Spatial Data Exploration via Multiple Regression

2012 ◽  
pp. 277-324
Keyword(s):  
Multiple Regression ◽  
Spatial Data ◽  
Data Exploration

Cognitive Aspects on the Representation of Dynamic Environmental Phenomena Using Animations

2001 ◽  
pp. 13-24 ◽  
Author(s):  
Patrick J. Ogao ◽  
Connie A. Blok
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Problem Solving ◽  
Spatial Data ◽  
Scientific Data ◽  
Data Availability ◽  
Data Exploration ◽  
Spatial Data Analysis ◽  
Wide Range ◽  
Interdisciplinary Nature

Measurements from dynamic environmental phenomena have resulted in the acquisition and generation of an enormous amount of data. This upsurge in data availability can be attributed to the interdisciplinary nature of environmental problem solving and the wide range of acquisition technology involved. In essence, users are dealing with data that is complex in nature, multidimensional and probably of a temporal nature. Also, the frequency by which this data is acquired far exceeds the rate at which it is being explored, a factor that has accelerated the search for innovative approaches and tools in spatial data analysis. These attempts have seen both analytical and visual techniques being used as aids in presentation and scientific data exploration. Examples are seen in techniques as in: data mining, data exploration and visualization.

The 2nd ACM SIGSPATIAL International Workshop on Geospatial Data Access and Processing APIs (SpatialAPI 2020)

2021 ◽  
Vol 12 (3) ◽  
pp. 20-22
Author(s):  
Ahmed Eldawy ◽  
Gobe Hobona
Keyword(s):  
Spatial Data ◽  
International Workshop ◽  
Data Access ◽  
Database Systems ◽  
Cutting Edge ◽  
Geospatial Data ◽  
Data Exploration

With the increasing amount of publicly available geospatial data, the demand on spatial data exploration and analysis kept growing. The SIGSPATIAL community is both a provider of new systems with cutting-edge technology on accessing and processing geospatial data, and a user for all these systems. The SpatialAPI workshop is designed to help the SIGSPATIAL community by growing the knowledge of the existing well-established systems that are available for accessing and processing geospatial data. This includes, but is not limited to, web APIs, programming libraries, database systems, and geospatial extensions to existing systems.

scholarly journals Rate Of Land Cover Change And Its Determinants In The Soudano-Guinea Transition Zone Of Benin Republic

1970 ◽  
Vol 10 (5) ◽  
pp. 691-704
Author(s):  
Joseph Oloukoi
Keyword(s):  
Land Cover ◽  
Multiple Regression ◽  
Transition Zone ◽  
Land Cover Change ◽  
Spatial Data ◽  
Multiple Regression Model ◽  
Rate Of Change ◽  
Change Rate ◽  
Land Cover Dynamics ◽  
Benin Republic

The paper assesses land cover dynamics and its associated drivers in the soudano-guinea transition zone of Benin Republic, using both spatial and non-spatial data. Multispectral and multi temporal Landsat imageries (Landsat TM of 1986, ETM+ of 2000 and OLI-TIRS of 2013) were used for the analysis of land cover dynamics through a supervised classification. Logistic Multiple Regression model is used to analyse the relationship between the rate of land cover change and nine explanatory variables considered as potential factors of land cover change rate. Land cover change rate was estimated to an average of -4.34% annually. This rate appears higher than the national rate estimated at -1.2% by Food and Agriculture Organization (FAO). The rate of change classified into four modalities ranging from -8.77 to -1.13, show a gradual decrease towards the northern part of the study area. From the R2, it is observed that the nine variables explained about 35.30% of the occurrence of land cover change. Variables such as ‘rank of agriculture in the source of income’ and ‘accessibility’ are significantly influencing the rate of land cover change in the study area.  The paper further advocates for the need to dwell more on indirect factors which remotely influence environmental dynamics. Key Words: Land cover, Rate of change, Benin, Logistic multiple regression, Determinants

scholarly journals The analysis of relationships between annual precipitation and relief and landscape characteristics in the Carpathian region of Ukraine

