A global assessment of market accessibility and market influence for global environmentalchange studies
Identifieur interne : 001302 ( Istex/Corpus ); précédent : 001301; suivant : 001303A global assessment of market accessibility and market influence for global environmentalchange studies
Auteurs : Peter H. Verburg ; Erle C. Ellis ; Aurelien LetourneauSource :
- Environmental Research Letters [ 1748-9326 ] ; 2011.
Abstract
Markets influence the global patterns of urbanization, deforestation, agriculture and otherland use systems. Yet market influence is rarely incorporated into spatially explicit globalstudies of environmental change, largely because consistent global data are lacking belowthe national level. Here we present the first high spatial resolution gridded data depictingmarket influence globally. The data jointly represent variations in both market strengthand accessibility based on three market influence indices derived from an indexof accessibility to market locations and national level gross domestic product(purchasing power parity). These indices show strong correspondence with humanpopulation density while also revealing several distinct and useful relationships withother global environmental patterns. As market influence grows, the need forhigh resolution global data on market influence and its dynamics will becomeincreasingly important to understanding and forecasting global environmental change.
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DOI: 10.1088/1748-9326/6/3/034019
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">A global assessment of market accessibility and market influence for global environmentalchange studies</title><author><name sortKey="Verburg, Peter H" sort="Verburg, Peter H" uniqKey="Verburg P" first="Peter H" last="Verburg">Peter H. Verburg</name><affiliation><mods:affiliation>Institute for Environmental Studies, Amsterdam Global Change Institute, VU University Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: Peter.Verburg@ivm.vu.nl</mods:affiliation></affiliation></author><author><name sortKey="Ellis, Erle C" sort="Ellis, Erle C" uniqKey="Ellis E" first="Erle C" last="Ellis">Erle C. Ellis</name><affiliation><mods:affiliation>Department of Geography & Environmental Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA</mods:affiliation></affiliation></author><author><name sortKey="Letourneau, Aurelien" sort="Letourneau, Aurelien" uniqKey="Letourneau A" first="Aurelien" last="Letourneau">Aurelien Letourneau</name><affiliation><mods:affiliation>UMR 5175 Centre dEcologie Fonctionnelle & Evolutive, Centre National de la Recherche Scientifique, 1919 Route de Mende, 34293 Montpellier cedex 5, France</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:59F8454B1E34A75C0F0345E6CFEB23396D32DCFF</idno><date when="2011" year="2011">2011</date><idno type="doi">10.1088/1748-9326/6/3/034019</idno><idno type="url">https://api.istex.fr/document/59F8454B1E34A75C0F0345E6CFEB23396D32DCFF/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001302</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001302</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">A global assessment of market accessibility and market influence for global environmentalchange studies</title><author><name sortKey="Verburg, Peter H" sort="Verburg, Peter H" uniqKey="Verburg P" first="Peter H" last="Verburg">Peter H. Verburg</name><affiliation><mods:affiliation>Institute for Environmental Studies, Amsterdam Global Change Institute, VU University Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: Peter.Verburg@ivm.vu.nl</mods:affiliation></affiliation></author><author><name sortKey="Ellis, Erle C" sort="Ellis, Erle C" uniqKey="Ellis E" first="Erle C" last="Ellis">Erle C. Ellis</name><affiliation><mods:affiliation>Department of Geography & Environmental Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA</mods:affiliation></affiliation></author><author><name sortKey="Letourneau, Aurelien" sort="Letourneau, Aurelien" uniqKey="Letourneau A" first="Aurelien" last="Letourneau">Aurelien Letourneau</name><affiliation><mods:affiliation>UMR 5175 Centre dEcologie Fonctionnelle & Evolutive, Centre National de la Recherche Scientifique, 1919 Route de Mende, 34293 Montpellier cedex 5, France</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Environmental Research Letters</title><title level="j" type="abbrev">Environ. Res. Lett.</title><idno type="ISSN">1748-9326</idno><idno type="eISSN">1748-9326</idno><imprint><publisher>IOP Publishing</publisher><date type="published" when="2011">2011</date><biblScope unit="volume">6</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="1">1</biblScope><biblScope unit="page" to="12">12</biblScope><biblScope unit="production">Printed in the UK</biblScope></imprint><idno type="ISSN">1748-9326</idno></series><idno type="istex">59F8454B1E34A75C0F0345E6CFEB23396D32DCFF</idno><idno type="DOI">10.1088/1748-9326/6/3/034019</idno><idno type="PII">S1748-9326(11)91689-9</idno><idno type="articleID">391689</idno><idno type="articleNumber">034019</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1748-9326</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Markets influence the global patterns of urbanization, deforestation, agriculture and otherland use systems. Yet market influence is rarely incorporated into spatially explicit globalstudies of environmental change, largely because consistent global data are lacking belowthe national level. Here we present the first high spatial resolution gridded data depictingmarket influence globally. The data jointly represent variations in both market strengthand accessibility based on three market influence indices derived from an indexof accessibility to market locations and national level gross domestic product(purchasing power parity). These indices show strong correspondence with humanpopulation density while also revealing several distinct and useful relationships withother global environmental patterns. As market influence grows, the need forhigh resolution global data on market influence and its dynamics will becomeincreasingly important to understanding and forecasting global environmental change.</div></front></TEI><istex><corpusName>iop</corpusName><author><json:item><name>Peter H Verburg</name><affiliations><json:string>Institute for Environmental Studies, Amsterdam Global Change Institute, VU University Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands</json:string><json:string>E-mail: Peter.Verburg@ivm.vu.nl</json:string></affiliations></json:item><json:item><name>Erle C Ellis</name><affiliations><json:string>Department of Geography & Environmental Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA</json:string></affiliations></json:item><json:item><name>Aurelien Letourneau</name><affiliations><json:string>UMR 5175 Centre dEcologie Fonctionnelle & Evolutive, Centre National de la Recherche Scientifique, 1919 Route de Mende, 34293 Montpellier cedex 5, France</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>accessibility</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>land use</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>environmental change</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>markets</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>economy</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>global</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>GDP</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>world</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>letter</json:string></originalGenre><abstract>Markets influence the global patterns of urbanization, deforestation, agriculture and otherland use systems. Yet market influence is rarely incorporated into spatially explicit globalstudies of environmental change, largely because consistent global data are lacking belowthe national level. Here we present the first high spatial resolution gridded data depictingmarket influence globally. The data jointly represent variations in both market strengthand accessibility based on three market influence indices derived from an indexof accessibility to market locations and national level gross domestic product(purchasing power parity). These indices show strong correspondence with humanpopulation density while also revealing several distinct and useful relationships withother global environmental patterns. 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Res. Lett.