ARCTSET:Arctic, Remote and Cold Territories

Social and Environmental TrendsResearch Group

@University of Northern Iowa (UNI)

Iowa’s Arctic research dimension

This study aims to conduct an exploratory spatiotemporal analysis to reveal spatial patterns and temporal fluctuations of wildfire events in the Arctic. Tundra wildfires have an important impact on arctic ecosystems as they can substantially alter the amount of biomass and animal abundance in affected areas. The knowledge base about tundra wildfires is limited. Most fires take place in remote areas and remain unmonitored from the ground or air. This study uses MODIS-derived active fire data to analyze spatial and temporal patterns of tundra wildfires between 2004 and 2008. The dataset incorporates locations of active fire events and estimates of fire radiated power (FRP).In 2004-2008 MODIS sensors registered over 3,000 fire events. The largest number of fire events was recorded in 2005. The wildfires exhibit clear seasonality determined by seasonal changes in tundra landscapes with most fires occurring in July and August. We observed inter-year fluctuations when a fire season either started earlier (in June) or lasted longer (in to September). The wildfires demonstrate a tendency to cluster, although year-to-year locations of clusters vary. Wildfires concentrated in Alaska and in Northwestern and Northeastern Russia. This is also true for the intensity of fires: in the five-year period the FRP values in some areas exhibited considerable spatial autocorrelation.To analyze possible factors that determine spatiotemporal variation of arctic wildfires occurrence and intensity, we analyzed fire events in respect to geographic location (latitude/longitude), climate parameters, vegetation types and proximity to points of human-caused disturbance (settlements, roads, pipelines, oil wells, etc.). The results clearly indicate the relationship between vegetation types and occurrences and intensity of wildfires: areas with larger amounts of combustible biomass and longer warm periods having a greater number and more intensive fires. We were unable to detect a clear relationship between wildfire locations and elements of anthropogenic disturbance.

Identify spatial (distribution and clustering) and temporal patterns (seasonal and multiyear) of arctic wildfire events and their intensity characteristics Analyze relationships between wildfire occurrence and intensity and environmental conditions

Wildfire data [FIRMS]:Fire events: detected fire occurrences (confidence >= 50)Fire Radiative Power (FRP): a measure of radiant heat output of detected fires in MegaWatts (MW) derived from MODIS Data Processing System (MODAPS) Collection 5.1 Active Fire Products

Other data:oClimate variables [U Delaware]oVegetation and bioclimatic variables [CAVM]oHuman-caused disturbance

Exploratory Analysis:oTime series analysis: character and fluctuations in seasonality and multiyear patternsoAnalysis of spatial distribution : evidence of spatial clustering (NN, Ripley’s K), spatial autocorrelation and fire hot-spots (Moran’s I , Geary’s C, LISA)Explanatory Analysis: (correlation analysis using wildfire environmental factors/drivers and grid and zonal aggregates of fire occurrences and intensity) oSpatial correspondence with temperature and precipitationoSpatial correspondence with vegetation types and biomassoSpatial correspondence with human-induced disturbances

Spatiotemporal characteristics of wildfires: Seasonality with most fires occurring in July and August. In some years a fire season either started earlier (in June) or lasted longer (in to September). Multiyear geographical consistency: concentrated in Alaska, NE and NW Russia. More intensive and frequent fires are concentrated in Alaska and NE Russia.Clustering and autocorrelation in intensity characteristics: tend to form localized isolated clusters, often of high intensity.

Relationships with environmental factors:Both fire occurrences and intensity are positively associated with temperatures in spring, and intensity with temperatures in May and August. More intensive fires correspond to higher temperatures in August and September.Fire intensity declines with higher precipitation in August, September and winter.Areas with more combustible biomass have a greater number and more intensive wildfires.No clear relationship between wildfire locations and anthropogenic disturbance.

