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Checchietal.InternationalJournalofHealthGeographics2013,12:4 INTERNATIONAL JOURNAL http://www.ij-healthgeographics.com/content/12/1/4 OF HEALTH GEOGRAPHICS RESEARCH Open Access Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations Francesco Checchi1, Barclay T Stewart1, Jennifer J Palmer1 and Chris Grundy2* Abstract Background: Estimating the size of forcibly displaced populationsis key to documenting their plightand allocating sufficient resources to their assistance, butis oftennot done, particularly during the acute phase of displacement, due to methodologicalchallenges and inaccessibility. In this study, weexploredthe potential use ofvery high resolution satellite imagery to remotely estimate forcibly displaced populations. Methods: Our method consisted of multiplying (i)manualcountsofassumed residential structures on a satellite image and (ii)estimatesof themean number of peopleper structure (structure occupancy) obtained from publicly available reports. Wecomputed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons’ camps and two urban neighbourhoods with a mixture of residents and displaced) ranging inpopulation from 1,969to 90,547, and compared these to “gold standard” reference population figures from census or other robustmethods. Results: Structure counts by independentanalystswere reasonably consistent. Betweenone and 11 occupancy reports were available persite and most ofthese reported people per household ratherthan per structure. The imagery-based method had a precision relative to reference population figures of <10% infour sites and 10–30%in threesites, but severely over-estimated the population in an Ethiopian camp with implausible occupancy data and two post-earthquake Haiti sites featuring dense and complex residential layout. For each site, estimates were produced in2–5workingperson-days. Conclusions: In settings withclearly distinguishable individual structures, theremote, imagery-based method had reasonable accuracy for thepurposesof rapid estimation, was simple and quick to implement,and would likely perform better inmore current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern ofroofs and multi-level buildings. Basedon these results, we discuss possibleways forward for the method’sdevelopment. Keywords: Population, Estimation,Refugee, Internally displaced person, Humanitarian, War, Displaced, Satellite imagery, Remote sensing *Correspondence:[email protected] 2FacultyofPublicHealthandPolicy,LondonSchoolofHygieneandTropical Medicine,KeppelSt,LondonWC1E7HT,UnitedKingdom Fulllistofauthorinformationisavailableattheendofthearticle ©2013Checchietal.;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreative CommonsAttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,and reproductioninanymedium,providedtheoriginalworkisproperlycited. Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page2of12 http://www.ij-healthgeographics.com/content/12/1/4 Introduction remoteassessmentsofsheltersandbombdamageduring Currently, an estimated 43 million people worldwide are the final phase of the war in Sri Lanka in 2009 (see forcibly displaced due to armed conflict or other crises; http://www.unitar.org/unosat/ and Kemper et al. [22]). of these, about 10 million are refugees and the remain- Since 2008, Metria, a Swedish company, has supported der internally displaced persons (IDPs) [1]. Knowing the UNHCR in performing repeat manual counts of shelters size of the displaced population in a given site is critical and buildings in the Afgooye IDP corridor in Somalia, tointerpretindicators(e.g.crudedeathrate,severeacute which are combined with UNHCR ground data on malnutrition prevalence, sanitation coverage), effectively population (see http://www.metria.se/Startpage/News1/ allocate resources (e.g. curative health services, vaccines News-1/). Despite the above advances, we could find and other preventive interventions, food, non-food only one study [23] that sought to validate an imagery- items, etc.) and plan mitigation measures to address based population estimate against robust ground popula- added pressure on natural resources due to the arrival of tionestimates;furthermore,whilethisstudyachievedhigh the displaced population [2]. Quantifying the number of accuracy(1%differencebetweentheestimates),theremote displaced people is a mandated function of the United estimateusedgrounddataonpopulationdensity. Nations High Commissioner for Refugees (UNHCR) [3] In this study, we sought to develop a relatively simple, and a common Sphere Standard for Initial Assessment remote analysis method for estimating IDP and refugee [4]. However, in the acute emergency phase of displace- populations based on VHRS imagery. We aimed to val- ment reliable population figures are often not available, idate the method in a variety of sites featuring different and asking the refugee or IDP leadership to perform its settlement patterns, by comparing estimates to available own census may result in bias [5]. Population estimation reference population figures derived by gold standard is not sufficiently prioritised, and expertise in ground- methodsontheground(seebelow). based estimation rarely available in the first days or weeks of displacement events, particularly given that Methods existing statistically robust methods require fairly com- Overviewofthemethod plex sampling and analysis [6-10]. Moreover, many The method we tested consists of the following three current guidelines for relief agencies recommend meth- steps (seebelow):(i)manuallycount allresidentialstruc- ods that are not validated or are based on convenience tures visible on the satellite image and falling within the samples [11]. Decreasing humanitarian access [12] site’s boundaries; (ii) review the published and unpub- means that many displaced populations are intermit- lished literature, including web sources, for estimates of tently accessible: failure to document the size of these the number of people per structure (structure occu- populations may lead to their neglect by governments, pancy) in the site or in similar sites within the same cri- relief agenciesandhumanitarian fundingmechanisms. sis region; and (iii) multiply the structure count by the Satellite imagery is increasingly available and has seen average estimate of structure occupancy from the litera- expanding application in recent emergencies for regional turetoobtain apopulationestimate. level mapping, site planning and vulnerability or damage assessments. Satellite imagery has also seen some use in Selectionofstudysites non-emergency settings to estimate population sizes We tested the method retrospectively in 11 sites [13-17]. Very high spatial resolution (VHSR) images, (Table 1), chosen because they featured (i) an IDP or defined by resolution <4 m, are of particular interest for refugee population; (ii) a reference “gold standard” esti- the data needs of the humanitarian sector, since they en- mate of population size obtained through census, ex- able visualisationofindividualresidentialstructuressuch haustive registration or demographic surveillance (i.e. as tents, huts or other buildings. VHSR sensors are cur- methods that are well-documented and considered rently onboard several orbiting satellites, take frequent robust), and supported by adequate documentation images and can be tasked on request to commission im- (namely a report or protocol providing information on agery of particular sites and time points of interest. Sev- how the gold standard estimate was arrived at); and eral exploratory projects using VHSR imagery for (iii) at least one VHSR satellite image covering the en- mapping IDP or refugee settlements have taken place. tire site, with cloud cover <10%, resolution <70 cm Multiple groups have analysed camps in Tanzania and and taken within one month (stable sites) or one week Darfur and showed that algorithms for automated (acute emergency situations) before/after the date of counting of residential structures can achieve reasonable the reference population estimate. We aimed to in- precision compared to manual methods [18-21]. The clude a range of settlement patterns from different Operational Satellite Applications Programme of the areas of the world. We excluded a priori urban set- United Nations Institute for Training and Research and tings with multi-storey buildings as it was recognised the European Union Joint Research Centre conducted from the start that these would not be suitable for the hC ttphe ://wcch wie wt TSiatebnleam1eDescriptiCoonunotfryst(ruedgyionsi/tceitsy) Settlementtype Crisis pReofpeurelantcioen Sesotuimrcaeteofreference Dreafetereonfce Dsaatteelliotef Notes .ij-healthal.Intern ga Kutupalong Bangladesh(Cox’sBazar Refugeecamp Rohingyarefugees e11st,0im47ate UNHCRregistration e31stDimeact2e009 i2m9aJagne2010 Alsoanalysedamakeshiftcamp eogrational District) fMroymanRmaakrhineState, (unpublisheddata) (seusrtriomuantdeidngpofoprumlaatliorenfu2g0e–e30c,0a0m0p). phicsJourn .ca Breidjing Chad(OuaddaïRegion) Refugeecamp Sudaneserefugees 26,770 UNHCRregistration 31Dec2005 30Jan2006 MédecinsSansFrontières(MSF) omlo fromDarfur (unpublisheddata) demographicsurveillanceestimated /cfH 27,500people[24]. onea Farchana Chad(OuaddaïRegion) Refugeecamp fSruodmanDeasrefurrefugees 19,070 (UuNnHpuCbRlirsehgeidstrdaatitoan) 31Dec2004 11Oct2004 Me>ans1StaiFmlmysdoaisentemdthdaotb1ege6sr.taw2p5eh0eicnpseimuorapvgeleeill[aa2nn4cd].e tent/12/1/4lthGeograph ic Bambu DemocraticRepublicof IDPcamp Insecurityandattacks 5871 UNOPSDataCenter 31Jan2010 29Jan2010 Analysisdatechosentocoincide s2 Congo(NorthKivu againstcivilians forIDPdemographic withpopulationverification 01 Province) surveillance[25] exercise. 