</title><idno type="pISSN">1748-9326</idno><idno type="eISSN">1748-9326</idno><imprint><publisher>IOP Publishing</publisher><date type="published" when="2011"></date><biblScope unit="volume">6</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="1">1</biblScope><biblScope unit="page" to="12">12</biblScope><biblScope unit="production">Printed in the UK</biblScope></imprint></monogr><idno type="istex">59F8454B1E34A75C0F0345E6CFEB23396D32DCFF</idno><idno type="DOI">10.1088/1748-9326/6/3/034019</idno><idno type="PII">S1748-9326(11)91689-9</idno><idno type="articleID">391689</idno><idno type="articleNumber">034019</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2011</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>Markets influence the global patterns of urbanization, deforestation, agriculture and otherland use systems. Yet market influence is rarely incorporated into spatially explicit globalstudies of environmental change, largely because consistent global data are lacking belowthe national level. Here we present the first high spatial resolution gridded data depictingmarket influence globally. The data jointly represent variations in both market strengthand accessibility based on three market influence indices derived from an indexof accessibility to market locations and national level gross domestic product(purchasing power parity). These indices show strong correspondence with humanpopulation density while also revealing several distinct and useful relationships withother global environmental patterns. As market influence grows, the need forhigh resolution global data on market influence and its dynamics will becomeincreasingly important to understanding and forecasting global environmental change.</p></abstract><textClass><keywords scheme="keyword"><list><head>keywords</head><item><term>accessibility</term></item><item><term>land use</term></item><item><term>environmental change</term></item><item><term>markets</term></item><item><term>economy</term></item><item><term>global</term></item><item><term>GDP</term></item><item><term>world</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2011">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/59F8454B1E34A75C0F0345E6CFEB23396D32DCFF/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus iop not found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="ISO-8859-1" </istex:xmlDeclaration><istex:docType SYSTEM="http://ej.iop.org/dtd/iopv1_5_2.dtd" name="istex:docType"></istex:docType><istex:document><article artid="erl391689"><article-metadata><jnl-data jnlid="erl"><jnl-fullname>Environmental Research Letters</jnl-fullname><jnl-abbreviation>Environ. 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Lett.</jnl-abbreviation><jnl-shortname>ERL</jnl-shortname><jnl-issn>1748-9326</jnl-issn><jnl-coden>ERLNAL</jnl-coden><jnl-imprint>IOP Publishing</jnl-imprint><jnl-web-address>stacks.iop.org/ERL</jnl-web-address></jnl-data><volume-data><year-publication>2011</year-publication><volume-number>6</volume-number></volume-data><issue-data><issue-number>3</issue-number><coverdate>July-September 2011</coverdate></issue-data><article-data><article-type type="letter" sort="regular"></article-type><type-number type="letter" numbering="article" artnum="034019"></type-number><article-number>391689</article-number><first-page>1</first-page><last-page>12</last-page><length>12</length><pii>S1748-9326(11)91689-9</pii><doi>10.1088/1748-9326/6/3/034019</doi><copyright>IOP Publishing Ltd</copyright><ccc>1748-9326/11/034019 + 12$33.00</ccc><printed>Printed in the UK</printed></article-data></article-metadata><header><title-group><title>A global assessment of market accessibility and market influence for global environmental
change studies</title><short-title>A market influence indicator for global change studies
</short-title><ej-title>A global assessment of market accessibility and market influence for global environmental
change studies
</ej-title></title-group><author-group><author address="erl391689ad1" email="erl391689ea1"><first-names>Peter H</first-names><second-name>Verburg</second-name></author><author address="erl391689ad2"><first-names>Erle C</first-names><second-name>Ellis</second-name></author><author address="erl391689ad3"><first-names>Aurelien</first-names><second-name>Letourneau</second-name></author><short-author-list>P H Verburg <italic>et al</italic> </short-author-list></author-group><address-group><address id="erl391689ad1"><orgname>Institute for Environmental Studies, Amsterdam Global Change Institute, VU University
Amsterdam</orgname>, De Boelelaan 1087, 1081 HV Amsterdam,
<country>The Netherlands</country></address><address id="erl391689ad2"><orgname>Department of Geography & Environmental Systems, University of Maryland</orgname>, Baltimore
County, Baltimore, MD 21250,
<country>USA</country></address><address id="erl391689ad3"><orgname>UMR 5175 Centre d’Ecologie Fonctionnelle & Evolutive, Centre National de la Recherche
Scientifique</orgname>, 1919 Route de Mende, 34293 Montpellier cedex 5,
<country>France</country></address><e-address id="erl391689ea1"><email mailto="Peter.Verburg@ivm.vu.nl">Peter.Verburg@ivm.vu.nl</email></e-address></address-group><history received="21 April 2011" accepted="2 August 2011" online="19 August 2011"></history><abstract-group><abstract><heading>Abstract</heading><p indent="no">Markets influence the global patterns of urbanization, deforestation, agriculture and other
land use systems. Yet market influence is rarely incorporated into spatially explicit global
studies of environmental change, largely because consistent global data are lacking below
the national level. Here we present the first high spatial resolution gridded data depicting
market influence globally. The data jointly represent variations in both market strength
and accessibility based on three market influence indices derived from an index
of accessibility to market locations and national level gross domestic product
(purchasing power parity). These indices show strong correspondence with human
population density while also revealing several distinct and useful relationships with
other global environmental patterns. As market influence grows, the need for
high resolution global data on market influence and its dynamics will become
increasingly important to understanding and forecasting global environmental change.</p></abstract></abstract-group><classifications><keywords><keyword>accessibility</keyword><keyword>land use</keyword><keyword>environmental change</keyword><keyword>markets</keyword><keyword>economy</keyword><keyword>global</keyword><keyword>GDP</keyword><keyword>world</keyword></keywords></classifications></header><body refstyle="alphabetic"><sec-level1 id="erl391689s1" label="1"><heading>Introduction</heading><p indent="no">Human interactions with local environments have far reaching consequences for the
functioning of the Earth system, including global climate, biodiversity, and biogeochemistry
(<cite linkend="erl391689bib59">Rockstrom <italic>et al</italic> 2009</cite>). Human–environment interactions vary greatly in
type, intensity and duration across Earth’s land surface, depending on a wide variety of
dynamic influences at global, regional and local scales, including climate, land suitability
for use, governance and economic systems and their history of interaction at specific
locations (<cite linkend="erl391689bib14">Ellis and Ramankutty 2008</cite>, <cite linkend="erl391689bib17">Gallup <italic>et al</italic> 1999</cite>, <cite linkend="erl391689bib42">Liverman and Cuesta 2008</cite>, <cite linkend="erl391689bib58">Rindfuss <italic>et al</italic> 2008</cite>). For this
reason, high spatial resolution data on both human and environmental systems are needed
to understand and assess the local causes and consequences of global environmental change
processes driven by human interactions with land.</p><p>High spatial resolution global data for land cover, soils and other biophysical
variables are now widely available from remote sensing and the coordinated
efforts of global observation networks (<cite linkend="erl391689bib1">Achard <italic>et al</italic> 2007</cite>, <cite linkend="erl391689bib3">Batjes 2009</cite>, <cite linkend="erl391689bib25">Herold <italic>et al</italic> 2008</cite>, <cite linkend="erl391689bib27">Hijmans <italic>et al</italic> 2005</cite>, <cite linkend="erl391689bib64">Sanchez <italic>et al</italic> 2009</cite>, <cite linkend="erl391689bib66">Schneider <italic>et al</italic> 2009</cite>). High spatial resolution
data on human systems are much less available (<cite linkend="erl391689bib26">Hibbard <italic>et al</italic> 2010</cite>, <cite linkend="erl391689bib71">Verburg <italic>et al</italic> 2011</cite>). While global agencies including the FAO and the
World Bank produce annual harmonized inventories of many human variables at
national level, these data are not generally available at sub-national scales. As a
result, global level analysis of environmental change tends to be biased toward
biophysical processes or restricted to national levels (<cite linkend="erl391689bib61">Rudel 2009</cite>).