2015 ◽  
pp. 224-229
Author(s):  
Oleksandr Mkrtchian ◽  
Pavlo Shuber
Keyword(s):  
Spatial Distribution ◽  
Multiple Regression ◽  
Spatial Data ◽  
Annual Precipitation ◽  
P Value ◽  
Landscape Characteristics ◽  
Carpathian Region ◽  
Spatially Distributed ◽  
Terrain Roughness ◽  
The Impact

The paper deals with the statistical analysis of relationships between the spatial distribution of precipitation values in the Carpathian region of Ukraine and the spatially distributed relief and landscape parameters. Processed data of 20 weather stations have been a data source of annual precipitation data for 1961–1991 period, while SRTM elevation dataset has been used as a source of spatial data on relief parameters. Step-wise multiple regression has revealed the set of parameters manifesting the strongest relationship with the precipitation distribution. This set includes following parameters: terrain roughness, local and focal elevation, and aspect factor for NW/SE direction; the overall relationship is highly statistically significant. The terrain roughness has appeared to be the single parameter with the strongest effect on precipitation values, followed by the local and focal elevation and the aspect factor. ANOVA results were much more modest in comparison with the multiple regression, suggesting that the quantitative spatial modeling, which uses relief parameters as predictors, produces much more reliable predictions of the precipitation spatial distribution than just averaging the precipitation values round the delineated natural regions. ANCOVA results show that the interaction between the quantitative and numerical predictors is statistically significant with the p-value of less than 0.01, suggesting that belonging to natural regions can moderate the impact of quantitative relief parameters. Thus considering the belonging to natural regions significantly improves the final prediction, when used in addition to numerical relief parameters. Key words: annual precipitation, climatic mapping, multiple regression, ANOVA, AVCOVA.

Multi-Touch Querying on Data Physicalizations in Immersive AR

2021 ◽  
Vol 5 (ISS) ◽  
pp. 1-20
Author(s):  
Bridger Herman ◽  
Maxwell Omdal ◽  
Stephanie Zeller ◽  
Clara A. Richter ◽  
Francesca Samsel ◽  
...  
Keyword(s):  
Spatial Data ◽  
Data Exploration ◽  
Climate Simulation ◽  
Terrain Models ◽  
3D Data ◽  
Data Querying ◽  
3D Printers ◽  
Abstract Data ◽  
3D Printed ◽  
Quantitative Performance

Data physicalizations (3D printed terrain models, anatomical scans, or even abstract data) can naturally engage both the visual and haptic senses in ways that are difficult or impossible to do with traditional planar touch screens and even immersive digital displays. Yet, the rigid 3D physicalizations produced with today's most common 3D printers are fundamentally limited for data exploration and querying tasks that require dynamic input (e.g., touch sensing) and output (e.g., animation), functions that are easily handled with digital displays. We introduce a novel style of hybrid virtual + physical visualization designed specifically to support interactive data exploration tasks. Working toward a "best of both worlds" solution, our approach fuses immersive AR, physical 3D data printouts, and touch sensing through the physicalization. We demonstrate that this solution can support three of the most common spatial data querying interactions used in scientific visualization (streamline seeding, dynamic cutting places, and world-in-miniature visualization). Finally, we present quantitative performance data and describe a first application to exploratory visualization of an actively studied supercomputer climate simulation data with feedback from domain scientists.

A user-centered design for a spatial data warehouse for data exploration in environmental research

2008 ◽  
Vol 3 (4-5) ◽  
pp. 273-285 ◽  
Author(s):  
Michael McGuire ◽  
Aryya Gangopadhyay ◽  
Anita Komlodi ◽  
Christopher Swan
Keyword(s):  
Data Warehouse ◽  
Spatial Data ◽  
Data Exploration ◽  
Environmental Research ◽  
User Centered Design ◽  
Spatial Data Warehouse ◽  
User Centered
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