3, 1 2 MugungaIII DemocraticRepublicof IDPcamp Insecurityandattacks 1969 UNOPSDataCenter 31Jan2010 29Jan2010 Analysisdatechosentocoincide :4 Congo(NorthKivu againstcivilians forIDPdemographic withpopulationverificationexercise. Province) surveillance[25] Sherkole Ethiopia(Benishangul- Refugeecamp SouthernSudanese 13,958 UNHCRregistration 31Dec2006 8Nov2006 >1monthbetweenimageand GumuzRegion) refugees,mainlyfrom (unpublisheddata) analysisdates. BlueNilestate Shimelba Ethiopia(TigrayRegion) Refugeecamp Eritreanrefugees 13,043 UNHCRregistration 31Dec2006 29Oct2006 >1monthbetweenimageand (unpublisheddata) analysisdates. Champs-de-Mars Haiti(Port-au-Prince InformalIDPcamp Earthquake 23,214 MédecinsSansFrontières/ 22Apr2010 30Apr2010 metropolitanarea) Epicentrecensus[26] Delmas24,Sollino, Haiti(Port-au-Prince Urbanneighbourhoods Earthquake 39,349 MédecinsSansFrontières/ 13May2010 11May2010 FortNational metropolitanarea) withmixtureofresidents Epicentrecensus[26] andinformalIDPs Kakuma Kenya(TurkanaDistrict) Refugeecamp SouthernSudanese, 90,457 UNHCRregistration 31Dec2006 14Jan2007 Somaliandother (unpublisheddata) refugees BairroEsturro Mozambique(Beira Urbanneighbourhood Residualdisplacement 9523 T-squaremethodestimate 15Aug2004 15Aug2004 AnalysedonlysectionofBairro municipality) withfewIDPs fromcivilwar byEpicentre[8] Esturroincludedinreference study.Acensustookplacein Sep2003andyieldedasimilar estimate(9479). P a g e 3 o f 1 2 Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page4of12 http://www.ij-healthgeographics.com/content/12/1/4 simple manual counting approach being tested, due to sized polygons occurring at the periphery or within the difficulty in assessing number of storeys from a defined areas of the site, strongly indicative of relief satellite image. In practice the selection of sites was warehouses, schools, places of worship and government heavily constrained by very limited options considering facilities). Each analyst then decided by eye which cat- our simultaneous requirements for available imagery egory to classify each structure into. Counting was car- and a reference estimate. riedoutinArcGIS10.1(Esri,Redlands,CA,USA). Eight sites were in Sub-Saharan Africa. Six were UNHCR-supported refugee camps (selected based on Structureoccupancyestimatereview information from UNHCR that the camps in question We sought reports published during the 10y prior to had exceptionally reliable registration procedures in each site’s analysis date, and containing an estimate of place), three were IDP camps and two were urban meanresidentialstructureoccupancyormeanhousehold neighbourhoods with a mixture of residents and IDPs size within the site itself; similar sites within the same (Table 1). Sites ranged in population from 1,969 to 90,457 crisis region (e.g. other Sudanese refugee camps in east- (median 13,958). Further descriptions are provided in the ern Chad; IDP camps in the eastern DRC); or for the Additionalfile1. populationcurrentlylivinginthesitebutbeforedisplace- ment. We included such reportsintheanalysis ifthefull Imageryacquisitionandmanualstructurecount text was available and contained primary data on occu- We obtained free ofcharge imagery ofKutupalongcamp pancy; and, for non-camp, urban sites, if the report from the US Department of State, and sourced the re- reflected urban populations only (e.g. for post-earthquake mainder from archives of commercial resellers. The ma- sites in Port-au-Prince, we excluded estimates of occu- jority of images were generated by the WorldView-1, pancyfromruralsites). WorldView-2 and Quickbird satellites, which provide a For each site, we carried out a systematic search for the multi-spectral image resolution of 65 cm. The Haiti abovereports,targetingbothpublishedandgreyliterature. images came from the GeoEye-1 satellite, with a reso- Thesearchstrategywasdesignedtobefeasibleforanana- lution of 50 cm. A mixture of delivery and spectral lyst with moderate skills working under emergency time- options was selected, including 3 band pan-sharpened, lines to generate a population estimate. Details of the 4 band pan-sharpened and 4 band bundle (4 multi- searchstrategyareprovidedintheAdditionalfile1. spectral bands and 1 panchromatic band). Only one So as to deal with multiple estimates for the same site candidate image fulfilling our criteria was available for and quantify the amount and quality of information each site, and therefore the chosen resolution and available, we attributed an “information score” to each image options were dictated by commercial offer. report, computed based on a hierarchy of evidence built We first explored the image, pan-sharpening images along threeattributes (how representative the report was that were supplied as a 4 band bundle, experimenting of the site’s population at the time point analysed; how with basic histogram stretching, and displaying images robust the data collection method was; and whether the in true and false colour where possible. The exact camp estimate reflected the size of households as opposed to outline or section of the image to be counted was residential structures), as shown in Table 2. We com- defined along with the typology of structures and resi- puted the information score for each report by multiply- dential layouts present in the site. We then overlaid ing points for each of the three attributes. Each score is gridlines of 200 m onto the image, which allowed us to therefore outofapossible 1000maximum. Foreachsite, organise the count. After trialling the count procedure we also calculated an “amount of information index” by on 2–3 squares, analysts manually marked structures in summingscoresofallavailable reports. each grid square, proceeding systematically from the top-left corner of the image. We viewed each square in Populationestimation both true and false colour (where available) so as to To compute population estimates, we used a simple ap- maximisethecontrastbetweenstructuresandotherland proach that may be appropriate for the likely skills set of features. We classified observed structures into categor- a remote imagery team and that may facilitate interpret- ies that were appropriate for the site (e.g. traditional ation by agencies. This consisted of multiplying the fol- huts, tents, large buildings, etc.). Counting was done in lowing quantities: (i) the mean of the two independent duplicate, with analysts blinded to each other’s results structurecounts;and(ii)theweightedmeanoftheavail- and to the reference population estimate. Ahead of the able occupancy estimates for the site, with weights given count, the two analysts discussed the image and bythe information score associated with eachoccupancy attempted to agree on which types of structure to estimate (i.e., where n is the total number of occupancy mark as residential and on the definition of large estimates available, x is the estimate from report i and i buildings (typically these were unusually shaped and w isthescoreofreporti). i Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page5of12 http://www.ij-healthgeographics.com/content/12/1/4 Table2Hierarchyofevidenceusedtoassigninformationscorestostructureoccupancyreports Category Points Attribute1:Howrepresentativeofthesite’spopulationandanalysistime-pointisthereportlikelytobe?† Reportfromthesiteitselfduringthecurrentcrisis,asdefinedbydatacollectionhavingtakenplacewithin3yofthedateofanalysis 10 Reportfromsite(s)withinthesamecrisisregionandduringthecurrentcrisis(datacollectionwithin3yofdataofanalysis) 8 Reportfromthesiteitselforsimilarsiteswithinthesamecrisisregion,butfrompreviouscrisisperiods,asdefinedbydatacollectionhaving 4 takenplacepriorto3ybutwithin10yofthedateofanalysis Reportfromthepopulationcurrentlylivinginthesitebutreflectingpre-displacementconditions(e.g.whileresidinginthecountryoforigin), 2 withdatacollectedwithin10yofthedateofanalysis Attribute2:Howrobustisthemethodfordatacollectiononwhichtheestimateisbased? Census,systematicregistrationexerciseorongoingdemographicsurveillance 10 Large(>200householdsforsimple/systematicrandomsampling,>400householdsand>20clustersforclustersampling)samplesurveyor 8 cross-sectionalpopulationsamplewithnoobvioustechnicalflaw(s) Othersamplesurveyorcross-sectionalpopulationsample 6 Estimatefromrapidassessment,sitevisitorreviewofprogrammaticdata 4 Anecdotalorunsubstantiatedestimate 1 Attribute3:Doesthereportquantifythemeanoccupancyofhouseholdsorresidentialstructures? Residentialstructures 10 Households,butthehouseholddefinitionisconsistentwithonehousehold=onestructure(e.g.“peoplesleepingtogetherunderoneroof”) 6 oronehousehold=onecompound(e.g.‘peoplesharingmeals’)insettlementsconsistingofcompoundsorgroupsofstructuresdemarcated byringsorfencesvisibleonthesatelliteimage Households,andthehouseholddefinitioniseitherunclearormaynotbecongruouswiththatofasinglestructure 1 †Ifanoccupancyestimatewascomputedbasedonasampleconsistingofboththesiteandsurroundingsites,withnobreakdownofresultsbysite,we calculatedanaveragescoreforthisattributebasedontheproportionofthesamplefallingwithinthesite.Ifthiswasnotprovided,weattributed2points (theminimum). Results Esturro. There was far more discrepancy in counts for Structurecounts non-residential structures: no agreement was reached Differences in the two independent counts were moder- ahead of analysis on which non-residential structures to ate for residential structures (Table 3), although strong count, as these did not have any influence on the popu- discrepancies were noted for Delmas 24 and Bairro lation estimate; typically, one of the two analysts made Table3Resultsofduplicatestructurecounts,bysiteandassumedtypeofstructure Sitename Residentialstructures(usedfor Otherstructures Totalstructures populationestimate) Count1 Count2 Difference† Count1 Count2 Difference† Count1 Count2 Difference† Kutupalong 371‡ 371‡ 0(0%) 440 444 4(0.9%) 811 815 4(0.5%) Breidjing 5423 6208 785(12.6%) 564 903 339(37.5%) 5987 7111 1124(18.8%) Farchana 4181 3466 715(17.1%) 493 900 407(45.2%) 4674 4366 308(6.6%) Bambu 1501 1380 121(8.1%) 41 50 9(18.0%) 1542 1430 112(7.3%) MugungaIII 588 518 70(11.9%) 2 129 127(98.4%) 590 647 57(8.8%) Sherkole 2643 2746 103(3.8%) 251 217 34(13.5%) 2894 2963 69(2.3%) Shimelba 2500 2604 104(4.0%) 741 408 333(44.9%) 3241 3012 229(7.1%) Champs-de-Mars 2169 2552 383(15.0%) 70 0 70(100.0%) 2239 2552 313(12.3%) Delmas24,Sollino,FortNational 2929 4849 1920(39.6%) 430 472 42(8.9%) 3359 5321 1962(36.9%) Kakuma 16,690¶ 11342 1137(9.1%) 2661¶ 2305 904(39.2%) 19,351¶ 13647 233(1.7%) BairroEsturro 1643 1194 449(27.3%) 242 222 20(8.3%) 1885 1416 469(24.9%) Bothabsoluteandrelativepercentdifferences(inparentheses)betweenthetwocountsarepresented. †Allrelativedifferencesaretheabsolutevalueofthedifferenceincountsdividedbythelargerofthetwocounts. ‡InKutupalongformalcampallvisibleresidentialstructureswereinfactlargemulti-householdsheds,resemblingbarracks. ¶Count2coveredonlyasub-sectionofKakumacamp.Count1forthissamesectionwas12479residential,1401otherand13880totalstructures. Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page6of12 http://www.ij-healthgeographics.com/content/12/1/4 a systematic decision to count or omit very small struc- difficult to identify, as walls or fences around dwellings tures, e.g. probable latrines or showers. There was no created a light-shade contrast that blended with that of apparent correlation between percent agreement and surrounding features. Farchana camp was also mostly the number of structures in the site (data not shown). organised in blocks (Figure 2), but with a more pre- While seven camps were chacterised by a predominant dictable structure of four dwellings per block side and residentialstructuretype,infoursites,Farchana,Shimelba, a less extensive network of fences, yielding a cleaner Delmas24andKakuma,residentialstructuresappearedto image to view. be a mix of different types, usually a combination of trad- Bambucampfeaturedmostlytent-likestructuresofvari- itional huts, tents and small buildings. In these settings able size (Figure 3); these were easily distinguishable from there were considerable discrepancies between analysts as other land features, but tree cover was considerable regards which category each structure was placed in. In (though most tree-shaded dwellings seemed at least partly Farchanacamp,thefirstandsecondcountsidentified3049 visible). Mugunga III had a similar layout but a visually smallhutsand1132tentsversus2386smallhutsand1080 messierimage(Figure3),withsmallwhite-colourareasdif- tents. In Shimelba camp, counts identified 373 traditional ficult to visually classify as tents in current use, tents that hutsand2127housesversus432traditionalhutsand2172 may have been abandoned, debris from previous tents, or houses; while in Delmas 24 1354 tents and 1874 houses merely“noise”(pixelswithnodata). were identified by the first count versus 1575 tents and Apart from an apparently commercial strip, Sherkole 2975 houses by the second (in Kakuma only one category camp mainly consisted of traditional huts (Figure 4). wasusedduringcounting). The challenges were tree cover and in one section dis- In Kutupalong makeshift camp (located all around the tinguishing huts from straw bales in fields. Counting of official camp), 3708 residential structures were addition- Shimelba camp (Figure 4) was also straightforward apart ally counted (not shown in Table 3 as a single analyst from two dense sections difficult to categorise as resi- didthe count forthis site). dentialhutsormarket shops. Champ de Mars featured very high-density tents and Issuesencounteredduringimageanalysis temporary roofing, with no demarcation between As shown in Figure 1, Kutupalong official and makeshift structures other than different roof colours (Figure 5). camps were adjacent. The former featured only one resi- The site also contained considerable tree cover. Fort dential structure type (long, easily distinguishable multi- National, Sollino & Delmas 24 was a mix of tent camps household sheds). The latter was comprised of smaller (some organised and easily countable, others resembling slum-like dwellings, clumped very close together with Champ de Mars), buildings of various size and tents or minimal street separation and a chaotic layout. The tarpaulin sheets located among these buildings, presum- image did not clearly allow individual huts to be identi- ably next to collapsed houses (Figure 5). We found it fied, and surrounding vegetation often looked similar to almost impossible to distinguish collapsed from stand- thesestructures. ing structures and decide what constituted a single- Breidjing camp contained a fairly consistent block lay- household dwelling given the very high density network out (Figure 2), but within these blocks structures were of roofs, chaotic layout and wide variety in building size, shape and colour. Exceptionally large Kakuma camp was a mixture of tented areas, small rectangular dwellings, traditional or slum-like hutsand closely packed buildings, including two orthreecommercialsections.Tentandresidentialhutsec- tions of the camp (Figure 6, left) were straightforward to count,withtownareas(right)beingmorechallenging. The image for Bairro Esturro predominantly showed stand-alone dwellings that were only partly obscured by vegetation (Figure 7). However, the centre of the site (most of Figure 7) featured very high density habitation with no clear boundary between one dwelling and the next. Distinguishing dwellings was difficult initially, partly duetotheircolour similaritytothebackground. Figure1Sections of Kutupalong official (left of black line) Structureoccupancyestimates and makeshift (right of black line) camps. Image copyright The search strategy yielded few eligible reports, ranging DigtalGlobe. from only one to 11 per site (Table 4). A total of 38 Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page7of12 http://www.ij-healthgeographics.com/content/12/1/4 Figure2SectionsofBreidjing(left)andFarchana(right)camps.ImagecopyrightDigtalGlobe. reports were eligible considering all sites (19 of these residential structures with a mean occupancy of 5.3, while were used to populate occupancy estimates for multiple a government census done a year earlier had counted sites within the crisis region). Afull listing is provided in 1,828structureswithanoccupancyof5.1.Wethusunder- theAdditionalfile1.Sixofthe38reportscontaineddata counted residential structures but balanced this with a on one of the sites collected within the past 3y; a further higher occupancy estimate (Table 4). The estimate for five referred to nearby similar sites within the past 3y; Kutupalong makeshift camp (not included in Table 4 due 16 referred to one of the sites or similar sites but at a to the absence of a gold standard reference estimate) was time point 10-3y prior; while 11 were pre-displacement 24,102, compared to various estimates of 20,000 to 30,000 estimates. The vast majority (31/38) were household overtheyear2010[28]. sample surveys, of which 16 were large according to A moderate precision of 10–30% was achieved for our scoring criteria (Table 2). Most reports (28/38) Breidjing, Farchana and Bambu. There was considerable provided an estimate of household size, either not over-estimationforMugungaIII(althoughmodestinabso- defined or with a definition that may not have been lute terms), and severe under-estimation for Sherkole (for consistent with occupying the same structure (e.g. which only one, fairly implausible estimate of occupancy “people eating together”). Only 5/38 reports estimated was available), and for the two Haiti sites. In Champs-de- the mean structure occupancy, while a further five Mars, 4542 shelters were counted by Médecins Sans estimated household size but defined in a way that Frontières census teams, with a mean occupancy of 5.1, was consistent with a residential structure. None of whileinDelmas24thesefigureswere8565and4.6respect- the reports provided information on occupancy by type ively,illustratingtheextentofourunder-count(Table4). of structure. Efficiency Populationestimatesandvalidation As shown in Table 5, the total person-time required to Theimagery-basedmethodachievedagooddegreeofpre- implement the method fully was reasonably consistent cision(relativedifferencecomparedtothereferencepopu- across sites, ranging from 16 to 42 person-hours, i.e. lation <10%) for Kutupalong, Shimelba, Kakuma and about 2 to 5 working days of a single analyst. Most Bairro Esturro (Table 4). In Bairro Esturro, The T-Square person-time (47%) was devoted to searching for occu- methodsurvey(apopulationestimationmethodthatcom- pancy reports. Preparing images and counting structures bines area sampling with average distance between struc- took up 17% and 24% of the total person-time, respect- tures and occupancy questionnaires [8]) on which the ively. Countingtime percapitapopulation wasalso fairly reference estimate was based yielded a figure of 1,685 consistent, ranging from 0.19 to 0.51 hours per 1000 Figure3SectionsofBambu(left)andMugungaIII(right)camps.ImagecopyrightDigtalGlobe. Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page8of12 http://www.ij-healthgeographics.com/content/12/1/4 Figure4SectionsofSherkole(left)andShimelba(right)camps.ImagecopyrightDigtalGlobe. population (mean 0.30): accordingly, a site of 100,000 to be explicitly emphasised when presenting this as an people mightbeprojected torequireabout 30hours. option forrapid estimation. Remote analysis appears feasible in terms of human Discussion resources and financial inputs: in our study, it required To our knowledge, this is the first study to have evalu- 2–5 days, two analysts and, apart from salary and office ated the validity of IDP or refugee population estima- expenditures, only minimal imagery procurement costs tion based on satellite imagery in a variety of different (15 to 25 USD per km2, though these costs would be sites and phases of displacement. Our findings suggest somewhathigher ifimageswere commissioned). that a remote analysis approach relying on manual Visual analysis of the imagery was not overly compli- counting of structures and published occupancy esti- cated,despite mostanalysts inthis studyhavingno prior mates can achieve reasonable precision in sites where GIS skills. However, the visual quality and complexity of individual structures are distinguishable and neither the image were critical determinants of both speed and clouds nor vegetation pose a significant barrier to vis- accuracy of counting. While experienced spatial analysts ual analysis. may be able to improve image quality by using various The method’s performance on the whole suggests that, techniques that enhance the visibility of features, we rather than referring to it as a valid approach, one could wished to evaluate use of the method by analysts with consider it “good enough” for certain purposes, assum- limited GIS skills, and thus refrained from making such ing that no robust ground estimation is possible within improvements to the images. Moreover, in many the same timeframe. Specifically, while inaccuracy of up instances the very typology and layout of structures (e.g. to 30% is probably unacceptable in post-acute emer- multiple walls, structures connected to each other and gency scenarios where resources for on the ground removal or abandonment of structures) imposed a limit population tracking are present, we believe that for the on accuracy that, given the present resolution of com- purposesofinitial planning (e.g.vaccination, distribution mercially available imagery, is likely to remain to some of food and non-food items, emergency water and sani- extent intractable (see Conclusions). However, having tation provision), this level of inaccuracy is a substantial four or more bands in the multi-spectral image did help improvement over no information or guesswork, which in a few cases to distinguish between vegetation and might be the case if the site is inaccessible or if expertise man-made structures when the latter were constructed in ground estimation cannot be sourced. However, the out of different materials, and we believe therefore that expected level of inaccuracy of the method would have these options should always be selected when obtaining Figure5SectionsofChampsdeMarscamp(left)andDelmas24,SollinoandFortNationalneighbourhoods(right).Imagecopyright GeoEyeInc. Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page9of12 http://www.ij-healthgeographics.com/content/12/1/4 Figure6SectionsofKakumacamp.ImagecopyrightDigtalGlobe. imagery. Beyond these challenges, availability of cloud remoteperiodsmayactuallyhaveincreasedinaccuracy(of free images may be a serious constraint in some loca- note,siteswiththehighestinformationscoredidnothave tions, particularly when a very short delay between the the greatest accuracy). It is likely that this may partly ex- analysis and image time point is needed (i.e. in dynamic, plain why sites with quality images and simple structure evolving situations): for example, in DRC we excluded layouts (Farchana, Bambu) did not perform as well as several candidate sites for analysis because no cloud free expected. This limitation could be addressed by carrying images were available. out a small, rapid structure occupancy survey to provide While some occupancy data were available for each locally appropriate data, but this is only an option if the site, the literature search was onerous and had a low site is accessible and diminishes the method’s relative yield. For one site (Sherkole), the sole estimate available advantages over other options. We tested such an ap- was clearly implausible and resulted in an under- proachinChad(paperforthcoming). estimateofpopulation.In general,we found very few ac- The above drawbacks are partly a result of our choice tual structure occupancy estimates, and had to rely to investigate a simple, manual method designed to em- instead onhouseholdsizefigures. Thesewere sometimes powernon-specialiststocarryoutpopulationestimation. fairlydivergentwithinthesamesite(seeAdditionalfile1), Automated counting methods would necessitate a far andtheirsparsitymadeitdifficulttoconstructstatistically higherskillslevelandthusrequireinputbycentresofex- meaningful confidence intervals around the population cellence in remote sensing. While a review of automated estimates. The hierarchy of evidence for structure occu- or semi-automated methods is beyond the scope of this pancy information that we used to attribute weights to paper, this is an area of vibrant research, and we believe eachreport(Table2)isanattempt torelyonall informa- that these methods have a considerably larger potential tionavailablewhileminimisinglikelybias,butcriteriaand for improvement than manual analysis. Automation scores used in this hierarchy are ultimately arbitrary and would prove particularly valuable in scenarios where can never fully reflect the actual validity of any individual population estimates need to be updated frequently to estimate.Occupancyisknowntofluctuateovertime,par- track displacement dynamics, and could perhaps provide ticularly in situations of protracted crisis, and thus to a solution for urban areas in which the manual method some extent our decision to include data from fairly mayneverperformasaccuratelyasneeded. Studylimitations Our findings should be considered conservative, as they reflect application of the method in a more challenging setofconditionsthan wouldbethe caseinmore current application by an agency with a recognised mandate. In prospectiveapplicationofthemethod,itmaybepossible to commission new imagery, thereby ensuring a minimal time difference between the analysis and imagery time points, though again subject to constraintssuch as cloud cover. Contemporary sensors increasingly have wider spectral ranges, allowing various false colour combina- tions so as to maximise the contrast between structures and other landscape features. While costs of imagery are already reasonable, it is also likely that in future crises Figure7SectionofBairroEsturro.ImagecopyrightDigtalGlobe. VHSR imagery will be reducing in cost and, in certain Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page10of12 http://www.ij-healthgeographics.com/content/12/1/4 Table4Populationestimatesbasedonremoteimageryanalysisandthereferencemethod Sitename Resultsfromimagery-basedmethod Reference Absolutedifference “Amountofinformation“ population (relative%)† index(numberofreports) Meanstructure Weightedmeanstructure Estimated estimate count occupancy population Kutupalong 371 6.5x5‡=32.5 12,058 11,047 +1011(+9.2%) 789(6) Breidjing 5816 6.0 34,896 26,770 +8126(+30.4%) 800(3) Farchana 3824 6.0 22,944 19,070 +3874(+20.3%) 808(3) Bambu 1441 5.3 7637 5871 +1766(+30.1%) 452(11) MugungaIII 553 5.4 2986 1969 +1017(+51.7%) 436(11) Sherkole 2695 3.1 8355 13,958 −5603(−40.1%) 16(1) Shimelba 2552 4.7 11,994 13,043 −1049(−8.0%) 60(1) Champs-de-Mars 2361 5.3 12,513 23,214 −10,701(−46.1%) 880(5) Delmas24,Sollino, 3889 5.3 20,612 39,349 −18,737(−47.6%) 880(5) FortNational Kakuma 16,690¶ 5.3 88,457 90,457 −2000(−2.2%) 428(4) BairroEsturro 1419 6.3 8940 9523 −583(−6.1%) 252(7) †Relativedifferencecomputedasthedifferencebetweenestimatesdividedbyreferenceestimate. ‡Averageof5familiespershedasreportedbyFeeny[27]. ¶Onlycount1wasconsidered. largescaleemergencies,beavailablefreeofcostaswasthe though data requests would have to be of limited bur- case in Haiti. Photographs taken by unmanned drones or den to field workers (e.g. if email contact with anyone other aircraft could also be used, particularly if agencies familiar with the site is possible, sample image screen- pooltogetherresourcestoobtainsuchimages.However,it grabs could be shared with the field to help define the shouldbenotedthatcoordinationofagenciesaroundpro- nature of certain areas or structures); in the early phase curement and use of satellite imagery, as well as, more of displacement, it is likely that there would be few broadly, sharing of resources for timely assessment and non-residential structures, thus simplifying the count; monitoring, has proven challenging in a variety of recent furthermore, the bank of occupancy reports available emergencies, and may remain so unless a clear mandate for any given site would probably be larger, as current andresourcesareattributedtoaleadagency. emergencies benefit from more frequent assessments In routine practice, it is likely that some real-time and household surveys, with a greater proportion of ground information may be available to the analyst, reports made available online; further reports could also Table5Person-timeinputs(hours)forthevariousstepsofthepopulationestimationmethod,byactivityandsite Sitename Timeperactivity(person-hours) Total Countingtime person- per1000 Obtain Prepare Count Occupancy Review Compute imagery imagery† structures datasearch occupancy population hours population‡ Kutupalong 1.75 7.50 4.50 4.75 0.50 0.25 19.25 0.41 Breidjing 2.75 1.75 5.00 7.50 0.25 0.25 17.50 0.19 Farchana 1.50 1.50 4.75 7.50 0.25 0.25 15.75 0.25 Bambu 1.25 3.00 1.25 15.25 0.75 0.25 21.75 0.21 MugungaIII 1.25 0.75 1.00 15.50 0.75 0.25 19.50 0.51 Sherkole 1.00 1.25 3.75 10.25 0.25 0.25 16.75 0.27 Shimelba 1.00 1.00 4.00 10.50 0.25 0.25 17.00 0.31 Champs-de-Mars 4.25 7.50 7.50 13.50 0.50 0.25 33.50 0.32 Delmas24,Sollino, 4.50 6.75 12.00 13.00 0.50 0.25 37.00 0.30 FortNational Kakuma 2.75 8.50 20.75 12.25 0.25 0.25 44.75 0.23 BairroEsturro 6.25 9.50 2.75 23.00 0.75 0.25 42.50 0.29 †Workrequiredtopreparetheimageforcounting,includingpan-sharpeningifneeded,creatingthecampoutlineandthe200mgrid. ‡Basedonreferencepopulationestimate.Onlyperson-timeforstructurecountingisconsidered,asallotheractivitiesarelessdependentonthesite’spopulationsize.

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Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations. PDF