Exceptions include high resolution gridded data for human population density
(<cite linkend="erl391689bib8">Dobson <italic>et al</italic> 2000</cite>, ORNL 2008) the use of land for urban settlements, crops,
pastures and livestock created by disaggregating national and sub-national datasets using
spatial models (<cite linkend="erl391689bib1">Achard <italic>et al</italic> 2007</cite>, <cite linkend="erl391689bib25">Herold <italic>et al</italic> 2008</cite>, <cite linkend="erl391689bib31">Klein Goldewijk <italic>et al</italic> 2011</cite>, <cite linkend="erl391689bib35">Kruska <italic>et al</italic> 2003</cite>, <cite linkend="erl391689bib56">Ramankutty and Foley 1998</cite>, <cite linkend="erl391689bib66">Schneider <italic>et al</italic> 2009</cite>) and even
for crop management (<cite linkend="erl391689bib47">Monfreda <italic>et al</italic> 2008</cite>, <cite linkend="erl391689bib63">Sacks <italic>et al</italic> 2010</cite>).</p><p>For socioeconomic variables, globally standardized data have been limited to population
density and gross domestic product (GDP) related measures. Most socioeconomic datasets
are the result of spatial downscaling of (sub)national statistics with the help of topographic
data (<cite linkend="erl391689bib2">Baer 2009</cite>). <cite linkend="erl391689bib17">Gallup <italic>et al</italic> (1999)</cite> produced the
first global gridded map of ‘GDP density’ by multiplying national level GDP
by human population density. Poverty data have been downscaled by nighttime
lights observed from remote sensing (<cite linkend="erl391689bib15">Elvidge <italic>et al</italic> 2009</cite>). In this
study correlations between national level poverty statistics and average national
level nighttime light intensity were used to assign poverty values to individual
pixels. <cite linkend="erl391689bib9">Doll <italic>et al</italic> (2006)</cite> similarly use nighttime light intensity to map
regional economic activity. Influential maps of economic activity have been prepared
using national and sub-national statistics and global gridded population data
(<cite linkend="erl391689bib52">Nordhaus 2006</cite>, <cite linkend="erl391689bib53">Nordhaus and Chen 2009</cite>). These studies
show that variation within nations of such socioeconomic parameters is often larger than
variation of average values between nations. If only average values from national level
datasets are used there is a large risk for misinterpretations due to the many non-linear
relations within human–environment interactions and the notion of ecological fallacy
(<cite linkend="erl391689bib11">Easterling 1997</cite>).</p><p>Markets have become one of the most important factors driving human activities and their
interactions with the global environment. Markets link local activities to larger regions and
global processes through trade. For farmers, markets are a means to sell their products to
consumers while at the same time markets provide access to inputs such as fertilizer and
pesticides to increase production (<cite linkend="erl391689bib29">Keys and McConnell 2005</cite>).
Markets also provide a strong incentive for investments and production choices
(<cite linkend="erl391689bib7">Chomitz and Gray 1996</cite>, <cite linkend="erl391689bib73">Walker 2004</cite>). In one of the early
theories on the geography of land use Von Thünen describes land use choices as a result of
market prices and transport costs to the market. The location of markets is therefore since
long considered an important determinant of land change, especially in a strongly
globalizing world. <cite linkend="erl391689bib54">Peet (1969)</cite> describes the role of markets within the
colonial system indicating that markets influence production decisions over large distances.
Since colonial times, global trade has increased many fold and improved accessibility has
made global market conditions even more important (<cite linkend="erl391689bib4">Britz and Hertel 2011</cite>, <cite linkend="erl391689bib37">Lambin and Meyfroidt 2011</cite>, <cite linkend="erl391689bib44">Meijl <italic>et al</italic> 2006</cite>). Market
access is also listed as one of the main determinants of deforestation in a
meta-analysis of case studies around the world (<cite linkend="erl391689bib18">Geist and Lambin 2002</cite>, <cite linkend="erl391689bib60">Rudel 2005</cite>) and is used in many regional studies of land change as an
important determinant (<cite linkend="erl391689bib50">Nelson <italic>et al</italic> 2004</cite>, <cite linkend="erl391689bib55">Pfaff 1999</cite>, <cite linkend="erl391689bib72">Verburg <italic>et al</italic> 2004</cite>).</p><p>Transportation infrastructure largely determines the access people have to markets, and this
market access tends to drive further infrastructure expansion (<cite linkend="erl391689bib24">Hansen 1959</cite>).
Infrastructure construction and operation is by itself a primary driver of environmental
change (<cite linkend="erl391689bib10">Doyle and Havlick 2009</cite>). Infrastructure expansion and associated
environmental changes, are driven by economic demand for the services that infrastructure
provides, combined with the political will and ability to facilitate the implementation
of the infrastructure construction and operational programs. As a result, the
processes of increasing market influence and improving market access tend to be
interwoven.</p><p>For global-scale studies, high spatial resolution data on the influence of markets is lacking.
This letter develops and demonstrates the first high spatial resolution global datasets of
market influence indices, including their strengths and weaknesses as predictors of the
global spatial patterns of land use and land cover, human populations, biomes, anthromes
(anthropogenic biomes: globally significant ecological patterns created by sustained
interactions between humans and ecosystems), plant species richness, and net primary
production.</p></sec-level1><sec-level1 id="erl391689s2" label="2"><heading>Data and methods</heading><sec-level2 id="erl391689s2.1" label="2.1"><heading>Development of market influence indices</heading><p indent="no">To derive a globally consistent indicator of market influence, the concept of market
influence was matched with available, independent, global datasets. It is assumed that
market influence at a specific location is determined as a function of accessibility to
markets and the importance of these markets. While the importance of individual markets
is difficult to measure and consistent data at the level of individual markets are lacking,
national level GDP is a general indicator of market importance across individual
nations, as it measures a nation’s overall economic output in terms of the market
value of all final goods and services made within the borders of a nation in a
year.</p><p>Accessibility to markets may be measured in many different ways and a wide literature
of accessibility measures is available (<cite linkend="erl391689bib19">Geurs and van Wee 2004</cite>, <cite linkend="erl391689bib36">Kwan <italic>et al</italic> 2003</cite>, <cite linkend="erl391689bib41">Lei and Church 2010</cite>). Accessibility is
loosely defined by <cite linkend="erl391689bib28">Ingram (1971)</cite> as the inherent characteristic (or
advantage) of a place with respect to overcoming some form of spatially operating source of
friction (for example time or distance). Different authors have discussed various measures
of accessibility varying from simple line distance between two locations to measures that
account for the infrastructure network and travel costs. Distance measures (also called
connectivity measures) are the simplest class of location-based accessibility measures. We
have chosen to base the accessibility not solely on distance but also account for the
infrastructure and a number of terrain characteristics that impede access to the
markets. Therefore, our measure is based on travel time rather than on the absolute
distance. Many studies have modified such simple location-based accessibility
measures by accounting for some aspects related to behavior and perception. In
so-called potential accessibility measures the influence of a location is diminishing by
travel time (<cite linkend="erl391689bib19">Geurs and van Wee 2004</cite>, <cite linkend="erl391689bib22">Guy 1983</cite>, <cite linkend="erl391689bib28">Ingram 1971</cite>). By integrating measures of economic strength and
accessibility we have created a simple and straightforward indicator for market
influence.