Research Objectives

Summary

Arctic Wildfires and Clime Change: A Spatial Longitudinal Analysis Using MODIS Data

Total Fires By Year And Month Average FRP by Year and Month

Number of Fires by Region

Results:ARCTIC FIRES SPATIAL AND SEASONAL DISTRIBUTION

CORRESPONDENCE TO ENVIRONMENTAL CONDITIONS

Methodology

Data Clustering: Ripley’s K Function

Hot Spot Analysis LISA Analysis

SPATIAL CLUSTERING, HOT-SPOTS

Correspondence with climatic parameters

MEASURE STATISTICS OBSERVATIONSAverage Nearest Neighbor [Clark-Evans

Index]R = 0.05;

z = -105.6 st dvClustered; significant

Global Moran’s I (FRP)I = 0.13;

z = -105.2 st dv;Threshold Distance = 100 km

Positive autocorrelation significant

Creative Arctic: Understanding the Role of Creative Capital in the Circumpolar North

Andrey N. Petrov, University of Northern Iowa, USA

Creative Arctic: Understanding the Role of Creative Capital in the Circumpolar North

Andrey N. Petrov, University of Northern Iowa, USA

ABSTRACTThe vast majority of studies in economic geography of talent and creativity have focused on large metropolitan areas and core regions. However, I argue that the creative capital is an equally necessary factor (an agent of economic transformation and revitalization) in the northern frontier. This theoretical account serves as the basis for the empirical study into the economic geography of talent and creative capital in the Canadian North. The paper advances the two-ring-four-sector approach to define the creative class structure. It extends the creative capital metrics to measure four ‘sectors’ of the creative class: scientists (“talent”), leaders, entrepreneurs and bohemia. The empirical part of the paper applies the extended creative class metrics at two different scales. The findings for 288 Canadian regions suggest that the geographic distribution of the creative capital is uneven and heavily clustered in major urban centers. However, some frontier regions appear to perform exceptionally well in all rankings. The in-depth analysis of 34 communities in the Canadian North identifies creative clusters in economically, geographically and politically privileged communities that serve as creative ‘hot spots.’ Thus, contrary to the metropolitan bias, these results indicate that northern communities are not ‘hopeless places’ fully deprived of the creative capital. Creative ‘hot spots’ in the Canadian North exist, and could become the centers of regional reinvention, if appropriate policies are introduced in support.

RESEARCH OBJECTIVESRethink and revise the creative capital approach to economic development to make it applicable in the North Develop creative capital metrics appropriate for northern communitiesConduct an exploratory analysis of the creative capital in the Canadian North

METHODOLOGYDevelop and test creative capital metrics

RESULTSTest dependencies between CC, attractiveness and specialization

CONCLUSIONSCreative capital (CC) is pivotal for the economic path-creation in northern regionsMost CC-economic development relationships found in the metropolis are maintained in the NorthThe nature and composition of the creative capital in the North is different, so are its interactions with other forms of capital, particularly social and civic. Direct application of Florida’s scripts to peripheral communities does not appear appropriateThe four-sector approach provides a more complete and sophisticated understanding of the creative capital, It is more accurate for the peripheryOther attractiveness factors must be considered: relation with Aboriginality, role of women in the communityCC structure: evidence of both intergroup clustering and disproportions. The North most seriously lacks the entrepreneurship and leadership components of the CCIn the North there were localized concentrations of the creative class, included Yellowknife, Whitehorse, Iqaluit, Inuvik, Fort Smith, Smithers

Creative class metrics (Characteristics of the creative class) Construct to be measured Traditional measures

Talent Index (TI) is a location quotient of the population over 20 years of age who have a university degree.

Level of formal education of the labour force

Bohemian Index (BI) is a location quotient of the employment in artistic and creative occupations: “Art and Culture” (NOC group F)

Creative class: bohemians

Additional (new) measures Leadership index (LI) is a location quotient of people with leadership and managerial occupations (NOC group A);

Creative class: leaders

Entrepreneurship index (EI) is a location quotient of people with business occupations (NOC group B);

Creative class: entrepreneurs

Applied science index (ASI) is a LQ of people with applied science occupations (NOC group C);

Creative class: scientists

Quality of Human Resources Index (QHRI) is an integral indicator that combines the educational and professional characteristics of population and is defined and a half-sum of the proportions of people with university degree and a creative occupation

A degree to which the region’s labour force has university education and engaged in

creative professional activity Creative Drive Index (CDI) is calculated by multiplying the TI by LI (i.e. by combining talent and leadership).

“Power” of the creative drive in a region

Measures of ‘quality of place’ (characteristics of attractiveness to the creative class)

Traditional measures Mosaic Index (MI) is a location quotient of the total population that is foreign-born. Society’s diversity Visible Minority Ratio (VMR) is a location quotient of visible minorities in total population.

