2. The last centuryhas seenamajor increase inthe world’soverallpopulation. In1900 itis
estimatedapproximately 1.564 billionpeople inhabitedthe earth(worldmapper); todaythatnumber
has more than quadrupledtoover 7.2 billionpeople. Naturally,asearth’spopulationincreases,sodoes
the overall populationof cities. The turnof thiscenturyalsomarkedthe firsttime that overhalf of the
world’spopulationlivedintownsorcities(UNFPA). By2050 approximately64.1% of the developing
worldand 85.9% of the developedworldwill be urbanized(The economist). Suchfiguresare staggering
consideringcitiesandtownsonlycoverappromitely0.5% of world’slandsurface.
Urbanization,orthe increasingmovementof peoplefromrural tourban areas, stemsfromthe overall
opportunitiescitiesprovide. People are more willingtolive incitiesortownsbecause there’sgreater
jobavailabilityandeasieraccesstoeducation,healthcare,sanitation,amongmanyotherthings. Social
expressionandmobilizationalsooccursbecause of the greateraccessof intellectual resources. Withso
manyhumansflockingtocitiesin orderto capitilize onthe aforementionedopportunities,
overpopulationbecomesaseriousproblem.
Citiesare are oftenoverwhelmedbythe overall demandof theircitizensforbetterinfrastructure
systems. Housingalsobecomesextremelyscarce withsuchhighdemandforlivingspaces; somuchso
that the pricesof housingfar exceed whatmanyof the city’spopulationcanaffordtopay. Asa result,
slumsandotherpoor denselypopulatedregionsof citiesform. Typicallyareaswithgreaterpollution
and less accessto essential servicesandotherbusinesses,whichleadstolessjobopportunity. Inorder
to make a living,manyinthese areasresortto crimessuchas robberyanddrug traffickingasa way of
makingan income (UnitedNationsNewsService). Suchactivitiesthenspreadtootherpartsof the city,
increasingthe overall crime. Asa resultof suchconditions,murderandhomocide are acommonplace.
Figure 1 Figure 2
3. The existance of suchmay determanyof the city’sinhabitantsfromcontinuingtolive there. Asaresult
an “suburbansprawl”occurs where manypeople move outfromthe cityintoresidential areastoavoid
the forementionedproblems,thusdecreasingoverall inhabitantsof the cityand increasingthe
populationof residential areassurroundingthe city.
In thisprojectI decided toexamineWashington,DC because of itsrelative proximitytothe University
of Maryland. The Districtof Columbia,alongwithahandful of othercitiesinthe UnitedStates,has
actuallyseenadecrease initspopulationoverthe pastfew decades. Accordingto informationfrom
the UnitedStatesCensusBureau,the populationwentform 638,333 residentsin1980 to 601,723
residentsin2010(Census). Withimagesprovidedbythe landsatsatellite,I’mgoingtocompare
photographsfrom1984 to 2011 to determine whetherthere wasactuallya“suburbansprawl”. The
periodof the mid1980s was the start of the infamousDCdrugtrade where violentcrime rose steadily
intothe 1990s. In theory,thisdrugtrade triggeredthe movementof manycityresidentsin to
surroundingsuburbanareas. Bycomparingthe builtlandcoversof urban and residential areasIwill try
to determine whetherthe “suburbansprawl”actuallyoccurred inthistime frame.
Methodology:
My firsttaskwas to downloadimageswithinthe statedtime frame. Luckilytwolandsatimages,one
from1984 and one from 2011, were located. Justby observingthese twoimagesastheyare you
cannot tell muchaboutthe size of residential areastourbanareas. Furthermore bothimageslook
relativelysimilar,soyoudefinitelycannotdistinguishmuchaboutpotentialdifferencesinlandcover
types.
Figure 3. Washington,DC1984 Figure 4. Washington,DC2011
4. My nexttaskwas creatinga supervisedimage withgroundtruthregionsof interest. Iseparatedland
coverareas intofive categories: urbanareas,residential areas,agricultural/openareas,forestedAreas,
and waterbodies. Eachof these categorieswere designatedROIswhere Ihandselectedgroupsof pixels
that bestrepresentedeacharea. The colorsthat representedeachareaare as follows:red-urbanareas,
residentialareas- cyan,agricultural/openareas-yellow,forested areas-green,andwaterbodies- blue.
