scholarly journals Integrating a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU<sup>&reg;</sup> Mobile Field Data Collection System Using Differentially Corrected Global Positioning System Technology and a Real-Time Bidirectional Actionable Platform within an ArcGIS Cyberenvironment for Implementing Mosquito Control

2014 ◽  
Vol 03 (03) ◽  
pp. 141-196 ◽  
Author(s):  
Benjamin G. Jacob ◽  
Robert J. Novak
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Global Positioning System ◽  
Field Data ◽  
Mosquito Control ◽  
Positioning System ◽  
System Technology ◽  
Collection System ◽  
Global Positioning ◽  
Field Data Collection

scholarly journals APPLICATION OF UNDERWATER PHOTO TRANSECT METHOD TO UNDERSTAND CORAL REEFS COVER IN POMBO ISLAND, MALUKU

2017 ◽  
Vol 8 (2) ◽  
pp. 633
Author(s):  
Arif Seno Adji ◽  
Terry Indrabudi ◽  
Robert Alik
Keyword(s):  
Data Collection ◽  
Coral Reefs ◽  
Global Positioning System ◽  
Benthic Community ◽  
Field Data ◽  
Spatial Information ◽  
Positioning System ◽  
Gps Receiver ◽  
Global Positioning ◽  
Field Data Collection

<em>Methods to understand coral reefs health are increasingly diverse. One of the promising method is georeferenced photo transect. In this method, a diver took benthic community photos using a camera installed or connected with a Global Positioning System (GPS) receiver, so that each photo taken has a geographic coordinate on it. This spatial information is very useful for monitoring coral reefs health. The purpose of this research is to understand coral reefs cover in Pombo Island, Maluku by using georeferenced photo transect method. Field data collection was done in April 2015 with 14 transect lines. Overall of 14 transect lines, 3 transects are in Good category, other 3 transects are in Pretty Good category, and the rest of 8 transects are in Not Good category. </em>C<em>oral species commonly found around transect lines are <span style="text-decoration: underline;">Porites</span> <span style="text-decoration: underline;">lutea</span>, <span style="text-decoration: underline;">Porites</span> <span style="text-decoration: underline;">lobata</span>, <span style="text-decoration: underline;">Acropora</span> <span style="text-decoration: underline;">valenciennesi</span>, <span style="text-decoration: underline;">Acropora</span> <span style="text-decoration: underline;">hyacinthus</span>, <span style="text-decoration: underline;">Turbinaria</span> <span style="text-decoration: underline;">mesenterina</span>, <span style="text-decoration: underline;">Stylophora</span> <span style="text-decoration: underline;">pistillata</span>, <span style="text-decoration: underline;">Mycedium</span> <span style="text-decoration: underline;">elephantotus</span>, <span style="text-decoration: underline;">Acropora</span> <span style="text-decoration: underline;">latistella</span></em>

Evaluation of CORSIM Car-Following Model by Using Global Positioning System Field Data

2000 ◽  
Vol 1710 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Sastry Chundury ◽  
Brian Wolshon
Keyword(s):  
Data Collection ◽  
Global Positioning System ◽  
Field Data ◽  
Goodness Of Fit ◽  
Positioning System ◽  
Goodness Of Fit Test ◽  
Distance Information ◽  
Car Following ◽  
Wide Range ◽  
Global Positioning

It has been recognized that CORSIM (and its constituent program, NETSIM) is one of the most widely used and effective computer programs for the simulation of traffic behavior on urban transportation networks. Its popularity is due in large part to the high level of detail incorporated into its modeling routines. However, the car-following models, used for the simulation of driver behavior in the program, have not been formally calibrated or validated. Since the model has performed well in a wide range of applications for so many years, it has always been assumed to have an implied validity. This study evaluated the NETSIM car-following models by comparing their results with field data. Car-following field data were collected using a new data collection system that incorporates new Global Positioning System and geographic information system technologies to improve the accuracy, ease, speed, and cost-effectiveness of car-following data collection activities. First, vehicle position and speed characteristics were collected under field conditions. Then simulated speeds and distances were based on identical lead vehicle actions using NETSIM car-following equations. Comparisons of simulated and field data were completed using both graphical and statistical methods. Although some differences were evident in the graphical comparisons, the graphs overall indicated a reasonable match between the field and simulated vehicle movements. Three statistical tests, including a goodness-of-fit test, appear to support these subjective conclusions. However, it was also found that definitive statistical conclusions were difficult to draw since no single test was able to compare the sets of speed and distance information on a truly impartial basis.

The Distribution of Hospitality Services in Uyo Urban, Nigeria

2018 ◽  
pp. 137-150
Author(s):  
Violet Bassey Eneyo
Keyword(s):  
Data Collection ◽  
Global Positioning System ◽  
Central Area ◽  
Positioning System ◽  
Nearest Neighbour ◽  
Global Positioning ◽  
Neighbour Analysis

This paper examines the distribution of hospitality services in Uyo Urban, Nigeria. GIS method was the primary tool used for data collection. A global positioning system (GPS) Garmin 60 model was used in tracking the location of 102 hospitality services in the study area. One hypothesis was stated and tested using the nearest neighbour analysis. The finding shows evidence of clustering of the various hospitality services. The tested hypothesis further indicated that hospitality services clustered in areas that guarantee a sustainable level of patronage to maximize profit. Thus, the hospitality services clustered in selected streets in the metropolis while limited numbers were found outside the city’s central area.

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