Society’s diversity

Additional (new) measures Common Law Couples Ratio (CLCR) is a location quotient of population in common law relationship.

Society’s tolerance

Feminist Index (FI) is a location quotient of women in managerial (leadership) occupations

Society’s openness, “low barriers of entry”

Aboriginality Index (AI) is a location quotient of people with Aboriginal identity (by the Census definition) in total population.

Presence of Aboriginal population

Resource-dependency Index (RDI) is a location quotient of employment in the occupations unique for the primary sector.

A degree of resource-reliance

Measure of technology sector specialization Tech-Pole Index (TPI) is a location quotient of the employment in North American Industry Classification System (NAICS) high technology sectors (information and cultural industries and professional, scientific and technical services).

Specialization in technology sectors

Components

1 2BI .924LI .858 .269

CDI .802 .535QHRI .779 .598

EI .664 .536TPI .578 .592TI .570 .692

ASI .959

Percent of variance

49.3 34.4

IndicesCDI QHRI TPI TI BI LI EI ASI MI VMR CLCR FI AI RDI

CDI 1.000.950*

*.712*

*

.872**

.835**

.876** .744**.559*

*.370* .411* .227 .692** .523** -.504**

QHRI 1.000.735*

*.890*

*.819*

*.817** .849**

.613**

.302 .328 .293 .622** .568* -.575

TPI 1.000.692*

*.596*

*.597** .679**

.545**

.531**

.379* .069 .429* .145 - .398*

TI 1.000.701*

*.563** .633**

.636**

.356* .369* .180 .360* .256 -.521*

BI 1.000 .743** .644** .297 .202 .209 .338 .597** .527** -.472*LI 1.000 .723** .354* .292 .276 .178 .886** .697** -.432*

EI 1.000.537*

*.204 .340* .315 .485** .551** -.587**

ASI 1.000 .294 .486* .214 .126 .195 -.305

MI 1.000.694*

*-.540** .305 .035 -.132

VMR 1.000 -.189 .192 .157 -.150

CLCR 1.000 .042 .276 -.179

FI 1.000 .667** -.213

AI 1.000 -.227

RDI 1.000

Rank Rankling 1: creative class Rankling 2: creative class ‘pull-factors’

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Yellowknife Whitehorse

Iqaluit Fort Smith Smithers Inuvik

Rouyn-Noranda Rankin Inlet Thunder Bay Slave Lake

Chicoutimi-Jonquiere Hay River Val-d’Or

Prince George Baie-Comeau

Sault Ste Marie Fort McMurray Kirkland Lake

Alma Thompson

Happy Valley-Goose Bay Elliot Lake

Kenora Fort St.John

Timmins Kapuskasing Labrador City Chibougamau

Mackenzie Roberval Flin Flon

Iroquois Falls Port Cartier Greenstone

Whitehorse Yellowknife

Inuvik Elliot Lake

Iqaluit Smithers

Rankin Inlet Thunder Bay Fort Smith

Prince George Fort McMurray

Hay River Sault Ste Marie

Kenora Slave Lake

Kirkland Lake Thompson

Fort St.John Mackenzie

Rouyn-Noranda Kapuskasing Labrador City

Timmins Baie-Comeau Greenstone

Chicoutimi-Jonquiere Val-d’Or Flin Flon Happy Va

Alma Iroquois Falls

Roberval Chibougamau Port Cartier

RESULTS

DIVERSITY

TOLERANCE

OPENNESS

ABORIGINA-LITY

)

,+ creative ‘hot spots’

Aboriginal creative townslarge industrial centerssmaller industrial centers (2 clusters)(

Types of communities

)

,+ creative ‘hot spots’

Aboriginal creative townslarge industrial centerssmaller industrial centers (2 clusters)(

Types of communities

attractiveness attractiveness

Creative capital

Creative capital ranking of northern communities

‘Talent’ in the North

MOREhttp://www.uni.edu/apetrov/creativearctic.html

metrIcs

metrIcs

Coping with ‘ethanol boom’:understanding

agricultural systems’ response in America’s Heartland

Andrey N. Petrov,Matthew Voss & Ramanatan Sugumaran

University of Northern Iowa

This research is NOT funded by NSF Iowa EPSCoR

Iowa NSF EPSCoR

• Harnessing Energy Flows in the Biosphere to Build Sustainable Energy Systems – IA is 1st in ethanol production (26%)– IA is 4th in wind energy generation

• Alternative energy systems• Energy resources • Energy generation and transportation• Pending at NSF

Background

• Much is written on energy, ecological, economic, industrial, climate impacts of increased ethanol production

• Little known on coping strategies/responses in agriculture-based socio-ecological systems in the USA

• Need for a detailed spatially-explicit analysis of ethanol boom implications and responses in America’s heartland.