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Checchietal.InternationalJournalofHealthGeographics2013,12:4 INTERNATIONAL JOURNAL http://www.ij-healthgeographics.com/content/12/1/4 OF HEALTH GEOGRAPHICS RESEARCH Open Access Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations Francesco Checchi1, Barclay T Stewart1, Jennifer J Palmer1 and Chris Grundy2* Abstract Background: Estimating the size of forcibly displaced populationsis key to documenting their plightand allocating sufficient resources to their assistance, butis oftennot done, particularly during the acute phase of displacement, due to methodologicalchallenges and inaccessibility. In this study, weexploredthe potential use ofvery high resolution satellite imagery to remotely estimate forcibly displaced populations. Methods: Our method consisted of multiplying (i)manualcountsofassumed residential structures on a satellite image and (ii)estimatesof themean number of peopleper structure (structure occupancy) obtained from publicly available reports. Wecomputed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons’ camps and two urban neighbourhoods with a mixture of residents and displaced) ranging inpopulation from 1,969to 90,547, and compared these to “gold standard” reference population figures from census or other robustmethods. Results: Structure counts by independentanalystswere reasonably consistent. Betweenone and 11 occupancy reports were available persite and most ofthese reported people per household ratherthan per structure. The imagery-based method had a precision relative to reference population figures of <10% infour sites and 10–30%in threesites, but severely over-estimated the population in an Ethiopian camp with implausible occupancy data and two post-earthquake Haiti sites featuring dense and complex residential layout. For each site, estimates were produced in2–5workingperson-days. Conclusions: In settings withclearly distinguishable individual structures, theremote, imagery-based method had reasonable accuracy for thepurposesof rapid estimation, was simple and quick to implement,and would likely perform better inmore current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern ofroofs and multi-level buildings. Basedon these results, we discuss possibleways forward for the method’sdevelopment. Keywords: Population, Estimation,Refugee, Internally displaced person, Humanitarian, War, Displaced, Satellite imagery, Remote sensing *Correspondence:[email protected] 2FacultyofPublicHealthandPolicy,LondonSchoolofHygieneandTropical Medicine,KeppelSt,LondonWC1E7HT,UnitedKingdom Fulllistofauthorinformationisavailableattheendofthearticle ©2013Checchietal.;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreative CommonsAttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,and reproductioninanymedium,providedtheoriginalworkisproperlycited. Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page2of12 http://www.ij-healthgeographics.com/content/12/1/4 Introduction remoteassessmentsofsheltersandbombdamageduring Currently, an estimated 43 million people worldwide are the final phase of the war in Sri Lanka in 2009 (see forcibly displaced due to armed conflict or other crises; http://www.unitar.org/unosat/ and Kemper et al. [22]). of these, about 10 million are refugees and the remain- Since 2008, Metria, a Swedish company, has supported der internally displaced persons (IDPs) [1]. Knowing the UNHCR in performing repeat manual counts of shelters size of the displaced population in a given site is critical and buildings in the Afgooye IDP corridor in Somalia, tointerpretindicators(e.g.crudedeathrate,severeacute which are combined with UNHCR ground data on malnutrition prevalence, sanitation coverage), effectively population (see http://www.metria.se/Startpage/News1/ allocate resources (e.g. curative health services, vaccines News-1/). Despite the above advances, we could find and other preventive interventions, food, non-food only one study [23] that sought to validate an imagery- items, etc.) and plan mitigation measures to address based population estimate against robust ground popula- added pressure on natural resources due to the arrival of tionestimates;furthermore,whilethisstudyachievedhigh the displaced population [2]. Quantifying the number of accuracy(1%differencebetweentheestimates),theremote displaced people is a mandated function of the United estimateusedgrounddataonpopulationdensity. Nations High Commissioner for Refugees (UNHCR) [3] In this study, we sought to develop a relatively simple, and a common Sphere Standard for Initial Assessment remote analysis method for estimating IDP and refugee [4]. However, in the acute emergency phase of displace- populations based on VHRS imagery. We aimed to val- ment reliable population figures are often not available, idate the method in a variety of sites featuring different and asking the refugee or IDP leadership to perform its settlement patterns, by comparing estimates to available own census may result in bias [5]. Population estimation reference population figures derived by gold standard is not sufficiently prioritised, and expertise in ground- methodsontheground(seebelow). based estimation rarely available in the first days or weeks of displacement events, particularly given that Methods existing statistically robust methods require fairly com- Overviewofthemethod plex sampling and analysis [6-10]. Moreover, many The method we tested consists of the following three current guidelines for relief agencies recommend meth- steps (seebelow):(i)manuallycount allresidentialstruc- ods that are not validated or are based on convenience tures visible on the satellite image and falling within the samples [11]. Decreasing humanitarian access [12] site’s boundaries; (ii) review the published and unpub- means that many displaced populations are intermit- lished literature, including web sources, for estimates of tently accessible: failure to document the size of these the number of people per structure (structure occu- populations may lead to their neglect by governments, pancy) in the site or in similar sites within the same cri- relief agenciesandhumanitarian fundingmechanisms. sis region; and (iii) multiply the structure count by the Satellite imagery is increasingly available and has seen average estimate of structure occupancy from the litera- expanding application in recent emergencies for regional turetoobtain apopulationestimate. level mapping, site planning and vulnerability or damage assessments. Satellite imagery has also seen some use in Selectionofstudysites non-emergency settings to estimate population sizes We tested the method retrospectively in 11 sites [13-17]. Very high spatial resolution (VHSR) images, (Table 1), chosen because they featured (i) an IDP or defined by resolution <4 m, are of particular interest for refugee population; (ii) a reference “gold standard” esti- the data needs of the humanitarian sector, since they en- mate of population size obtained through census, ex- able visualisationofindividualresidentialstructuressuch haustive registration or demographic surveillance (i.e. as tents, huts or other buildings. VHSR sensors are cur- methods that are well-documented and considered rently onboard several orbiting satellites, take frequent robust), and supported by adequate documentation images and can be tasked on request to commission im- (namely a report or protocol providing information on agery of particular sites and time points of interest. Sev- how the gold standard estimate was arrived at); and eral exploratory projects using VHSR imagery for (iii) at least one VHSR satellite image covering the en- mapping IDP or refugee settlements have taken place. tire site, with cloud cover <10%, resolution <70 cm Multiple groups have analysed camps in Tanzania and and taken within one month (stable sites) or one week Darfur and showed that algorithms for automated (acute emergency situations) before/after the date of counting of residential structures can achieve reasonable the reference population estimate. We aimed to in- precision compared to manual methods [18-21]. The clude a range of settlement patterns from different Operational Satellite Applications Programme of the areas of the world. We excluded a priori urban set- United Nations Institute for Training and Research and tings with multi-storey buildings as it was recognised the European Union Joint Research Centre conducted from the start that these would not be suitable for the hC ttphe ://wcch wie wt TSiatebnleam1eDescriptiCoonunotfryst(ruedgyionsi/tceitsy) Settlementtype Crisis pReofpeurelantcioen Sesotuimrcaeteofreference Dreafetereonfce Dsaatteelliotef Notes .ij-healthal.Intern ga Kutupalong Bangladesh(Cox’sBazar Refugeecamp Rohingyarefugees e11st,0im47ate UNHCRregistration e31stDimeact2e009 i2m9aJagne2010 Alsoanalysedamakeshiftcamp eogrational District) fMroymanRmaakrhineState, (unpublisheddata) (seusrtriomuantdeidngpofoprumlaatliorenfu2g0e–e30c,0a0m0p). phicsJourn .ca Breidjing Chad(OuaddaïRegion) Refugeecamp Sudaneserefugees 26,770 UNHCRregistration 31Dec2005 30Jan2006 MédecinsSansFrontières(MSF) omlo fromDarfur (unpublisheddata) demographicsurveillanceestimated /cfH 27,500people[24]. onea Farchana Chad(OuaddaïRegion) Refugeecamp fSruodmanDeasrefurrefugees 19,070 (UuNnHpuCbRlirsehgeidstrdaatitoan) 31Dec2004 11Oct2004 Me>ans1StaiFmlmysdoaisentemdthdaotb1ege6sr.taw2p5eh0eicnpseimuorapvgeleeill[aa2nn4cd].e tent/12/1/4lthGeograph ic Bambu DemocraticRepublicof IDPcamp Insecurityandattacks 5871 UNOPSDataCenter 31Jan2010 29Jan2010 Analysisdatechosentocoincide s2 Congo(NorthKivu againstcivilians forIDPdemographic withpopulationverification 01 Province) surveillance[25] exercise. 