</p></sec-level2><sec-level2 id="erl391689s2.2" label="2.2"><heading>Data</heading><p indent="no">A variety of publicly available global datasets were used in calculating market influence
indices (table <tabref linkend="erl391689tab1">1</tabref>). For a number of variables, different alternative datasets were available
and a selection was made based on global consistency and fit with the specific
application. International data on roads are extremely patchy and inconsistent, with
frequent gaps and many large changes in time that are often quickly reversed
(<cite linkend="erl391689bib5">Canning 1998</cite>). Most available datasets are based on sources of individual
nations (<cite linkend="erl391689bib49">Nelson <italic>et al</italic> 2006</cite>). Different nations define roads differently, and
the definition of a road often changes within nations over time. Rural roads above a certain
quality threshold are often centrally controlled, while urban roads are controlled by
municipal authorities, leading to an underreporting of urban and low-quality rural
roads controlled by the central authority. We have used the VMAP0 database of
infrastructure given its public availability and global consistency as compared
to more recent databases. Though more recent databases contain much more
detail in road patterns for a number of nations, differences in detail between
countries is also much greater, derailing the construction of globally consistent
accessibility indices (<cite linkend="erl391689bib49">Nelson <italic>et al</italic> 2006</cite>). Rivers were also taken
from VMAP0 which is generally considered to provide the most comprehensive
and consistent global river network data currently available. It is based on the
US DMA (now NGA) Operational Navigation Charts. Although more detailed,
satellite-based river network data are available (<cite linkend="erl391689bib39">Lehner 2005</cite>), these
include many smaller streams that are not navigable and therefore not adding to
accessibility. Gross domestic product (GDP) values on a purchasing power parity (PPP)
basis were obtained from the CIA Factbook and a link with a map of national
territories was made to allow spatial representation. GDP measured in PPP was
chosen over GDP measured at market exchange rates to standardize international
comparisons.</p><table id="erl391689tab1" width="42pc"><caption id="tc1" label="Table 1"><p indent="no">List of global datasets used for calculating the market influence index.</p></caption><tgroup cols="4"><colspec colnum="1" colname="col1" align="left"></colspec><colspec colnum="2" colname="col2" colwidth="4pc" align="justify"></colspec><colspec colnum="3" colname="col3" colwidth="6pc" align="justify"></colspec><colspec colnum="4" colname="col4" colwidth="16.4pc" align="justify"></colspec><thead><row><entry>Variable</entry><entry>Year</entry><entry>Spatial characteristics</entry><entry>Source</entry></row></thead><tbody><row><entry>Road network, rivers</entry><entry>1979–1999</entry><entry>Vector map, 1:1 M</entry><entry>National Geospatial Intelligence Agency (NGA); VMAP0</entry></row><row><entry>Slope</entry><entry>—</entry><entry>Slope derived from resampled altitude data; so the slope only captures the overall
topography and is no measure of the real slope</entry><entry>Based on SRTM elevation data (<cite linkend="erl391689bib16">Farr <italic>et al</italic> 2007</cite>) resampled to 1 km</entry></row><row><entry>Wetlands</entry><entry>Approx. 1990–2000</entry><entry>30 s resolution map containing different wetland types</entry><entry>(<cite linkend="erl391689bib40">Lehner and Döll 2004</cite>)</entry></row><row><entry>Cities > 750 000</entry><entry>2003</entry><entry>Point data</entry><entry>Selected from UNEP major urban agglomeration database (<webref url="http://www.geodata.grid.unep.ch">www.geodata.grid.unep.ch</webref>)</entry></row><row><entry>Cities > 50 000</entry><entry>Approx. 2000</entry><entry>Point data</entry><entry>Database compiled by the Joint Research Centre of the European Commission
(<cite linkend="erl391689bib48">Nelson 2008</cite>) based on the GPW database, CIESIN, Columbia University
and the World Bank database of air pollution in World cities</entry></row><row><entry>Maritime ports</entry><entry>2005</entry><entry>Point data; harbors with size ‘large’ are
selected<sup>a</sup></entry><entry>Global Maritime Ports Database produced by General Dynamics Advanced Information
Systems</entry></row><row><entry>Population density</entry><entry>2000</entry><entry>30 s resolution grid</entry><entry>GPWv3 database; Center for International Earth Science Information Network
(CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical
(CIAT). 2005. Gridded Population of the World Version 3 (GPWv3). Palisades, NY:
Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at
<webref url="http://sedac.ciesin.columbia.edu/gpw">http://sedac.ciesin.columbia.edu/gpw</webref>.</entry></row><row><entry>Gross domestic product (on a purchasing power parity basis)</entry><entry>2010, in case of missing data earlier years</entry><entry>National level</entry><entry>CIA World factbook (<webref url="http://www.cia.gov/library/publications/the-world-factbook">www.cia.gov/library/publications/the-world-factbook</webref>)</entry></row></tbody><tfoot><sup>a</sup> The size classification in the database is based on a combination of attributes including
area, facilities, and wharf space. Ports classified as large must at least be able to
accommodate vessels over 500 feet.</tfoot></tgroup></table></sec-level2><sec-level2 id="erl391689s2.3" label="2.3"><heading>Calculation of market access and market influence indices</heading><p indent="no">Calculation of market influence indices consisted of two distinct steps (figure <figref linkend="erl391689fig1">1</figref>): first, an index of access to national and international markets was
calculated, and second, two indices of market influence, one by combining national GDP
data directly with the access index, and the other by downscaling national GDP using a
measure of economic density. All calculations are made at a spatial resolution of 1
km<sup>2</sup>
in Eckert IV Equal Area Projection using a geographical information system
(GIS).
<figure id="erl391689fig1" parts="single" width="page" position="float" printstyle="normal" orientation="port"><graphic><graphic-file version="print" format="EPS" scale="100" filename="images/9168901.eps"></graphic-file><graphic-file version="ej" format="JPEG" filename="images/9168901.jpg"></graphic-file></graphic><caption id="erl391689fc1" type="figure" label="Figure 1"><p indent="no">Overview of the different steps involved in calculating the market influence indices.</p></caption></figure></p><sec-level3 id="erl391689s2.3.1" label="2.3.1"><heading>Market access index</heading><p indent="no">The calculation of market access is based on a set of destinations that people travel to and
a measure of the costs of traveling, either in distance, time or monetary costs. For this
study we have used two groups of locations as destinations. The first group represents large
domestic and international markets. Consistent data are not available at global scale
representing the locations of markets. Therefore, as a proxy for these locations
cities or urban agglomerations with more than 750.000 inhabitants were used.
Cities/agglomerations of such a size are in any case locations of important domestic
markets while they often have airports important for the import/export of the
country. In addition to these, large maritime ports are included as important
locations representing the influence of international markets. The second group
represents locations that are important destinations as regional and domestic markets.
All towns and cities with a population of more than 50.000 inhabitants were
selected.</p><p>Travel time accounting for infrastructure and some aspects of terrain was used to measure
the accessibility to the selected destinations. Although costs of travel, means of transport
and the quality of infrastructure have a high variation a globally uniform approach was
used to represent differences in travel time to each of the two groups of destinations.
