faisal_alswied_ crimes in chicago

8/18/2019 Faisal_Alswied_ Crimes in Chicago

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Crimes in Chicago

By: Faisal Alswied

03/20/2015

Dr. Jonathan emmell

A!stract:

"n this #a#er " am $sing the City o% Chicago crime data to analy&e the association !etween the crimes and the locations. Also' a data set o% the socioeconomic indicators in Chicago

was $sed. (rinci#al com#onent analysis was $sed to analy&e the data. )here were two models*

the %irst model di+ided the crimes into three ty#es: +iolent crimes' non+iolent crimes andair#orts, crimes. )he second model di+ide the crimes into two ty#es: +iolent and non+iolent and

their association to the socioeconomic indicators.


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that has !ad socioeconomic conditions' !$t doesn,t ha+e high n$m!er o% crimes. )his tells $s that

there is a correlation !etween crime and socioeconomic conditions !$t that doesn,t mean

ca$sation.

Technical Summary

I. Data & Data cleaning

For this #ro-ect " $sed a crime data that was o!tained %rom the City o% Chicago we!site.

)he data set is really large* it has a##roimately two million res#onses and thirty +aria!les. )his

data set was aggregated to the comm$nity le+el and ty#e o% crime. Beside this data set " $sed the

socioeconomic indicators that ha+e !een aggregated at the comm$nity le+el. 9ence' " ended $#

ha+ing rows and 1;0 +aria!les. was not a!le to #re%orm (CA on this large n$m!er o%

+aria!les. As a res$lt' " did analysis to 7now the distri!$tion o% each +aria!le and see which o%

these +aria!les is signi%icant. " chose the +aria!les that ha+e a mean o% 100 %or those +aria!les

that normally distri!$ted and a median o% 100 %or those that s7ewed. Conse


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li7ely to ha##en in the %ollowing neigh!orhoods A$stin with the highest score in the %irst

com#onent' oseland' o$th hore' orth 4awndale' Chicago 4awn' est 6nglewood'

6nglewood' A$!$rn resham and reater rand Crossing. )o see the scores %or the %irst

com#onent see >)a!le "" A##endi B.? )he second #rinci#al com#onent is a!o$t the

non+iolent crimes and the crimes that ha##en indoors those crimes incl$de dece#ti+e

#ractices' the%t' and !an7 ro!!eries see >)a!le " A##endi B? %or the loadings. )hese ty#es o%

crimes are most li7ely to ha##en in ear orth ide and the 4oo# see >)a!le "" A##endi B?

%or the scores. )he third #rinci#al com#onent is a!o$t he crimes that ha##en in air#orts.

)hose crimes incl$de the crimes that ha##en in air#lanes and inside the air#orts, terminals

see >)a!le " A##endi B? %or the loadings and >)a!le "" A##endi B.? %or the scores. ra#h

""" !elow shows a #lot o% the scores and the location o% the neigh!orhoods. )hose interesting

%indings made me wonder a!o$t the e%%ect o% the socioeconomic %actors on the crimes.

9ence' " decided to incl$de them in the second model.

Graph I


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Graph II

Graph III

III. Model II

)he %irst model only showed $s only the association !etween the ty#es o% crimes and the

neigh!orhoods. 9owe+er' it did not show $s what is the moti+e !ehind those crimes' the le+el

o% income in those neigh!orhoods or the other socioeconomic indicators. As a res$lt' " added

the socioeconomic indicators see >)a!le "" A##endi A? %or the list o% +aria!les. )hose

+aria!les were added to the +aria!les that were $sed in model " > )a!le " A##endi A.? and


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the res$lts were slightly di%%erent. A%ter loo7ing at the scree #lot' " decided to etract three

com#onents ra#h "8 !elow shows the scree #lot. )he three #rinci#al com#onents that were

etracted descri!e 3 #ercent o% the +aria!ility in the data.

ra#h "8

)he #rinci#le com#onents that were etracted are almost similar to the one that were etracted

%or model one. )he %irst model is a!o$t +iolent crimes. )he second #rinci#le com#onent is a!o$t

non+iolent crimes' !$t there is some contri!$tion %rom some o% the socioeconomic +aria!les s$ch

as income and the n$m!er o% #eo#le who are a!o+e 5 and yo$nger than 1;. )he third

com#onent is com#letely di%%erent than the %irst model and it is a!o$t the socioeconomic

indicators. ra#h 8 show the association !etween +iolent crimes and the socioeconomic

indicators. )he gra#h !elow shows $s that the neigh!orhoods that ha+e high n$m!er o% +iolent

crimes ha+e !ad socioeconomic conditions. 9owe+er' this is not always the case. )here are some

neigh!orhoods that ha+e really !ad socioeconomic conditions s$ch as i+erdale' !$t doesn,t

ha+e high rate o% crimes. )his tells $s that there is a correlation !etween !ad socioeconomic


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conditions and +iolent crimes' !$t this does not indicate ca$sation. )a!le """ and )a!le "8

a##endi B show the loadings and scores %or the second model.