3, 1 2 MugungaIII DemocraticRepublicof IDPcamp Insecurityandattacks 1969 UNOPSDataCenter 31Jan2010 29Jan2010 Analysisdatechosentocoincide :4 Congo(NorthKivu againstcivilians forIDPdemographic withpopulationverificationexercise. Province) surveillance[25] Sherkole Ethiopia(Benishangul- Refugeecamp SouthernSudanese 13,958 UNHCRregistration 31Dec2006 8Nov2006 >1monthbetweenimageand GumuzRegion) refugees,mainlyfrom (unpublisheddata) analysisdates. BlueNilestate Shimelba Ethiopia(TigrayRegion) Refugeecamp Eritreanrefugees 13,043 UNHCRregistration 31Dec2006 29Oct2006 >1monthbetweenimageand (unpublisheddata) analysisdates. Champs-de-Mars Haiti(Port-au-Prince InformalIDPcamp Earthquake 23,214 MédecinsSansFrontières/ 22Apr2010 30Apr2010 metropolitanarea) Epicentrecensus[26] Delmas24,Sollino, Haiti(Port-au-Prince Urbanneighbourhoods Earthquake 39,349 MédecinsSansFrontières/ 13May2010 11May2010 FortNational metropolitanarea) withmixtureofresidents Epicentrecensus[26] andinformalIDPs Kakuma Kenya(TurkanaDistrict) Refugeecamp SouthernSudanese, 90,457 UNHCRregistration 31Dec2006 14Jan2007 Somaliandother (unpublisheddata) refugees BairroEsturro Mozambique(Beira Urbanneighbourhood Residualdisplacement 9523 T-squaremethodestimate 15Aug2004 15Aug2004 AnalysedonlysectionofBairro municipality) withfewIDPs fromcivilwar byEpicentre[8] Esturroincludedinreference study.Acensustookplacein Sep2003andyieldedasimilar estimate(9479). P a g e 3 o f 1 2 Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page4of12 http://www.ij-healthgeographics.com/content/12/1/4 simple manual counting approach being tested, due to sized polygons occurring at the periphery or within the difficulty in assessing number of storeys from a defined areas of the site, strongly indicative of relief satellite image. In practice the selection of sites was warehouses, schools, places of worship and government heavily constrained by very limited options considering facilities). Each analyst then decided by eye which cat- our simultaneous requirements for available imagery egory to classify each structure into. Counting was car- and a reference estimate. riedoutinArcGIS10.1(Esri,Redlands,CA,USA). Eight sites were in Sub-Saharan Africa. Six were UNHCR-supported refugee camps (selected based on Structureoccupancyestimatereview information from UNHCR that the camps in question We sought reports published during the 10y prior to had exceptionally reliable registration procedures in each site’s analysis date, and containing an estimate of place), three were IDP camps and two were urban meanresidentialstructureoccupancyormeanhousehold neighbourhoods with a mixture of residents and IDPs size within the site itself; similar sites within the same (Table 1). Sites ranged in population from 1,969 to 90,457 crisis region (e.g. other Sudanese refugee camps in east- (median 13,958). Further descriptions are provided in the ern Chad; IDP camps in the eastern DRC); or for the Additionalfile1. populationcurrentlylivinginthesitebutbeforedisplace- ment. We included such reportsintheanalysis ifthefull Imageryacquisitionandmanualstructurecount text was available and contained primary data on occu- We obtained free ofcharge imagery ofKutupalongcamp pancy; and, for non-camp, urban sites, if the report from the US Department of State, and sourced the re- reflected urban populations only (e.g. for post-earthquake mainder from archives of commercial resellers. The ma- sites in Port-au-Prince, we excluded estimates of occu- jority of images were generated by the WorldView-1, pancyfromruralsites). WorldView-2 and Quickbird satellites, which provide a For each site, we carried out a systematic search for the multi-spectral image resolution of 65 cm. The Haiti abovereports,targetingbothpublishedandgreyliterature. images came from the GeoEye-1 satellite, with a reso- Thesearchstrategywasdesignedtobefeasibleforanana- lution of 50 cm. A mixture of delivery and spectral lyst with moderate skills working under emergency time- options was selected, including 3 band pan-sharpened, lines to generate a population estimate. Details of the 4 band pan-sharpened and 4 band bundle (4 multi- searchstrategyareprovidedintheAdditionalfile1. spectral bands and 1 panchromatic band). Only one So as to deal with multiple estimates for the same site candidate image fulfilling our criteria was available for and quantify the amount and quality of information each site, and therefore the chosen resolution and available, we attributed an “information score” to each image options were dictated by commercial offer. report, computed based on a hierarchy of evidence built We first explored the image, pan-sharpening images along threeattributes (how representative the report was that were supplied as a 4 band bundle, experimenting of the site’s population at the time point analysed; how with basic histogram stretching, and displaying images robust the data collection method was; and whether the in true and false colour where possible. The exact camp estimate reflected the size of households as opposed to outline or section of the image to be counted was residential structures), as shown in Table 2. We com- defined along with the typology of structures and resi- puted the information score for each report by multiply- dential layouts present in the site. We then overlaid ing points for each of the three attributes. Each score is gridlines of 200 m onto the image, which allowed us to therefore outofapossible 1000maximum. Foreachsite, organise the count. After trialling the count procedure we also calculated an “amount of information index” by on 2–3 squares, analysts manually marked structures in summingscoresofallavailable reports. each grid square, proceeding systematically from the top-left corner of the image. We viewed each square in Populationestimation both true and false colour (where available) so as to To compute population estimates, we used a simple ap- maximisethecontrastbetweenstructuresandotherland proach that may be appropriate for the likely skills set of features. We classified observed structures into categor- a remote imagery team and that may facilitate interpret- ies that were appropriate for the site (e.g. traditional ation by agencies. This consisted of multiplying the fol- huts, tents, large buildings, etc.). Counting was done in lowing quantities: (i) the mean of the two independent duplicate, with analysts blinded to each other’s results structurecounts;and(ii)theweightedmeanoftheavail- and to the reference population estimate. Ahead of the able occupancy estimates for the site, with weights given count, the two analysts discussed the image and bythe information score associated with eachoccupancy attempted to agree on which types of structure to estimate (i.e., where n is the total number of occupancy mark as residential and on the definition of large estimates available, x is the estimate from report i and i buildings (typically these were unusually shaped and w isthescoreofreporti). i Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page5of12 http://www.ij-healthgeographics.com/content/12/1/4 Table2Hierarchyofevidenceusedtoassigninformationscorestostructureoccupancyreports Category Points Attribute1:Howrepresentativeofthesite’spopulationandanalysistime-pointisthereportlikelytobe?† Reportfromthesiteitselfduringthecurrentcrisis,asdefinedbydatacollectionhavingtakenplacewithin3yofthedateofanalysis 10 Reportfromsite(s)withinthesamecrisisregionandduringthecurrentcrisis(datacollectionwithin3yofdataofanalysis) 8 Reportfromthesiteitselforsimilarsiteswithinthesamecrisisregion,butfrompreviouscrisisperiods,asdefinedbydatacollectionhaving 4 takenplacepriorto3ybutwithin10yofthedateofanalysis Reportfromthepopulationcurrentlylivinginthesitebutreflectingpre-displacementconditions(e.g.whileresidinginthecountryoforigin), 2 withdatacollectedwithin10yofthedateofanalysis Attribute2:Howrobustisthemethodfordatacollectiononwhichtheestimateisbased? Census,systematicregistrationexerciseorongoingdemographicsurveillance 10 Large(>200householdsforsimple/systematicrandomsampling,>400householdsand>20clustersforclustersampling)samplesurveyor 8 cross-sectionalpopulationsamplewithnoobvioustechnicalflaw(s) Othersamplesurveyorcross-sectionalpopulationsample 6 Estimatefromrapidassessment,sitevisitorreviewofprogrammaticdata 4 Anecdotalorunsubstantiatedestimate 1 Attribute3:Doesthereportquantifythemeanoccupancyofhouseholdsorresidentialstructures? Residentialstructures 10 Households,butthehouseholddefinitionisconsistentwithonehousehold=onestructure(e.g.“peoplesleepingtogetherunderoneroof”) 6 oronehousehold=onecompound(e.g.‘peoplesharingmeals’)insettlementsconsistingofcompoundsorgroupsofstructuresdemarcated byringsorfencesvisibleonthesatelliteimage Households,andthehouseholddefinitioniseitherunclearormaynotbecongruouswiththatofasinglestructure 1 †Ifanoccupancyestimatewascomputedbasedonasampleconsistingofboththesiteandsurroundingsites,withnobreakdownofresultsbysite,we calculatedanaveragescoreforthisattributebasedontheproportionofthesamplefallingwithinthesite.Ifthiswasnotprovided,weattributed2points (theminimum). Results Esturro. There was far more discrepancy in counts for Structurecounts non-residential structures: no agreement was reached Differences in the two independent counts were moder- ahead of analysis on which non-residential structures to ate for residential structures (Table 3), although strong count, as these did not have any influence on the popu- discrepancies were noted for Delmas 24 and Bairro lation estimate; typically, one of the two analysts made Table3Resultsofduplicatestructurecounts,bysiteandassumedtypeofstructure Sitename Residentialstructures(usedfor Otherstructures Totalstructures populationestimate) Count1 Count2 Difference† Count1 Count2 Difference† Count1 Count2 Difference† Kutupalong 371‡ 371‡ 0(0%) 440 444 4(0.9%) 811 815 4(0.5%) Breidjing 5423 6208 785(12.6%) 564 903 339(37.5%) 5987 7111 1124(18.8%) Farchana 4181 3466 715(17.1%) 493 900 407(45.2%) 4674 4366 308(6.6%) Bambu 1501 1380 121(8.1%) 41 50 9(18.0%) 1542 1430 112(7.3%) MugungaIII 588 518 70(11.9%) 2 129 127(98.4%) 590 647 57(8.8%) Sherkole 2643 2746 103(3.8%) 251 217 34(13.5%) 2894 2963 69(2.3%) Shimelba 2500 2604 104(4.0%) 741 408 333(44.9%) 3241 3012 229(7.1%) Champs-de-Mars 2169 2552 383(15.0%) 70 0 70(100.0%) 2239 2552 313(12.3%) Delmas24,Sollino,FortNational 2929 4849 1920(39.6%) 430 472 42(8.9%) 3359 5321 1962(36.9%) Kakuma 16,690¶ 11342 1137(9.1%) 2661¶ 2305 904(39.2%) 19,351¶ 13647 233(1.7%) BairroEsturro 1643 1194 449(27.3%) 242 222 20(8.3%) 1885 1416 469(24.9%) Bothabsoluteandrelativepercentdifferences(inparentheses)betweenthetwocountsarepresented. †Allrelativedifferencesaretheabsolutevalueofthedifferenceincountsdividedbythelargerofthetwocounts. ‡InKutupalongformalcampallvisibleresidentialstructureswereinfactlargemulti-householdsheds,resemblingbarracks. ¶Count2coveredonlyasub-sectionofKakumacamp.Count1forthissamesectionwas12479residential,1401otherand13880totalstructures. Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page6of12 http://www.ij-healthgeographics.com/content/12/1/4 a systematic decision to count or omit very small struc- difficult to identify, as walls or fences around dwellings tures, e.g. probable latrines or showers. There was no created a light-shade contrast that blended with that of apparent correlation between percent agreement and surrounding features. Farchana camp was also mostly the number of structures in the site (data not shown). organised in blocks (Figure 2), but with a more pre- While seven camps were chacterised by a predominant dictable structure of four dwellings per block side and residentialstructuretype,infoursites,Farchana,Shimelba, a less extensive network of fences, yielding a cleaner Delmas24andKakuma,residentialstructuresappearedto image to view. be a mix of different types, usually a combination of trad- Bambucampfeaturedmostlytent-likestructuresofvari- itional huts, tents and small buildings. In these settings able size (Figure 3); these were easily distinguishable from there were considerable discrepancies between analysts as other land features, but tree cover was considerable regards which category each structure was placed in. In (though most tree-shaded dwellings seemed at least partly Farchanacamp,thefirstandsecondcountsidentified3049 visible). Mugunga III had a similar layout but a visually smallhutsand1132tentsversus2386smallhutsand1080 messierimage(Figure3),withsmallwhite-colourareasdif- tents. In Shimelba camp, counts identified 373 traditional ficult to visually classify as tents in current use, tents that hutsand2127housesversus432traditionalhutsand2172 may have been abandoned, debris from previous tents, or houses; while in Delmas 24 1354 tents and 1874 houses merely“noise”(pixelswithnodata). were identified by the first count versus 1575 tents and Apart from an apparently commercial strip, Sherkole 2975 houses by the second (in Kakuma only one category camp mainly consisted of traditional huts (Figure 4). wasusedduringcounting). The challenges were tree cover and in one section dis- In Kutupalong makeshift camp (located all around the tinguishing huts from straw bales in fields. Counting of official camp), 3708 residential structures were addition- Shimelba camp (Figure 4) was also straightforward apart ally counted (not shown in Table 3 as a single analyst from two dense sections difficult to categorise as resi- didthe count forthis site). dentialhutsormarket shops. Champ de Mars featured very high-density tents and Issuesencounteredduringimageanalysis temporary roofing, with no demarcation between As shown in Figure 1, Kutupalong official and makeshift structures other than different roof colours (Figure 5). camps were adjacent. The former featured only one resi- The site also contained considerable tree cover. Fort dential structure type (long, easily distinguishable multi- National, Sollino & Delmas 24 was a mix of tent camps household sheds). The latter was comprised of smaller (some organised and easily countable, others resembling slum-like dwellings, clumped very close together with Champ de Mars), buildings of various size and tents or minimal street separation and a chaotic layout. The tarpaulin sheets located among these buildings, presum- image did not clearly allow individual huts to be identi- ably next to collapsed houses (Figure 5). We found it fied, and surrounding vegetation often looked similar to almost impossible to distinguish collapsed from stand- thesestructures. ing structures and decide what constituted a single- Breidjing camp contained a fairly consistent block lay- household dwelling given the very high density network out (Figure 2), but within these blocks structures were of roofs, chaotic layout and wide variety in building size, shape and colour. Exceptionally large Kakuma camp was a mixture of tented areas, small rectangular dwellings, traditional or slum-like hutsand closely packed buildings, including two orthreecommercialsections.Tentandresidentialhutsec- tions of the camp (Figure 6, left) were straightforward to count,withtownareas(right)beingmorechallenging. The image for Bairro Esturro predominantly showed stand-alone dwellings that were only partly obscured by vegetation (Figure 7). However, the centre of the site (most of Figure 7) featured very high density habitation with no clear boundary between one dwelling and the next. Distinguishing dwellings was difficult initially, partly duetotheircolour similaritytothebackground. Figure1Sections of Kutupalong official (left of black line) Structureoccupancyestimates and makeshift (right of black line) camps. Image copyright The search strategy yielded few eligible reports, ranging DigtalGlobe. from only one to 11 per site (Table 4). A total of 38 Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page7of12 http://www.ij-healthgeographics.com/content/12/1/4 Figure2SectionsofBreidjing(left)andFarchana(right)camps.ImagecopyrightDigtalGlobe. reports were eligible considering all sites (19 of these residential structures with a mean occupancy of 5.3, while were used to populate occupancy estimates for multiple a government census done a year earlier had counted sites within the crisis region). Afull listing is provided in 1,828structureswithanoccupancyof5.1.Wethusunder- theAdditionalfile1.Sixofthe38reportscontaineddata counted residential structures but balanced this with a on one of the sites collected within the past 3y; a further higher occupancy estimate (Table 4). The estimate for five referred to nearby similar sites within the past 3y; Kutupalong makeshift camp (not included in Table 4 due 16 referred to one of the sites or similar sites but at a to the absence of a gold standard reference estimate) was time point 10-3y prior; while 11 were pre-displacement 24,102, compared to various estimates of 20,000 to 30,000 estimates. The vast majority (31/38) were household overtheyear2010[28]. sample surveys, of which 16 were large according to A moderate precision of 10–30% was achieved for our scoring criteria (Table 2). Most reports (28/38) Breidjing, Farchana and Bambu. There was considerable provided an estimate of household size, either not over-estimationforMugungaIII(althoughmodestinabso- defined or with a definition that may not have been lute terms), and severe under-estimation for Sherkole (for consistent with occupying the same structure (e.g. which only one, fairly implausible estimate of occupancy “people eating together”). Only 5/38 reports estimated was available), and for the two Haiti sites. In Champs-de- the mean structure occupancy, while a further five Mars, 4542 shelters were counted by Médecins Sans estimated household size but defined in a way that Frontières census teams, with a mean occupancy of 5.1, was consistent with a residential structure. None of whileinDelmas24thesefigureswere8565and4.6respect- the reports provided information on occupancy by type ively,illustratingtheextentofourunder-count(Table4). of structure. Efficiency Populationestimatesandvalidation As shown in Table 5, the total person-time required to Theimagery-basedmethodachievedagooddegreeofpre- implement the method fully was reasonably consistent cision(relativedifferencecomparedtothereferencepopu- across sites, ranging from 16 to 42 person-hours, i.e. lation <10%) for Kutupalong, Shimelba, Kakuma and about 2 to 5 working days of a single analyst. Most Bairro Esturro (Table 4). In Bairro Esturro, The T-Square person-time (47%) was devoted to searching for occu- methodsurvey(apopulationestimationmethodthatcom- pancy reports. Preparing images and counting structures bines area sampling with average distance between struc- took up 17% and 24% of the total person-time, respect- tures and occupancy questionnaires [8]) on which the ively. Countingtime percapitapopulation wasalso fairly reference estimate was based yielded a figure of 1,685 consistent, ranging from 0.19 to 0.51 hours per 1000 Figure3SectionsofBambu(left)andMugungaIII(right)camps.ImagecopyrightDigtalGlobe. Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page8of12 http://www.ij-healthgeographics.com/content/12/1/4 Figure4SectionsofSherkole(left)andShimelba(right)camps.ImagecopyrightDigtalGlobe. population (mean 0.30): accordingly, a site of 100,000 to be explicitly emphasised when presenting this as an people mightbeprojected torequireabout 30hours. option forrapid estimation. Remote analysis appears feasible in terms of human Discussion resources and financial inputs: in our study, it required To our knowledge, this is the first study to have evalu- 2–5 days, two analysts and, apart from salary and office ated the validity of IDP or refugee population estima- expenditures, only minimal imagery procurement costs tion based on satellite imagery in a variety of different (15 to 25 USD per km2, though these costs would be sites and phases of displacement. Our findings suggest somewhathigher ifimageswere commissioned). that a remote analysis approach relying on manual Visual analysis of the imagery was not overly compli- counting of structures and published occupancy esti- cated,despite mostanalysts inthis studyhavingno prior mates can achieve reasonable precision in sites where GIS skills. However, the visual quality and complexity of individual structures are distinguishable and neither the image were critical determinants of both speed and clouds nor vegetation pose a significant barrier to vis- accuracy of counting. While experienced spatial analysts ual analysis. may be able to improve image quality by using various The method’s performance on the whole suggests that, techniques that enhance the visibility of features, we rather than referring to it as a valid approach, one could wished to evaluate use of the method by analysts with consider it “good enough” for certain purposes, assum- limited GIS skills, and thus refrained from making such ing that no robust ground estimation is possible within improvements to the images. Moreover, in many the same timeframe. Specifically, while inaccuracy of up instances the very typology and layout of structures (e.g. to 30% is probably unacceptable in post-acute emer- multiple walls, structures connected to each other and gency scenarios where resources for on the ground removal or abandonment of structures) imposed a limit population tracking are present, we believe that for the on accuracy that, given the present resolution of com- purposesofinitial planning (e.g.vaccination, distribution mercially available imagery, is likely to remain to some of food and non-food items, emergency water and sani- extent intractable (see Conclusions). However, having tation provision), this level of inaccuracy is a substantial four or more bands in the multi-spectral image did help improvement over no information or guesswork, which in a few cases to distinguish between vegetation and might be the case if the site is inaccessible or if expertise man-made structures when the latter were constructed in ground estimation cannot be sourced. However, the out of different materials, and we believe therefore that expected level of inaccuracy of the method would have these options should always be selected when obtaining Figure5SectionsofChampsdeMarscamp(left)andDelmas24,SollinoandFortNationalneighbourhoods(right).Imagecopyright GeoEyeInc. Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page9of12 http://www.ij-healthgeographics.com/content/12/1/4 Figure6SectionsofKakumacamp.ImagecopyrightDigtalGlobe. imagery. Beyond these challenges, availability of cloud remoteperiodsmayactuallyhaveincreasedinaccuracy(of free images may be a serious constraint in some loca- note,siteswiththehighestinformationscoredidnothave tions, particularly when a very short delay between the the greatest accuracy). It is likely that this may partly ex- analysis and image time point is needed (i.e. in dynamic, plain why sites with quality images and simple structure evolving situations): for example, in DRC we excluded layouts (Farchana, Bambu) did not perform as well as several candidate sites for analysis because no cloud free expected. This limitation could be addressed by carrying images were available. out a small, rapid structure occupancy survey to provide While some occupancy data were available for each locally appropriate data, but this is only an option if the site, the literature search was onerous and had a low site is accessible and diminishes the method’s relative yield. For one site (Sherkole), the sole estimate available advantages over other options. We tested such an ap- was clearly implausible and resulted in an under- proachinChad(paperforthcoming). estimateofpopulation.In general,we found very few ac- The above drawbacks are partly a result of our choice tual structure occupancy estimates, and had to rely to investigate a simple, manual method designed to em- instead onhouseholdsizefigures. Thesewere sometimes powernon-specialiststocarryoutpopulationestimation. fairlydivergentwithinthesamesite(seeAdditionalfile1), Automated counting methods would necessitate a far andtheirsparsitymadeitdifficulttoconstructstatistically higherskillslevelandthusrequireinputbycentresofex- meaningful confidence intervals around the population cellence in remote sensing. While a review of automated estimates. The hierarchy of evidence for structure occu- or semi-automated methods is beyond the scope of this pancy information that we used to attribute weights to paper, this is an area of vibrant research, and we believe eachreport(Table2)isanattempt torelyonall informa- that these methods have a considerably larger potential tionavailablewhileminimisinglikelybias,butcriteriaand for improvement than manual analysis. Automation scores used in this hierarchy are ultimately arbitrary and would prove particularly valuable in scenarios where can never fully reflect the actual validity of any individual population estimates need to be updated frequently to estimate.Occupancyisknowntofluctuateovertime,par- track displacement dynamics, and could perhaps provide ticularly in situations of protracted crisis, and thus to a solution for urban areas in which the manual method some extent our decision to include data from fairly mayneverperformasaccuratelyasneeded. Studylimitations Our findings should be considered conservative, as they reflect application of the method in a more challenging setofconditionsthan wouldbethe caseinmore current application by an agency with a recognised mandate. In prospectiveapplicationofthemethod,itmaybepossible to commission new imagery, thereby ensuring a minimal time difference between the analysis and imagery time points, though again subject to constraintssuch as cloud cover. Contemporary sensors increasingly have wider spectral ranges, allowing various false colour combina- tions so as to maximise the contrast between structures and other landscape features. While costs of imagery are already reasonable, it is also likely that in future crises Figure7SectionofBairroEsturro.ImagecopyrightDigtalGlobe. VHSR imagery will be reducing in cost and, in certain Checchietal.InternationalJournalofHealthGeographics2013,12:4 Page10of12 http://www.ij-healthgeographics.com/content/12/1/4 Table4Populationestimatesbasedonremoteimageryanalysisandthereferencemethod Sitename Resultsfromimagery-basedmethod Reference Absolutedifference “Amountofinformation“ population (relative%)† index(numberofreports) Meanstructure Weightedmeanstructure Estimated estimate count occupancy population Kutupalong 371 6.5x5‡=32.5 12,058 11,047 +1011(+9.2%) 789(6) Breidjing 5816 6.0 34,896 26,770 +8126(+30.4%) 800(3) Farchana 3824 6.0 22,944 19,070 +3874(+20.3%) 808(3) Bambu 1441 5.3 7637 5871 +1766(+30.1%) 452(11) MugungaIII 553 5.4 2986 1969 +1017(+51.7%) 436(11) Sherkole 2695 3.1 8355 13,958 −5603(−40.1%) 16(1) Shimelba 2552 4.7 11,994 13,043 −1049(−8.0%) 60(1) Champs-de-Mars 2361 5.3 12,513 23,214 −10,701(−46.1%) 880(5) Delmas24,Sollino, 3889 5.3 20,612 39,349 −18,737(−47.6%) 880(5) FortNational Kakuma 16,690¶ 5.3 88,457 90,457 −2000(−2.2%) 428(4) BairroEsturro 1419 6.3 8940 9523 −583(−6.1%) 252(7) †Relativedifferencecomputedasthedifferencebetweenestimatesdividedbyreferenceestimate. ‡Averageof5familiespershedasreportedbyFeeny[27]. ¶Onlycount1wasconsidered. largescaleemergencies,beavailablefreeofcostaswasthe though data requests would have to be of limited bur- case in Haiti. Photographs taken by unmanned drones or den to field workers (e.g. if email contact with anyone other aircraft could also be used, particularly if agencies familiar with the site is possible, sample image screen- pooltogetherresourcestoobtainsuchimages.However,it grabs could be shared with the field to help define the shouldbenotedthatcoordinationofagenciesaroundpro- nature of certain areas or structures); in the early phase curement and use of satellite imagery, as well as, more of displacement, it is likely that there would be few broadly, sharing of resources for timely assessment and non-residential structures, thus simplifying the count; monitoring, has proven challenging in a variety of recent furthermore, the bank of occupancy reports available emergencies, and may remain so unless a clear mandate for any given site would probably be larger, as current andresourcesareattributedtoaleadagency. emergencies benefit from more frequent assessments In routine practice, it is likely that some real-time and household surveys, with a greater proportion of ground information may be available to the analyst, reports made available online; further reports could also Table5Person-timeinputs(hours)forthevariousstepsofthepopulationestimationmethod,byactivityandsite Sitename Timeperactivity(person-hours) Total Countingtime person- per1000 Obtain Prepare Count Occupancy Review Compute imagery imagery† structures datasearch occupancy population hours population‡ Kutupalong 1.75 7.50 4.50 4.75 0.50 0.25 19.25 0.41 Breidjing 2.75 1.75 5.00 7.50 0.25 0.25 17.50 0.19 Farchana 1.50 1.50 4.75 7.50 0.25 0.25 15.75 0.25 Bambu 1.25 3.00 1.25 15.25 0.75 0.25 21.75 0.21 MugungaIII 1.25 0.75 1.00 15.50 0.75 0.25 19.50 0.51 Sherkole 1.00 1.25 3.75 10.25 0.25 0.25 16.75 0.27 Shimelba 1.00 1.00 4.00 10.50 0.25 0.25 17.00 0.31 Champs-de-Mars 4.25 7.50 7.50 13.50 0.50 0.25 33.50 0.32 Delmas24,Sollino, 4.50 6.75 12.00 13.00 0.50 0.25 37.00 0.30 FortNational Kakuma 2.75 8.50 20.75 12.25 0.25 0.25 44.75 0.23 BairroEsturro 6.25 9.50 2.75 23.00 0.75 0.25 42.50 0.29 †Workrequiredtopreparetheimageforcounting,includingpan-sharpeningifneeded,creatingthecampoutlineandthe200mgrid. ‡Basedonreferencepopulationestimate.Onlyperson-timeforstructurecountingisconsidered,asallotheractivitiesarelessdependentonthesite’spopulationsize.

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