Table <tabref linkend="erl391689tab2">2</tabref> provides an overview of the assumed velocities to calculate the total travel
time. Assumed velocities are within the range of 75–100% of the most common speed limits
for the different road types while the speed on large rivers is accounting for waiting times
at sluices etc. It is assumed that it is also possible to travel outside the infrastructure
network represented in the data as many smaller roads are available. Off-road
speed is however assumed to be relatively low to account for deviations from
straight-line connections, especially in mountain and wetland landscapes that pose
barriers and often have a lower density of smaller roads. Because the calculated
travel times are converted to an index it is the relative speed across different
types of infrastructure and terrain that is important rather than the absolute
values. Air transportation is ignored in the analysis as it mainly links urban areas
that are designated as destinations in the analysis and consequently have a high
accessibility.</p><table id="erl391689tab2"><caption id="tc2" label="Table 2"><p indent="no">Speed assumed for different types of infrastructure and terrain to calculate the travel time
to the nearest market.</p></caption><tgroup cols="2"><colspec colnum="1" colname="col1" align="left"></colspec><colspec colnum="2" colname="col2" align="left"></colspec><thead><row><entry>Infrastructure/terrain type</entry><entry>Speed (km h<sup> − 1</sup>)</entry></row></thead><tbody><row><entry>Highways</entry><entry>100</entry></row><row><entry>Primary/secondary roads</entry><entry>65</entry></row><row><entry>Tertiary roads</entry><entry>40</entry></row><row><entry>Railways</entry><entry>70</entry></row><row><entry>Large rivers</entry><entry>10</entry></row><row><entry>Canals</entry><entry>5</entry></row><row><entry>Seas, lakes and reservoirs</entry><entry>2</entry></row><row><entry>Off-road:</entry><entry></entry></row><row><entry>Flat or gentle slopes, small rivers</entry><entry>5</entry></row><row><entry>Moderate slopes</entry><entry>3</entry></row><row><entry>Steep slopes</entry><entry>1</entry></row><row><entry>Marshes, Swamps, Bogs, Peatlands<sup>a</sup></entry><entry>3</entry></row><row><entry>International borders</entry><entry>Speed is divided by 10 over a distance of 1 km</entry></row></tbody><tfoot><sup>a</sup> Only wetland classes covering more than 50% of the designated area in the database are
considered.</tfoot></tgroup></table><p>The calculated travel times to the two different types of destination are integrated into one
index accounting for travel behavior. As distance or travel time increases people
are less likely to travel to certain locations and curvilinear functions of distance
are more suitable than linear relationships. <cite linkend="erl391689bib28">Ingram (1971)</cite> and <cite linkend="erl391689bib22">Guy (1983)</cite> compare different functional forms and conclude that a Gaussian
curve is the most applicable for the quantitative measurement of accessibility. A
Gaussian function has a slow rate of decline in the region close to the origin, thus
allowing for the zone where the frictional effects of distance on accessibility is low
following:
<display-eqn id="erl391689eqn1" eqnnum="1"></display-eqn>
where <italic>a</italic><sub><italic>ij</italic></sub> is the relative
accessibility of point <italic>i</italic>
to destination <italic>j</italic> and
<italic>d</italic><sub><italic>i</italic>, <italic>j</italic></sub> is the distance
between points <italic>i</italic> and
<italic>j</italic>. For each location
<italic>i</italic>, <italic>a</italic><sub><italic>ij</italic></sub> is calculated for the
closest destination <italic>j</italic>
for respectively the national/international market locations and the
regional markets. The importance (size or frequency of visit) of destination
<italic>j</italic> is
<italic>Sj</italic> and
<italic>v</italic>
is a constant specific to the study. In our study we have assigned
<italic>Sj</italic>
a value of 1 for the national and international market locations (including ports) and a
value of 0.5 for the regional market locations. This arbitrary choice of values allows us to
distinguish the influence of the different types of market. Within equation (<eqnref linkend="erl391689eqn1">1</eqnref>)
<italic>v</italic>
is a constant. According to <cite linkend="erl391689bib28">Ingram (1971)</cite> this constant may be
set at the average squared distance between all points considered. However,
no rational is provided. <cite linkend="erl391689bib22">Guy (1983)</cite> indicates that a value for
<italic>v</italic>
may be chosen such that the steepest part of the graph (that is, the point of
inflexion) is at a predetermined distance from the origin. In this case it can be shown
that:
<display-eqn id="erl391689eqn2" eqnnum="2"></display-eqn>
where <italic>d</italic><sub>*</sub> is the
distance from <italic>i</italic>
at which accessibility is deemed to decline at the most rapid rate. Since we assume that the
influence of large market locations is stronger than the influence of regional markets we
have calculated the constant for the two groups of destinations with values for
the inflection point of respectively 2 h for large markets and 45 min for smaller
regional markets. In the final accessibility index we have used the maximum value
<italic>a</italic><sub><italic>ij</italic></sub>
for the national/international markets and the regional markets. Figure <figref linkend="erl391689fig2">2</figref> provides an illustration of the decline in accessibility index with the travel
time from a large market.
<figure id="erl391689fig2" parts="single" width="column" position="float" printstyle="normal" orientation="port"><graphic><graphic-file version="print" format="EPS" scale="100" filename="images/9168902.eps"></graphic-file><graphic-file version="ej" format="JPEG" filename="images/9168902.jpg"></graphic-file></graphic><caption id="erl391689fc2" type="figure" label="Figure 2"><p indent="no">Illustration of the decrease in market access index with travel time from the location of a
large city. At 150 and 250 min from the large city two regional markets are located.</p></caption></figure></p></sec-level3><sec-level3 id="erl391689s2.3.2" label="2.3.2"><heading>Market influence indices</heading><p indent="no">Two different indices of market influence were developed (figure <figref linkend="erl391689fig1">1</figref>). The simplest, market influence index, characterizes market influence by
simply multiplying the accessibility index by national level per capita GDP values,
independent of population density data. This creates an index of market influence expressed in
$ per capita (market access is dimensionless), essentially treating market influence as the
multiplicative effect of local market access and national market importance (GDP/capita
measured in PPP). A second index, market density, incorporates population density data in
an effort to allocate market importance (GDP/capita) across space before combining it
with market access, thereby describing market influence as a density expressed in $
km<sup> − 2</sup>. This is accomplished by dividing the local market accessibility index by the national
average of the market access index and then multiplying this by the PPP and population
density as illustrated in figure <figref linkend="erl391689fig1">1</figref>. The global data for both indices are available for download at
<webref url="http://www.ivm.vu.nl/marketinfluence">www.ivm.vu.nl/marketinfluence</webref>.
</p></sec-level3></sec-level2><sec-level2 id="erl391689s2.4" label="2.4"><heading>Analysis of market influence in relation to other global patterns</heading><p indent="no">To investigate the utility of our new global market indices, their relationships with existing
global data for human and environmental variables were assessed. Global land cover data
were obtained from the GlobCover v2.2 dataset (<webref url="http://ionia1.esrin.esa.int/">http://ionia1.esrin.esa.int/</webref>)
and simplified into ten land cover classes by merging forest and scrubland types.
Spatial data at 5 arc min resolution were obtained for human population density
in year 2000 (<cite linkend="erl391689bib30">Klein Goldewijk <italic>et al</italic> 2010</cite> based on Landscan
(<cite linkend="erl391689bib8">Dobson <italic>et al</italic> 2000</cite>)), anthromes (<cite linkend="erl391689bib13">Ellis <italic>et al</italic> 2010</cite>), potential
vegetation biomes (<cite linkend="erl391689bib57">Ramankutty and Foley 1999</cite>), potential net primary
productivity (NPP; <cite linkend="erl391689bib23">Haberl <italic>et al</italic> 2007</cite>), and potential plant species richness
in regional landscapes (based on <cite linkend="erl391689bib33">Kreft and Jetz 2007</cite>). For analysis,
population density, NPP and plant species richness were stratified into 11 classes (including
a ‘zero class’) covering their full range of variation. Global data were processed at 5 arc
minute resolution using zonal statistics in a GIS to create a database allowing calculation
of land area-weighted statistics for market indices and population density in relation to
other global patterns.
</p></sec-level2></sec-level1><sec-level1 id="erl391689s3" label="3"><heading>Results</heading><sec-level2 id="erl391689s3.1" label="3.1"><heading>Spatial patterns in market influence indices</heading><p indent="no">Figure <figref linkend="erl391689fig3">3</figref> illustrates global patterns in market influence described by each of our three
indices. As expected, market influence is strongest near large cities in all indices, declining
to zero in regions without human populations. Also as expected, the market access index is
saturated near 1.0 in large cities (figure <figref linkend="erl391689fig3">3</figref>(a)), declining to moderate values in densely populated regions which have
an abundance of smaller cities and towns, as intercity distances are so small that the index
never declines below 0.2. Examples of such regions are West Africa, Central America and
parts of South America, India and China.
