東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Emerging Computing Trends, Web GIS T...

Matney, Jason Andrew.

Linked to FindBook

Google Book

Amazon

博客來

Emerging Computing Trends, Web GIS Tools, and Forecasting Methods for Geospatial Environmental Decision Support in Service of Complex Land Management Challenges.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Emerging Computing Trends, Web GIS Tools, and Forecasting Methods for Geospatial Environmental Decision Support in Service of Complex Land Management Challenges./
Author:	Matney, Jason Andrew.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	137 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-03, Section: A.
Contained By:	Dissertations Abstracts International81-03A.
Subject:	Recreation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27529099
ISBN:	9781088357583

Emerging Computing Trends, Web GIS Tools, and Forecasting Methods for Geospatial Environmental Decision Support in Service of Complex Land Management Challenges.
Matney, Jason Andrew.

Emerging Computing Trends, Web GIS Tools, and Forecasting Methods for Geospatial Environmental Decision Support in Service of Complex Land Management Challenges. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 137 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2019.

This item must not be sold to any third party vendors.

As global ecological concerns associated with climate change grow increasingly salient, professional conservationists, land managers, and public and private decision makers face increasingly complex responsibilities related to these changes. Tasks such as monitoring large-scale environmental change, responding to more frequent natural disasters, and leveraging decision support tools for managing geospatial information are becoming more important in the careers of land managers. This dissertation is comprised of three manuscripts which explore the present and future of web mapping, the application of a web-based geospatial management solution, and a comparison of time series forecasting methods. The overarching rationale is to provide decision support to land managers to assist them in addressing these grand challenges. The dissertation therefore seeks to contribute to the applied GIScience literature from a land management perspective. The first manuscript explores the state of the current era of web mapping as of 2019. The publication is in preparation for submission to Transactions of the Institute of British Geographers. The text explores how six technologies / configurations / implementations are influencing the trajectory of web mapping applications for land management since 2017. Rather than providing results in the manner of a traditional research paper, the manuscript instead (1) historically situates these 2017-2019 web mapping developments, (2) serves as a broad literature review of the subject, and (3) speculates on future directions into the early 2020s. The second manuscript uses the case study of the US National Park Service's Rivers, Trails, and Conservation Assistance program as an example of how a standardized geospatial tool can be deployed within a land management decision support context. This manuscript was published in the Journal of Park and Recreation Administration in 2019. The text elucidates the background, implementation, and evaluation of a web mapping tool as developed and deployed for use by staff based in disparate US National Park Service offices. Results show that while many users found that the online tool helped to improve their access to and management of project data, the tool on the whole required migration to a cloud-based platform (e.g., ArcGIS Online) to resolve serious technical errors. The third manuscript considers the capacity different of time series forecasting methods to provide insight and predictions into US National Park Service visitation and geotagged social media post temporal trends. The manuscript is in preparation for submission to the Journal of Environmental Management. Three forecasting methods are compared to each other (two traditionally statistical, one based on artificial intelligence and machine learning concepts) in order to determine which is most effective based on several performance metrics. Forecasting is described for both social media posts drawn from a web-scraped Flickr dataset, as well as US National Park Service recreation visitation for the 12 most-visited parks from 2007 to 2017 (of those parks located within the contiguous US, except those located in heavily populated areas). The Flickr posts are configured as Photo User Days, and only those posts geotagged within the polygonal boundaries of these parks are considered in the analysis. Results include the recommendation that land managers utilize Facebook Prophet for time series visitation forecasting tasks, based on its relative ease of use and comparable performance to more traditional statistical forecasting methods.