Once finishedestablishingmyROIsthe supervisedimage wasprocessedusingthe maximumlikelihood
method. The resultsare the followingtwoimages:
Figure 5. Supervised1984 Figure 6. Supervised2011
Observingthese twoimagesitseemsasif mytheorycouldbe potentiallydisproved. The supervised
image from2011 seemstoshowa greateramountof urban areasthan inthe 1984. However,it’shard
to determine the difference inthe residential areasfrombothimages. Inorderto getan accurate
representationof whatpercentage of the Washington,DCareaiscomprisedof urbanand residential
areas,I generatedaconfusionmatrix forbothsupervisedimages.The resultsare asfollows:
Confusion Matrix: V:UsersDDocumentssupervised_1984
Overall Accuracy = (40625/43976) 92.3799%
Kappa Coefficient = 0.8957
Ground Truth (Pixels)
Class Urban Forest Water Agriculture
Residential
Unclassified 0 0 0 0
0
Urban [Red] 7 6613 0 0 82
286
5. Forest [Green 0 6052 0 0
0
Water [Blue] 6 0 6231 516
84
Agriculture [ 0 0 199 18230
81
Residential [ 851 7 504 735
3499
Total 7470 6059 6934 19563
3950
Ground Truth (Pixels)
Class Total
Unclassified 0
Urban [Red] 7 6981
Forest [Green 6052
Water [Blue] 6837
Agriculture [ 18510
Residential [ 5596
Total 43976
Ground Truth (Percent)
Class Urban Forest Water Agriculture
Residential
Unclassified 0.00 0.00 0.00 0.00
0.00
Urban [Red] 7 88.53 0.00 0.00 0.42
7.24
Forest [Green 0.00 99.88 0.00 0.00
0.00
Water [Blue] 0.08 0.00 89.86 2.64
2.13
Agriculture [ 0.00 0.00 2.87 93.19
2.05
Residential [ 11.39 0.12 7.27 3.76
88.58
Total 100.00 100.00 100.00 100.00
100.00
Ground Truth (Percent)
Class Total
Unclassified 0.00
Urban [Red] 7 15.87
Forest [Green 13.76
Water [Blue] 15.55
Agriculture [ 42.09
Residential [ 12.73
Total 100.00
7. Forest [Green 0 8851 122 262
17
Water [Blue] 52 143 7193 544
514
Agriculture [ 977 5 49 11413
272
Residential [ 3710 16 139 2009
5041
Total 22137 9015 7503 14487
6128
Ground Truth (Pixels)
Class Total
Unclassified 0
Urban [Red] 2 17941
Forest [Green 9252
Water [Blue] 8446
Agriculture [ 12716
Residential [ 10915
Total 59270
Ground Truth (Percent)
Class Urban Forest Water Agriculture
Residential
Unclassified 0.00 0.00 0.00 0.00
0.00
Urban [Red] 2 78.59 0.00 0.00 1.79
4.63
Forest [Green 0.00 98.18 1.63 1.81
0.28
Water [Blue] 0.23 1.59 95.87 3.76
8.39
Agriculture [ 4.41 0.06 0.65 78.78
4.44
Residential [ 16.76 0.18 1.85 13.87
82.26
Total 100.00 100.00 100.00 100.00
100.00
Ground Truth (Percent)
Class Total
Unclassified 0.00
Urban [Red] 2 30.27
Forest [Green 15.61
Water [Blue] 14.25
Agriculture [ 21.45
Residential [ 18.42
Total 100.00
8. Class Commission Omission Commission
Omission
(Percent) (Percent) (Pixels)
(Pixels)
Urban [Red] 2 3.03 21.41 543/17941
4739/22137
Forest [Green 4.33 1.82 401/9252
164/9015
Water [Blue] 14.84 4.13 1253/8446
310/7503
Agriculture [ 10.25 21.22 1303/12716
3074/14487
Residential [ 53.82 17.74 5874/10915
1087/6128
Class Prod. Acc. User Acc. Prod. Acc.
User Acc.
(Percent) (Percent) (Pixels)
(Pixels)
Urban [Red] 2 78.59 96.97 17398/22137
17398/17941
Forest [Green 98.18 95.67 8851/9015
8851/9252
Water [Blue] 95.87 85.16 7193/7503
7193/8446
Agriculture [ 78.78 89.75 11413/14487
11413/12716
Residential [ 82.26 46.18 5041/6128
5041/10915
Analysisof results:
The Overall Accuracyof the 1984 image was92.3799%, having40625 of the 43976 pixelsof the
image representedcorrectly. ItsKappaCoefficientis0.8957. The overall accuracyof the 2011 image
was 84.1842% and 49896 of the 59270 pixelsinthe image representedcorrectly. ItsKappaCoefficientis
0.7956. In simple terms,the firstimage ismore accurate inevaluatingandcorrectlyclassifyingthe ROI
points. The breakdownof the regionsof interestfromthe 1984 image are as follows: Urbanareas
covered15.55 % of the pixelsonthe image,ForestedAreascovered13.76 %, Wateris 15.55%,
agriculture/openareasare 42.09% and residential areasare 12.73%. The breakdownof the regions of
interestfromthe 2011 image are as follows:Urbanareascovered30.27%, ForestAreascovered15.61%,
Water covered14.25%, Agricultural/openareascovered21.45% and residential areascovered18.42%.