<figure id="erl391689fig3" parts="single" width="page" position="float" printstyle="normal" orientation="port"><graphic><graphic-file version="print" format="EPS" scale="98" filename="images/9168903.eps"></graphic-file><graphic-file version="ej" format="JPEG" filename="images/9168903.jpg"></graphic-file></graphic><caption id="erl391689fc3" type="figure" label="Figure 3"><p indent="no">Global overview of the market access index (A), the market influence index (B) and the
market influence density index (C).</p></caption></figure></p><p>The market influence index (figure <figref linkend="erl391689fig3">3</figref>(b)) differs from the market access index most clearly in areas with similar
population densities but different levels of market strength (GDP per capita). These
differences originate in the different economic conditions of these regions. For example,
India has high scores in the market access index, similar to Europe, yet the market
influence index is much lower than Europe as a result of the region’s relatively low GDP
per capita. The effects of differences in GDP are even clearer if we look at the maps in
more detail, as in figure <figref linkend="erl391689fig4">4</figref> which zooms in to a part of the South-East Asian region. Market access is
strong in Peninsular Malaysia around Kuala Lumpur and also around the city of Medan in
Indonesia. The major differences between these two regions only become apparent when
market influence is expressed in per capita units using the market influence index, with the
lower GDP of Indonesia clearly creating different spatial patterns of market influence than
in Malaysia, areas that are otherwise similar in terms of population densities and
environmental conditions. Only when market influence is adjusted for human
population density, as it is in the market density index, do the most densely populated
developing regions of the world tend to become more prominent, as in China
(figure <figref linkend="erl391689fig3">3</figref>(c)) and the island of Java in Indonesia (figure <figref linkend="erl391689fig4">4</figref>).
<figure id="erl391689fig4" parts="single" width="page" position="float" printstyle="normal" orientation="port"><graphic><graphic-file version="print" format="EPS" scale="100" filename="images/9168904.eps"></graphic-file><graphic-file version="ej" format="JPEG" filename="images/9168904.jpg"></graphic-file></graphic><caption id="erl391689fc4" type="figure" label="Figure 4"><p indent="no">Detailed views of the accessibility and market influence indices for part of the South-East
Asian region.</p></caption></figure></p></sec-level2><sec-level2 id="erl391689s3.2" label="3.2"><heading>Global relationships between market influence and human and environmental patterns</heading><p indent="no">Figure <figref linkend="erl391689fig5">5</figref> illustrates global relationships between our three market indices and major
global patterns in human population density, land cover, anthromes, biomes, potential
NPP and potential plant species richness (an indicator of biodiversity). Global
relationships with human population density are also depicted at the top of the figure,
illustrating the strength of this most basic measure of human influence and allowing
the relative utility and additional strengths of different market indicators to be
observed. Moreover, the means, medians and inter-quartile ranges in these charts
make clear that the global variables used to assess relationships among variables
are highly non-linear, skewed, and full of inherent variation, often because large
numbers of low and zero values are combined with small numbers of extremely
high values, in some cases even causing mean values to exceed the inter-quartile
range.
<figure id="erl391689fig5" parts="single" width="page" position="float" printstyle="normal" orientation="port"><graphic><graphic-file version="print" format="EPS" scale="100" filename="images/9168905.eps"></graphic-file><graphic-file version="ej" format="JPEG" filename="images/9168905.jpg"></graphic-file></graphic><caption id="erl391689fc5" type="figure" label="Figure 5"><p indent="no">Global patterns in population density, market access and market influence indices in
relation to population density, land cover (sorted by access value), anthromes
(<cite linkend="erl391689bib13">Ellis <italic>et al</italic> 2010</cite>), biomes (<cite linkend="erl391689bib57">Ramankutty and Foley 1999</cite>),
potential net primary productivity (NPP; <cite linkend="erl391689bib23">Haberl <italic>et al</italic> 2007</cite>) and
potential plant species richness (<cite linkend="erl391689bib33">Kreft and Jetz 2007</cite>). Colored bars are
area-weighted means, diamonds are medians; error bars depict inter-quartile range.</p></caption></figure></p><p>The strongest relationships evident in the charts of figure <figref linkend="erl391689fig5">5</figref> depict the strong positive correspondence between market influence and
human population density. Urban areas in particular tend to score ‘off the charts’ on all
three indices of market influence; an obvious result given that the location of cities was
used to derive these indices. The very strong relationship of all three market influence
indices with population density outside urban areas is also unsurprising, given that maps of
market access and population density are both derived from similar input maps, including
not only settlement maps but also maps of transportation and land use. Therefore, the
distributions of other indicators with respect to market influence are more interesting,
especially those that deviate from patterns indicated by population density by
itself.</p><p>The clear global relationships between market influence and agricultural land cover in
figure <figref linkend="erl391689fig5">5</figref> appear to correspond to the classical ‘Von Thünen’ patterns, in which
(intensive) arable agriculture predominates in areas of highest market influence followed by
mosaic landscapes (crop/natural being mostly crops, natural/crops, vice versa), grazing
land and savannahs, and natural land cover types. All of the market influence indices show
these patterns, as does human population density.</p><p>Relationships between anthrome classes and market influence indices also resemble those
with population density, with notable exceptions. Villages tended toward higher population
densities than dense settlements, yet their market access was similar and their
market influence tended to be significantly lower in terms of market density. This
makes sense, in that villages generally persist only in developing nations with
long histories of subsistence economies, while dense settlements, which have low
levels of agricultural land use, mostly occur in wealthier nations relying solely on
commercial agricultural systems. Comparisons among croplands, rangelands and
semi-natural anthromes with similar population densities are also interesting
(‘Residential’ anthromes have 10–100, ‘Populated’ 1–10, and ‘Remote’ anthromes < 1 persons km<sup> − 2</sup>). As predicted by Von Thünen, market access and influence are greater in croplands than in
rangelands and semi-natural lands. Further, market influence is on average higher in
semi-natural woodlands than in rangelands, indicating perhaps the abandonment of
agriculture or conservation of nonagricultural areas in more market influenced
areas.</p><p>Relationships between markets and biomes, NPP and plant species richness are weaker
than those with anthromes and anthropogenic land cover. The strongest relationship
observed was between market influence and biomes, with a significantly higher market
influence by all indices in the temperate woodlands, confirming the prevalence of dense
and wealthy populations across the temperate woodlands. Interestingly, market
density and market access appeared to be more strongly associated with temperate
woodlands than population density, a fairly exceptional result across all measures in
figure <figref linkend="erl391689fig5">5</figref>. Population density appeared to have slightly stronger relationships with
NPP and plant species richness than did market influence indices, with very low levels of
NPP and plant species richness strongly associated with low populations, market access
and influence, and all of these have, on average, higher at intermediate middle levels of
NPP and plant species richness, though inherent variation in the values is greater than the
apparent trends.