ISBN: 9781088357583Subjects--Topical Terms:

535376
Recreation.
Subjects--Index Terms:

Web mapping

Emerging Computing Trends, Web GIS Tools, and Forecasting Methods for Geospatial Environmental Decision Support in Service of Complex Land Management Challenges.
LDR:04954nmm a2200385 4500 001 2269866
005 20200921070347.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781088357583
035 $a (MiAaPQ)AAI27529099
035 $a (MiAaPQ)NCState_Univ18402036765
035 $a AAI27529099
040 $a MiAaPQ $c MiAaPQ
100 1 $a Matney, Jason Andrew. $3 3547218
245 1 0 $a Emerging Computing Trends, Web GIS Tools, and Forecasting Methods for Geospatial Environmental Decision Support in Service of Complex Land Management Challenges.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 137 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-03, Section: A.
500 $a Advisor: Slocumb, William;Smith, Jordan;McCarter, James;Supak, Stacy;Nelson, Stacy Arnold.
502 $a Thesis (Ph.D.)--North Carolina State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a As global ecological concerns associated with climate change grow increasingly salient, professional conservationists, land managers, and public and private decision makers face increasingly complex responsibilities related to these changes. Tasks such as monitoring large-scale environmental change, responding to more frequent natural disasters, and leveraging decision support tools for managing geospatial information are becoming more important in the careers of land managers. This dissertation is comprised of three manuscripts which explore the present and future of web mapping, the application of a web-based geospatial management solution, and a comparison of time series forecasting methods. The overarching rationale is to provide decision support to land managers to assist them in addressing these grand challenges. The dissertation therefore seeks to contribute to the applied GIScience literature from a land management perspective. The first manuscript explores the state of the current era of web mapping as of 2019. The publication is in preparation for submission to Transactions of the Institute of British Geographers. The text explores how six technologies / configurations / implementations are influencing the trajectory of web mapping applications for land management since 2017. Rather than providing results in the manner of a traditional research paper, the manuscript instead (1) historically situates these 2017-2019 web mapping developments, (2) serves as a broad literature review of the subject, and (3) speculates on future directions into the early 2020s. The second manuscript uses the case study of the US National Park Service's Rivers, Trails, and Conservation Assistance program as an example of how a standardized geospatial tool can be deployed within a land management decision support context. This manuscript was published in the Journal of Park and Recreation Administration in 2019. The text elucidates the background, implementation, and evaluation of a web mapping tool as developed and deployed for use by staff based in disparate US National Park Service offices. Results show that while many users found that the online tool helped to improve their access to and management of project data, the tool on the whole required migration to a cloud-based platform (e.g., ArcGIS Online) to resolve serious technical errors. The third manuscript considers the capacity different of time series forecasting methods to provide insight and predictions into US National Park Service visitation and geotagged social media post temporal trends. The manuscript is in preparation for submission to the Journal of Environmental Management. Three forecasting methods are compared to each other (two traditionally statistical, one based on artificial intelligence and machine learning concepts) in order to determine which is most effective based on several performance metrics. Forecasting is described for both social media posts drawn from a web-scraped Flickr dataset, as well as US National Park Service recreation visitation for the 12 most-visited parks from 2007 to 2017 (of those parks located within the contiguous US, except those located in heavily populated areas). The Flickr posts are configured as Photo User Days, and only those posts geotagged within the polygonal boundaries of these parks are considered in the analysis. Results include the recommendation that land managers utilize Facebook Prophet for time series visitation forecasting tasks, based on its relative ease of use and comparable performance to more traditional statistical forecasting methods.
590 $a School code: 0155.
650 4 $a Recreation. $3 535376
650 4 $a Natural resource management. $3 589570
650 4 $a Remote sensing. $3 535394
650 4 $a Geographic information science. $3 3432445
650 4 $a Environmental management. $3 535182
650 4 $a Climate change. $2 bicssc $3 2079509
653 $a Web mapping
653 $a National park management
690 $a 0404
690 $a 0799
690 $a 0370
690 $a 0814
690 $a 0528
690 $a 0474
710 2 $a North Carolina State University. $3 1018772
773 0 $t Dissertations Abstracts International $g 81-03A.
790 $a 0155
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27529099