The regionwiththe lowestUseraccuracy are residential areasbyfar,witha 62.53% readingin1984 and
a 46.18% readingin2011.
The analysisof the resultsseemtoconfirmwhatwasobservedabouturbanareas whenlooking
at the imageswithanakedeye;from1984 to 2011 urbanarea in the Districtgrew. In fact,it has almost
9. doubledinthe past17 years. Thisseems togo againstthe theorythat urbanareas woulddecrease asa
“suburbansprawl”occurred. However,residential areadidincrease from12.73% in1984 to 18.42% in
2011. Thisinformation doesn’tnecessarilydisprovethe ideaof anurbansprawl occurring,maybe itjust
changesthe approach of what we define asurbansprawl. Asobservedbythe 2011 supervisedimage
(figure 6) there seemstobe pocketsof urban areasoutside the actual city. Many of these urbanareas
may have developedinresponse toa“suburbansprawl”. Townsand smallercitiesformaround
residentialareastocater to the needsof the new growingurbanpopulation. Many of these smaller
citiesthatgrewaround residential areas,suchasRockville andBethesda,have populationsthatexceed
over50,000 people. Asmentioned,thismaybe aresponse tothe growingpopulation’swantfor
resourcesandconspicuousconsumptionwithouthavingtodeal withthe crime inthe city.
Justusinga supervisedimage andclassifyinglandcoversourcesmaynotpaintthe entire picture
whenanalyzingwhethersucha“suburbansprawl”happened. Aswithmanycities,vacantbuildingscan
tendto be a problemespeciallywhenclassifyingland cover. These buildingsare accountedforinurban
landcover,but serve nopurpose orfunctionwhentryingtodetermine whetherthere wasagrowthin
urban development. Infact,theyhurtour analysisbecause we’re automaticallyassociatingsuch
buildingswithurbanpopulationgrowth.
The analysisalsoshowedflawsinthe datacollectionpoints. Creatingasupervisedimage from
ROIsis a veryrough wayto try and find whetherthere are decreasesinoverall urbanandsuburban
development. Ideally,demographicinformationforall residential areassurroundingthe city,inaddition
to informationcollected,wouldallowforadeeperanalysisof “suburbansprawl”. Also,as statedearlier,
the useraccuracy wasrather poor forresidentialareas. It’sextremelyhardtopickoutresidential ROIs
because of theirtendencytohave characteristicsof bothurbanand agricultural/openregions. The
computeralsohas a difficulttime distinguishingresidentialareasforthatsame reason. The collectionof
was made evenmore difficultdue tothe factthe imageswere notanniversaryimage,thusincreasing
the potential forthere tobe greatervariabilitydue tochangesinweather. The firstoriginal image from
1984 (figure 1) isclearlylesscloudythanthe secondoriginal image from2011 (figure 2) and can easily
effecthowthe ROIsare processed.
Furthermore,there’sapossibilitywe overestimatedthe “suburbansprawl”especially withall of
the re developmentof the citythat startedto occur in the beginningof the 2000s. Censusdata shows
that the overall populationactuallygrew from 572,059 residentsin2000 to 601,723 residentsin2010
whichwasa 5.2% increase inthat10 yearperiod. Thiswasthe largest10 year periodof growththe city
has seensince the babyboomereraof the 1950s. Gentrification,orthe reconditioning,of
neighborhoodsinthe cityhave enticedyoungprofessionalstomove inandconvincedlongtime
residentstostay(The gentrificationreader). Thisinflux of youngindividualsmeansmore neighborhoods
inthe districtare slatedforgentrification,reducingcrime andmakingthe cityevenmore desirablefor
potential residents.
Conclusion:
Although the builturbanareadoubled,there maystill be evidence of a“suburbansprawl”
occurringbetween1984 and2011. There was an almost50% increase inresidential areasinthistime
period. Furthermore,thereisapossibilitysmall urbanhubswere developedorcreatedaround
residentialareasasa resultof an increasingsuburbanpopulationthatwantedtohave the convenience
and feel of citiesnearbywhile nothavingtodeal withthe crime anddangersof the city. Thistype of
demandmayhave leadto the creationof pocketcitiesinthe suburbs. To trulyassesswhetherthis
happened,andwhetherthere wasamass exodusfromthe cityto these areas,more demographicdata
wouldbe needed. Alsoimageswithmore consistentROIpixels,aswell asanniversaryimages,would