</p></sec-level2></sec-level1><sec-level1 id="erl391689s4" label="4"><heading>Discussion and conclusions</heading><sec-level2 id="erl391689s4.1" label="4.1"><heading>Evaluation of results</heading><p indent="no">The global market influence indices presented in this letter offer an important addition to
the data available for analysis and modeling of global environmental change and land
use. Existing global models, such as IMAGE (<cite linkend="erl391689bib12">Eickhout <italic>et al</italic> 2007</cite>, <cite linkend="erl391689bib69">van Vuuren <italic>et al</italic> 2010</cite>), which is used in a wide range of global
environmental assessments, account for accessibility effects using only a simple distance to
city measure. The market influence data presented here can be used directly in IMAGE
and other such models. Compared to earlier published global accessibility datasets
(<cite linkend="erl391689bib48">Nelson 2008</cite>) this new dataset distinguishes different types of destinations,
including important maritime ports, and accounts for the strength of markets, allowing
derivation of multiple distinct and useful indicators of the global patterns of market
influence. The inclusion of ports in this study is especially important, as these can drive
environmental changes such as deforestation and plantation development wherever these
are constructed to support commodity export markets, such as bananas in central America
(<cite linkend="erl391689bib32">Kok and Veldkamp 2001</cite>).</p><p>The market influence indices presented here do have some similarities with earlier efforts to
downscale gross domestic product. The simplest method for determining the spatial spread
of GDP is to assume that GDP is equally distributed among the inhabitants of a nation or
region, so that the spread and influence of GDP is directly related to the distribution of
population (<cite linkend="erl391689bib45">Metzger <italic>et al</italic> 2010</cite>, <cite linkend="erl391689bib52">Nordhaus 2006</cite>). The
G-Econ database uses sub-national GDP values as a basis for such downscaling,
thereby capturing to some extent the differences in GDP/capita within countries
(<cite linkend="erl391689bib53">Nordhaus and Chen 2009</cite>). The representation in this database does,
however, not explicitly incorporate income differences between urban and rural regions, and
it also creates data that are very tightly and artifactually correlated with population
density. <cite linkend="erl391689bib21">Grübler <italic>et al</italic> (2007)</cite> have downscaled national level GDP values
using income distribution statistics by assuming that the richest 20% of a country’s
population reside in urban areas. The remaining 80% of the population and their
income share is assumed to be distributed according to the remaining rural–urban
population distribution. A third alternative to account for sub-national differences in
per capita GDP is the use of nighttime lights as a measure of economic activity
(<cite linkend="erl391689bib9">Doll <italic>et al</italic> 2006</cite>, <cite linkend="erl391689bib67">Sutton and Costanza 2002</cite>). While the indices
presented here, especially market density, in some ways resemble existing GDP downscaling
efforts, they differ in one very important way: all three of the new indicators couple
market strength (GDP) with variations in market access across Earth’s land using
detailed infrastructure datasets enabling much higher spatial resolutions than earlier
studies.
</p></sec-level2><sec-level2 id="erl391689s4.2" label="4.2"><heading>Relating markets to land use and anthropogenic global change</heading><p indent="no">Associations between market influence indices and land cover demonstrate that the
most intensive modifications of natural land cover, urbanization, land clearing
and cultivation, are found in areas with the highest market influence. In that
sense, market influence is a proxy for the intensity of human alteration of natural
environments and shows similar patterns as the human footprint calculated by <cite linkend="erl391689bib65">Sanderson <italic>et al</italic> (2002)</cite>. However, such an interpretation should be made
with extreme care: large areas in South Asia and Africa are densely populated with
intensive agricultural systems focused mainly on subsistence. In spite of the large
impact of these systems on the environment, the influence of markets is (still)
relatively small—and this can be differentiated by comparing market density with the
market influence index, with lower values of the latter highlighting subsistence
regions.</p><p>Besides altering land cover patterns, market access is also believed to determine the
intensity of land management practices including use of irrigation, fertilizers and other
agrichemicals (<cite linkend="erl391689bib38">Lambin <italic>et al</italic> 2001</cite>). Easy access to markets is likely to
raise land prices and favor intensive cultivation practices and access to inputs
such as fertilizers and other chemicals (<cite linkend="erl391689bib29">Keys and McConnell 2005</cite>, <cite linkend="erl391689bib70">Verburg <italic>et al</italic> 2000</cite>, <cite linkend="erl391689bib72">2004</cite>). In a global-scale analysis of the
spatial distribution of grain yields <cite linkend="erl391689bib51">Neumann <italic>et al</italic> (2010)</cite> used the market
influence index presented in this letter as one of the factors explaining the gap between
actual yield and the highest attainable yield (as defined by a frontier function). For all
three crops analyzed (rice, wheat, maize) the market influence index yielded a significant
relation with the efficiency in production; i.e. upon higher values of the market
influence index yields tended to be closer to the maximum attainable yield. The
authors used both the market influence and a standard accessibility index. Both
indicators were significant in the estimated regression models, clearly indicating the
additional value of the market influence index to the more traditional accessibility
indicators.</p><p>There is a close relationship between population density and all market influence indices
(figure <figref linkend="erl391689fig5">5</figref>). Is this just an artifact of the similar inputs used to map these variables,
or is there a good theoretical reason for this strong relationship? Theory would
indicate the latter. Markets emerge in space as a function of population density,
with low densities capable of feeding themselves without markets and having less
labor and consumptive demand to offer the marketplace, while higher densities
would increase the need for, support for, and advantages of the marketplace.
However, at the same time the results indicate that not all regions with high
population densities evolve into strong markets. Examples of regions with high
population densities and low values of the market influence index are Nigeria, Ethiopia
and the rural areas of Southern Asia (e.g. large parts of Bangladesh). Many of
such landscapes with high population densities rely on subsistence farming and
have poorly developed markets. Moreover, integration of the rural hinterland
into the market economy strongly depends on infrastructural conditions. It is
especially these aspects that are captured in our new indices, together with patterns
depending more on market forces than on population patterns, especially for the
market access and influence indices, which were derived independent of population
data.</p><p>Accessibility and economic development are also related to other geographic factors such as
terrain and climate. The provision of infrastructure is significantly correlated with
geography, particularly for poorer countries, probably because the costs and benefits of
infrastructure vary with geography. This implies that the impact of infrastructure on
economic growth may depend on geography and that geographical considerations should be
taken into account when analyzing these effects (<cite linkend="erl391689bib6">Canning and Pedroni 2008</cite>, <cite linkend="erl391689bib34">Krugman 1999</cite>). Although such relations have been explored by
several authors (<cite linkend="erl391689bib52">Nordhaus 2006</cite>) it is obvious that large regional
deviations occur. Therefore, the inclusion of data on the spatial distribution of
socioeconomic conditions in global environmental change studies will remain essential. The
high spatial resolution indicator datasets presented in this letter can potentially
make an important contribution to global change assessments and models by
addressing one of the most important dimensions of human–environment processes: the
marketplace.
</p></sec-level2><sec-level2 id="erl391689s4.3" label="4.3"><heading>Limitations</heading><p indent="no">As in any global analysis, data available for use in our analysis are subject to the
inconsistencies and other limitations of international socioeconomic data collections
(<cite linkend="erl391689bib71">Verburg <italic>et al</italic> 2011</cite>). Publicly available road data in most regions are
outdated and major extensions have been made to the road system in recent years, perhaps
most notably in China. Also, the level of detail of road maps is inconsistent between
nations, leading to further bias.</p><p>Our use of an arbitrary cut-off for city size does not necessarily reflect the relative
strength and global importance of their markets. Some cities with less than 750 000
inhabitants are important international markets and are disregarded in this analysis.
Also the selection of ports does not fully account for the type of commodities
shipped in the port. Finally, the weight given to respectively large and smaller
market locations was arbitrarily chosen similar to the distance decay coefficient
of the Gaussian function of the accessibility index. Although all choices were
discussed at various workshops and accounted for literature and observations, it is
clear that for different regions such values might best be chosen differently. In
this study it was decided to derive a consistent global measure. A sensitivity
analysis on these underlying assumptions reveals that although locally the patterns
may change the overall global pattern in the market influence index remains the
same. When sub-national data on GDP become more easily available for a larger
range of countries (preferably all of them), it will become possible to specify the
strength of regional markets in more detail based on sub-national GDP levels
(<cite linkend="erl391689bib53">Nordhaus and Chen 2009</cite>).</p><p>In a world where large amounts of money are spent to monitor global biophysical variables
from space, it is remarkable that there is no international effort or institution in place to
annually obtain and share globally consistent, high spatial resolution, socioeconomic data
such as sub-national GDP, demographics, and road data. Global environmental changes are
increasingly driven by the dynamics and spatial patterning of market forces. Given
appropriate data, the simple and straightforward indicators presented here would make it
possible to assess changes in market influence on local environments as rapidly as
infrastructure and GDP data could become available. Under a more advanced regime of
global socioeconomic data gathering and distribution, these maps could be used to
monitor changes in the global patterns of market influence on global environmental
change.
