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Towards a Better Understanding of Hu...

Cleland, Zachary W.

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Towards a Better Understanding of Human Caused Wildfire in Colorado with Spatial Data Mining.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards a Better Understanding of Human Caused Wildfire in Colorado with Spatial Data Mining./
Author:	Cleland, Zachary W.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	131 p.
Notes:	Source: Masters Abstracts International, Volume: 81-10.
Contained By:	Masters Abstracts International81-10.
Subject:	Forestry. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27836259
ISBN:	9798641785349

Towards a Better Understanding of Human Caused Wildfire in Colorado with Spatial Data Mining.
Cleland, Zachary W.

Towards a Better Understanding of Human Caused Wildfire in Colorado with Spatial Data Mining. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 131 p.

Source: Masters Abstracts International, Volume: 81-10.

Thesis (M.A.)--University of Colorado Colorado Springs, 2020.

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

This study investigated the circumstances of human caused wildfire from 1992-2015 within Colorado's Forested Mountain and North American Desert eco-regions using spatial data mining. This process successfully extracted the associations amongst human caused wildfire variables by utilizing the Apriori data mining algorithm while allowing various spatial predicates to present complex iterative behaviors of variables across space. This research also examined changes in the spatial associations between three time periods, 1992-1999, 2000-2007, and 2008-2015. The findings were compared against existing wildfire literature. The data mining methodology extracted frequent itemset rules from Colorado wildfire data to evaluate the state's patterns against previous wildfire occurrence research. This study suggests new knowledge regarding human caused wildfire and the variable of population spillover effects. The results indicated that as human population centers increase in population size the spillover effects further influence the spatial association patterns of human caused wildfire. Other variable results proved consistent with existing literature. The evidence also showed low population density and low housing unit density as the most dominant spatial association features with in the study. A novel mapping methodology rendered the extracted frequent itemset rules in a manner that weighted their consistency over time. The information developed here can potentially be used for informing human caused wildfire mitigation, risk analysis, and future human caused wildfire modeling studies within Colorado.

ISBN: 9798641785349Subjects--Topical Terms:

895157
Forestry.
Subjects--Index Terms:

Data mining

Towards a Better Understanding of Human Caused Wildfire in Colorado with Spatial Data Mining.
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245 1 0 $a Towards a Better Understanding of Human Caused Wildfire in Colorado with Spatial Data Mining.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 131 p.
500 $a Source: Masters Abstracts International, Volume: 81-10.
500 $a Includes supplementary digital materials.
500 $a Advisor: Dao, Diep T.
502 $a Thesis (M.A.)--University of Colorado Colorado Springs, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This study investigated the circumstances of human caused wildfire from 1992-2015 within Colorado's Forested Mountain and North American Desert eco-regions using spatial data mining. This process successfully extracted the associations amongst human caused wildfire variables by utilizing the Apriori data mining algorithm while allowing various spatial predicates to present complex iterative behaviors of variables across space. This research also examined changes in the spatial associations between three time periods, 1992-1999, 2000-2007, and 2008-2015. The findings were compared against existing wildfire literature. The data mining methodology extracted frequent itemset rules from Colorado wildfire data to evaluate the state's patterns against previous wildfire occurrence research. This study suggests new knowledge regarding human caused wildfire and the variable of population spillover effects. The results indicated that as human population centers increase in population size the spillover effects further influence the spatial association patterns of human caused wildfire. Other variable results proved consistent with existing literature. The evidence also showed low population density and low housing unit density as the most dominant spatial association features with in the study. A novel mapping methodology rendered the extracted frequent itemset rules in a manner that weighted their consistency over time. The information developed here can potentially be used for informing human caused wildfire mitigation, risk analysis, and future human caused wildfire modeling studies within Colorado.
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653 $a GIScience
653 $a Wildfire
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710 2 $a University of Colorado Colorado Springs. $b College of Letters, Arts, and Sciences-Applied Geography. $3 3541938
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793 $a English
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