear effects of continuous covariates as well as fixed effects of categorical and continuous covariates in addition to spatial effects. BGS models also produce maps of the posterior spatial effects, which allows for the impact of the covariates in explaining the spatial patterns of the outcome variable to be examined.
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Floods are one of the most common and expensive natural hazards in the United States, affecting communities at various levels. To facilitate and improve flood planning, forecasting, damage assessments, and emergency responses, the USGS-funded FloodGrid project provides an integrated platform for inundation modeling, property loss estimation, and visualization. Rather than centralizing all capabilities onto a specific platform, we have developed this system following open service architecture principles, packaging functionalities as Web Services and pipelining them as an end-to-end workflow. Integration is achieved via a simple Web interface requiring minimal user interactions. This is an example of a relatively simple Science Gateway. As we review here, even this simple system combines real-time data services, computational services, GIS information and data services, and several data models. We build some of these services and leverage third party providers for others. We may consider this to be analogous to a Web 2.0 mash-up. FloodGrid is a collaboration between the Polis Center at IUPUI and the Community Grids Laboratory at IU.
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The main purposes of this study are to examine the general geospatial demand for overnight recreation on federal lands prior to the 2008 recession and to examine the speci ﬁ c geospatial demand for selected national park regions. The national geospatial demand for overnight recreation on federal lands provides a snapshot from which speci ﬁ c national park regions were selected for further investigation. The geospatial demand for the selected national park regions were then used to characterize the destination as having some combination of local, regional or national visitors. By inves- tigating the geospatial distribution of visitors to national parks regions, destination managers for both the federally managed facilities within the region and their corresponding gateway com- munities can improve marketing and management decisions. Spe- ci ﬁ cally, understanding existing customers more fully and targeting new prospective markets more precisely are direct bene ﬁ ts. These bene ﬁ ts can be particularly powerful for gateway communities that desire enterprise growth but also need to maintain a balance between marketing efforts and desired visitor experiences. As demand market data for national park gateway community desti- nations is often hard to assemble, we see this study as presenting an approach for characterizing the visiting populations to any gateway community. Although we present analysis and interpretations for only three selected national park regions, managers of other national parks and their respective gateway communities can utilize this approach to become smarter destinations.
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In 2004 the NCSU Libraries and the NC Center for Geographic Information & Analysis entered into an agreement with the Library of Congress to pursue preservation of state and local digital geospatial data as part of the National Digital Information Infrastructure and Preservation Program (NDIIPP). The North Carolina Geospatial Data Archiving Project (NCGDAP) will help inform development of a national digital preservation infrastructure through a “learning by doing” approach focused on identifying, acquiring, and preserving content within the context of the NC OneMap initiative and its framework of partnerships with state, local, and federal agencies. As a component of the National Map, NC OneMap provides an opportunity to engage content through traditional distribution channels as well as through emerging web services based modes of access.
Environmental management is inherently a spatial endeavor. Its data are particularly complex as they require two descriptors; namely, precise location of what is being described, as well as a clear description of its physical characteristics [Joseph, 1999]. The web GIS is an extension and application of client/server computing, where the geospatial data is accessible in a shareable envi- ronment [Harish et al, 2007]. Through geo-tools we aim to define statistically that part of popula- tion which is mostly exposed to any pollution level (air, water, soil, etc) based on a specific geograph -
varies from 175.12 to 437.02 ppm (Fig. 5). The highest value was determined in the North east part of the study area. According to Hem, (1985), carbon dioxide species that harvest alkalinity in the surface or groundwater, owing to respiration by plants and oxidation of organic matter which makes enriched in carbon dioxide trapped in the unsaturated-zone. Basalt rock in the Aland taluka shows numerous fracture zones which may support the above statement through which carbon dioxide enriched and dissolved in the groundwater leads the higher concentration of alkalinity. The map shows geospatial distribution of iron concentration in groundwater samples from Aland taluka iron concentration varies from 0 to 0.49722 ppm (Fig. 6).
Geospatial has been widely and extensively used as a research tool across the human activity spectrum. Education sector is no exemption with geospatial being taught in all education institutions, secondary or tertiary. In geography education, tourism courses are among courses that employ geospatial in their teaching and learning material to define the data collection and associate the data with technology which has geographic and locational component. Coastal ecotourism, for example, utilize geospatial in its management where geographic information can be stored in layers and integrated with geographic software program. The information can then be created, stored, manipulated, analyzed and visualized. More interestingly, the result of the spatial information can be integrate with various other research discipline. This paper reviews: 1) geospatial as one of the tools used in geography teaching material; 2) the application of geospatial in coastal ecotourism management; and 3) geospatial based coastal ecotourism management for geography education. A review from geospatial based coastal ecotourism management for geography teaching material development was established. Hence, its effectiveness and efficiency is also discussed.