• Iowa is the main producer of corn-based ethanol

Research Tasks

• Analyze the relationship between ethanol production and land cover change [2006-07]

• Analyze the effects of ethanol on the Conservation Reserve Program lands and other vulnerable ecosystems

• Analyze the effect of ethanol on long term sustainability of agriculture:– Utilization and preservation of land resources– Maintaining crop diversity– Possible impacts on soil fertility and fertilizer use

Working hypotheses• (1) Corn production could have displaced other crops,

possibly causing a decline in crop diversity and redistribution of croplands to areas less suitable for growing crops

• (2) Increases in corn production and acreage have been largely achieved by altering crop rotation patterns with more severe rotation deviations prevalent on most fertile and productive lands.

• (3) Crop production expanded to Conservation Reserve Program lands and other vulnerable and underrepresented ecosystems (such as wetlands and wooded areas).

• (4) Proximity of ethanol plants per se does not cause corn acreage growth in particular areas.

Data

• NASS crop acreage and harvest statistics (2000-08)

• NASS Cropland Data Layer (2000-08)• SSURGO (Soil Survey) soil data; CSR – corn

Suitability Rating• FSA Crop Reserve Program data (CRP lands)

Data pre-processing & Issues

• CDL is based on different imagery - need to resample to make comparable

• Most comparable CDLs: 2005, 2006 and 2007 (same imagery source)

• CDL accuracy 95% for most large crops, but only 50% for some smaller crops

• Buffer ethanol plants (15 and 30 miles); separate models run for 2003, 2004 and 2007

Measuring crop rotation

• Corn-soybeans and corn-corn-soybeans are accepted rotation patterns

• Deviation: corn-corn-corn… (corn at least for three consecutive years)

• 2000-2002 to 2005-2007

Land quality data

Methods

• Land cover change detection (ERDAS, ArcGIS)• “expected” (Markovian probabilities) vs. actual

crop acreage (Idrisi)• Crop rotation estimates• Compare crop allocation, rotation deviations and

quality of land (CSR)• Overlay with CRP lands• Estimate corn increase in proximity and away

from ethanol plants

Results

Increase in corn acreage

-land under corn grew 12.6%,-deviated from “expected” 17% -under soybeans dropped 15%-switch in trend comparedto the 1990s.

Displacement of ‘other’ crops and land cover types; decrease in crop diversity

2005 2006 2007 2008 Oats 210 110 67 75 Hay,

including alfalfa 1600 1500 1480 1550

Alfalfa 1250 1180 1140 1150

Land cover types replaced by corn the following season

Deviation from normal rotation cycles

Corn x 3 seasons in a row

2003-05 2004-06

2005-07

Most rotation deviations are on most fertile soils

Corn and land quality

% corn planted on low CSR decreased from 34% to 29%. cornfields area on high CSR soil increased from 66% to 71% These data give no evidence that corn was pushed to lower quality or marginal lands.An increasing area of more productive lands was allocated for growing corn. This trend reflects the tendency to address the demand to increase corn planted areas by planting the most valuable crop (corn) on most suitable soils overuse of most fertile land2007 season – highest ever use of fertilizers

CRP lands and the boom• CRP declined in 2005-2006; 2007-2009

2007 2008• Acres eligible: 500,000 330,000• Acres withdrawn in Iowa (%) 28 33• Acres withdrawn in USA(%) 15 19

• Termination of CRP contracts at the peak of the ‘boom’ in 2007 and subsequent 2008 was one of the reasons of the 13.5% CRP acreage decline in 2007-2009

Year Near plants Not Near plants

Concluding remarks: Implications of ethanol boom

• Overuse of primary lands, highest-quality soils• Rotation deviation and increased use of fertilizers• Displacement of secondary crops to less productive

soils• Decrease in crop diversity• Withdrawal from CRP, but limited (30%)• Little relationship between location of ethanol

plants and corn acreage expansion• Most coping practices are unsustainable

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