scholarly journals Detecting temporal change in land-surface altitude using robotic land-surveying techniques and geographic information system applications at an earthen dam site in Southern Westchester County, New York

2017 ◽  
Author(s):  
Michael L. Noll ◽  
Anthony Chu
Keyword(s):  
New York ◽  
Information System ◽  
Geographic Information System ◽  
Land Surface ◽  
Temporal Change ◽  
Geographic Information ◽  
Earthen Dam ◽  
Land Surveying ◽  
Dam Site ◽  
Westchester County

scholarly journals Slug-test analysis of selected wells at an earthen dam site in southern Westchester County, New York

2019 ◽  
Author(s):  
Michael L. Noll ◽  
Anthony Chu ◽  
William Capurso
Keyword(s):  
New York ◽  
Earthen Dam ◽  
Test Analysis ◽  
Slug Test ◽  
Dam Site ◽  
Westchester County

Geographic Information System-Based Digital Catalog for Managing Subsurface Geotechnical and Geologic Data

2003 ◽  
Vol 1821 (1) ◽  
pp. 90-96
Author(s):  
Anne I. Veeger ◽  
Daniel P. Murray ◽  
O. Don Hermes ◽  
Jon C. Boothroyd ◽  
Nasir Hamidzada
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Rhode Island ◽  
Relational Database ◽  
Land Surface ◽  
Geographic Information ◽  
Transportation Systems ◽  
Borehole Data ◽  
Organic Sediment ◽  
Geologic Data

Knowledge of surface and subsurface geology and geotechnical properties is fundamental to planning, developing, and modernizing transportation systems. Through dynamic coupling of readily available areal geographic information system coverages and subsurface borehole data stored in a relational database, a spatially referenced digital catalog of borehole data was created for two pilot areas in Rhode Island. The borehole database was populated with data derived from Rhode Island Department of Transportation geotechnical reports and supplemental data from the U.S. Geological Survey groundwater site inventory system and local storm water and sewer projects. Most of these data were previously maintained in paper format, making historical or interproject data comparisons virtually impossible. Unification of these data in a single relational database yields two primary benefits: ( a) historical data are readily accessible for review and therefore can be incorporated easily into the planning stages of new projects and ( b) sophisticated analysis of the region becomes possible with access to data from multiple projects with both spatial and temporal coverage. Geologic data include bedrock geology, surface outcrops, unconsolidated materials, soil type, topographic and orthophotographic base maps, and location of boreholes and wells. Subsurface data include land surface elevation, depth to water table, depth to bedrock, presence of fill, high and low blow-count zones, and organic sediment. The digital catalog is distributed on a CD-ROM that includes ArcView project files and an Access relational database. The borehole data are also accessible through the Internet, with retrieval access for all users and data entry privileges for registered users.

scholarly journals Groundwater-level data from an earthen dam site in southern Westchester County, New York

Data Series ◽  
2018 ◽  
Author(s):  
Michael L. Noll ◽  
Anthony Chu
Keyword(s):  
New York ◽  
Groundwater Level ◽  
Earthen Dam ◽  
Level Data ◽  
Dam Site ◽  
Westchester County

Using Geographic Information System Technology To Synthesize Census Areawide and Linear Highway Data for Location of Weigh-in-Motion Sites

1997 ◽  
Vol 1570 (1) ◽  
pp. 172-180
Author(s):  
Nathan S. Erlbaum ◽  
Thomas M. Vaughan
Keyword(s):  
New York ◽  
Information System ◽  
Geographic Information System ◽  
New York State ◽  
Geographic Information ◽  
Vehicle Classification ◽  
York State Department ◽  
Gis Technology ◽  
Weigh In Motion ◽  
Information System Technology

A process is described that was developed by the New York State Department of Transportation to identify potential areas around the state for location of new weigh-in-motion sites. The procedure uses geographic information system (GIS) technology to synthesize census areawide travel and business data with linear highway system traffic and vehicle classification data from the Department’s computer inventories. The conclusion is that GIS is a powerful tool for synthesizing and displaying diverse data sets for use in solving engineering problems.

scholarly journals Models for Predicting Bulinids Species Habitats in Southwestern Nigeria Using Geographic Information System

2020 ◽  
Author(s):  
Opeyemi G. Oso ◽  
Alex Odaibo
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Land Surface ◽  
Environmental Variables ◽  
Regression Models ◽  
Geographic Information ◽  
Snail Species ◽  
Base Line ◽  
Southwestern Nigeria ◽  
Predictive Risk

Abstract Background: Schistosomiasis prevalence is high in southwestern Nigeria and planorbids of the genus Bulinus had been implicated in the transmission of the disease in the area. The knowledge of species distribution in relation to environmental variables will be auspicious in planning control strategies. Methods: Satellite imagery and geographic information system (GIS) were used to develop models for predicting the habitats suitable for bulinid species. Monthly snail sample collection was done in twenty-three randomly selected water contact sites using standard method for a period of two years. Remotely sensed variables such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) were extracted from Landsat TM, ETM+; Slope and Elevation were obtained from digital elevation model (DEM) while Rainfall was retrieved from European Meteorology Research Program. These environmental factors and snail species were integrated into GIS to predict the potential habitats of different bulinid species using exploratory regression models. Results: The following environmental variables: flat-moderate slope (0.01-15.83), LST (21.1oC-23.4oC), NDVI (0.19-0.52), spatial rainfall (> 1,569.34 mm) and elevation (1-278 meters) all contributed to the model used in predicting habitat suitable for bulinids snail intermediate hosts. Exploratory regression models showed that LST, NDVI and slope were predictors of B. globosus and B. jousseaumei; elevation, LST, Rainfall and slope were predictors of B. camerunensis; spatial rainfall, NDVI and slope were predictors of B. senegalensis while NDVI and slope were predictors of B. forskalii in the area. Bulinids in the forskalii group showed clustering in middle belt and south. The predictive risk map of B. jousseaumei was similar to the pattern described for B. globosus, but with a high R-square value of 81%. Conclusion: The predictive risk models of bulinid species in this study provided a robust output for the study area which could be used as base-line for other areas in that ecological zone. It will be useful in appropriate allocation of scares resources in the control of schistosomiasis in that environment.

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