</p></sec-level2><sec-level2 id="erl391689s4.4" label="4.4"><heading>Applications</heading><p indent="no">The three different market indices developed in this letter represent three different aspects
of market influence on land use decisions. Choice of an optimal market index or indices for
a specific application will therefore depend on the purpose of the analysis. The market
access index is the simplest measure, indicating only the degree to which market
access time and the relative scale of markets (large cities versus towns) produces
the global patterns of market influence, independent of total population size or
wealth. This index is therefore the best measure for applications in which the fixed
infrastructure of the marketplace is the main interest (density of market locations
and transportation infrastructure). The market influence index expands on this
infrastructural effect by also incorporating the effects of regional and national variations
in economic power expressed by GDP, making this index the most useful for
investigating the more dynamic and development-related effects of the marketplace, but
without adjusting for variations in population size. The market density index
is the most comprehensive measure, taking into account variations in market
infrastructure, national economic power, and population size, potentially offering the most
useful indicator of the overall influence of the marketplace on land use decisions.
However, in studies that intend to consider variations in population density as an
independent variable, the Market density index should be avoided because it
already includes population density, making Market influence index the better
choice.</p><p>Our results also highlight the large differences between urban and rural regions worldwide.
Many global databases on social and economic characteristics disregard urban/rural
differences by presenting national level GDP as a fixed influence across nations. The new
indicators we present offer the first spatially explicit global approximation of
the distribution of economic assets and influence within and across nations. The
large spatial variations observable in these maps are in themselves drivers of a
wide assortment of global change processes (<cite linkend="erl391689bib20">Grau and Aide 2008</cite>, <cite linkend="erl391689bib43">Mcdonald <italic>et al</italic> 2009</cite>, <cite linkend="erl391689bib68">Thurow and Kilman 2009</cite>). As a
result, these new datasets and indices are the first step toward providing a high
spatial resolution global overview of regional and local disparities in economic
development.</p><p><cite linkend="erl391689bib65">Sanderson <italic>et al</italic> (2002)</cite> mapped a human impact indicator (human
footprint), and <cite linkend="erl391689bib14">Ellis and Ramankutty (2008)</cite> mapped the global patterns
sustained direct human interaction with terrestrial ecosystems (anthromes) using empirical
analyses of existing global data. These global assessments of human influence on local
environments are mere descriptions; unable to fully explain or predict the causes of these
global patterns in the environmental changes driven by human activity. Human
interactions with local environments depend heavily on the state of local economic
development and the level of local interaction with domestic and global markets
(<cite linkend="erl391689bib46">Meyfroidt and Lambin 2009</cite>, <cite linkend="erl391689bib62">Rudel <italic>et al</italic> 2005</cite>). It is
therefore essential that market influence be incorporated in future efforts to map and
classify anthromes and other more dynamic and robust global representations of human
interactions with the environment that drive global environmental change.
</p></sec-level2></sec-level1><acknowledgment><heading>Acknowledgments</heading><p indent="no">We acknowledge ESA and the ESA GlobCover Project led by MEDIAS France/POSTEL
for using the GlobCover data. This letter is based on work funded by the Netherlands
Organization for Scientific Research (NWO). The work presented in this letter contributes
to the Global Land Project (<webref url="http://www.globallandproject.org">www.globallandproject.org</webref>).
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Rev.</jnl-title><volume>27</volume><pages>247–70</pages><crossref><cr_doi>http://dx.doi.org/10.1177/0160017604266026</cr_doi><cr_issn type="print">01600176</cr_issn><cr_issn type="electronic">00000000</cr_issn></crossref></journal-ref></reference-list></references></back></article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="eng"><title>A global assessment of market accessibility and market influence for global environmentalchange studies</title></titleInfo><titleInfo type="abbreviated"><title>A market influence indicator for global change studies</title></titleInfo><titleInfo type="alternative" lang="eng"><title>A global assessment of market accessibility and market influence for global environmental change studies</title></titleInfo><name type="personal"><namePart type="given">Peter H</namePart><namePart type="family">Verburg</namePart><affiliation>Institute for Environmental Studies, Amsterdam Global Change Institute, VU University Amsterdam, De Boelelaan 1087, 1081 HV Amsterdam, The Netherlands</affiliation><affiliation>E-mail: Peter.Verburg@ivm.vu.nl</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Erle C</namePart><namePart type="family">Ellis</namePart><affiliation>Department of Geography & Environmental Systems, University of Maryland, Baltimore County, Baltimore, MD 21250, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Aurelien</namePart><namePart type="family">Letourneau</namePart><affiliation>UMR 5175 Centre dEcologie Fonctionnelle & Evolutive, Centre National de la Recherche Scientifique, 1919 Route de Mende, 34293 Montpellier cedex 5, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="letter">letter</genre><originInfo><publisher>IOP Publishing</publisher><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><note type="production">Printed in the UK</note></physicalDescription><abstract>Markets influence the global patterns of urbanization, deforestation, agriculture and otherland use systems. Yet market influence is rarely incorporated into spatially explicit globalstudies of environmental change, largely because consistent global data are lacking belowthe national level. Here we present the first high spatial resolution gridded data depictingmarket influence globally. The data jointly represent variations in both market strengthand accessibility based on three market influence indices derived from an indexof accessibility to market locations and national level gross domestic product(purchasing power parity). These indices show strong correspondence with humanpopulation density while also revealing several distinct and useful relationships withother global environmental patterns. As market influence grows, the need forhigh resolution global data on market influence and its dynamics will becomeincreasingly important to understanding and forecasting global environmental change.</abstract><subject><genre>keywords</genre><topic>accessibility</topic><topic>land use</topic><topic>environmental change</topic><topic>markets</topic><topic>economy</topic><topic>global</topic><topic>GDP</topic><topic>world</topic></subject><relatedItem type="host"><titleInfo><title>Environmental Research Letters</title></titleInfo><titleInfo type="abbreviated"><title>Environ. Res. Lett.</title></titleInfo><genre type="journal">journal</genre><identifier type="ISSN">1748-9326</identifier><identifier type="eISSN">1748-9326</identifier><identifier type="PublisherID">erl</identifier><identifier type="CODEN">ERLNAL</identifier><identifier type="URL">stacks.iop.org/ERL</identifier><part><date>2011</date><detail type="volume"><caption>vol.</caption><number>6</number></detail><detail type="issue"><caption>no.</caption><number>3</number></detail><extent unit="pages"><start>1</start><end>12</end><total>12</total></extent></part></relatedItem><identifier type="istex">59F8454B1E34A75C0F0345E6CFEB23396D32DCFF</identifier><identifier type="DOI">10.1088/1748-9326/6/3/034019</identifier><identifier type="PII">S1748-9326(11)91689-9</identifier><identifier type="articleID">391689</identifier><identifier type="articleNumber">034019</identifier><accessCondition type="use and reproduction" contentType="copyright">IOP Publishing Ltd</accessCondition><recordInfo><recordContentSource>IOP</recordContentSource><recordOrigin>IOP Publishing Ltd</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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