Graph V

IV. Conclusion


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A%ter #er%orming (CA on this high dimensional data set' " was a!le to get a meaning. )he

%irst model told $s that the data has three ty#es o% crimes' which are +iolent' non+iolent'

and Air#orts. ome neigh!orhoods are high on one or the other and some o% them high in

!oth. )he second model etracted almost the same %actors. 9owe+er' it told $s that the

socioeconomic indicators are associated with the high n$m!er o% crimes' !$t this is not

always the case. )here are some neigh!orhoods that ha+e really !ad socioeconomic

conditions' !$t they don,t ha+e high rate o% crimes. As a res$lt' we can see that there is a

correlation' !$t this doesn,t indicate ca$sation. )hen the


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C)AH(4A)FE@ 0.3 0.10;

D6(A)@6)H)E6 0.;2=

D"86AHHH6"D6)"A4 0.; G0.101

DH)E6 0.15 0.52

AH)A)"E 0.;=2

EC6HFEEDH)E6 0.3 0.2

9E)64H@E)64 G0.15 0.;;2E)96 0.5 0.;5

(AH(E(6) 0.31 0.5=2

(A"H4E)HAA6HEH6 0.55 0.52

6"D6C6 0.=1

6"D6C6H(EC9H9A44A 0.=22 0.1;

6"D6C6HAA6 0.51 0.131 G0.1;1

6"D6)"A4HADHHFE)HB 0.=52

6)AA) 0.=3

C9EE4HH(B4"CHHB"4D" 0.2 G0.102

C9EE4HH(B4"CHHED 0.;=

"D6A4 0.=2 0.215

@A44H6)A"4H)E6 0.5; 0.;21

)66) 0.=0= 0.33;8ACA)H4E)H4AD 0.;15

869"C46HEHCE@@6C"A4 0.=0 0.23

BAHEH)A86 0.

CE86"6C6H)E6 0.1= 0.5;

C)AH)A" 0.;23 0.11

9E(")A4HB"4D"HED 0.22 0.5; G0.102

(E4"C6HFAC"4")H869H(A" 0.32

)A86H4"KEH)E6 0.= 0.32 G0.102

A"(E)H)6@"A4H((6H46 0.=2

A"(E)H)6@"A4H4E6H46 0.=2

Ta"le II 2Scores 3or the 3irst model.Name Violent4outdoor Non5iolent irport

ogers (ar7 0.1205=325 0.1;==;=22 0.0235;;==

est idge 0.00;110 G0.00;1= G0.1===1

#town G0.122325 0.52=2515 G0.115=

4incoln


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A+ondale G0.25=;;= G0.113;00=5 G0.1=5=1

4ogan


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@o$nt reenwood G0.;32025203 G0.51330121= G0.13233555

@organ (ar7 G0.2133 G0.05;5 G0.22225=3

EL9are G0.5=112; G0.335152 ;.3;0551

6dgewater G0.0151321 0.21332 G0.1;=55

Ta"le III 2$oadings 3or the second model.Varia"le Name Violent4outdoor Non5iolent Socioeconomic

AE 0.;=; 0.22=

AA4) 0.=33 0.1= 0.253

BA))6 0.=3 0.1== 0.23

B4A 0.=3

C"@H6IA4HAA4) 0.=3 0.235 0.1=

C"@"A4HDA@A6 0.=5 0.2

C"@"A4H)6(A 0.5 0.2=

D6C6()"86H(AC)"C6 0.332 0.=13

@E)EH869"C46H)96F) 0.=1 0.15

ACE)"C 0.;5 0.31

EFF66H"8E48"HC9"4D6 0.=3 0.1=

E)96HEFF66 0.=25 0.21(B4"CH(6AC6H8"E4A)"E 0.52 0.235 0.1;1

EBB6 0.=15 0.21 0.21

6IHEFF66 0.; 0.3;5

)96F) 0.; 0.;3= G0.15;

6A(EH8"E4A)"E 0.;= 0.355

A@B4" 0.3 0.303

")6F66C6H")9H(B4"CHEFF 0.;=2 0.1; 0.2

(E)"))"E 0.3 0.2 0.35

A"(E)HA"CAF) G0.22=

A446 0.=32 0.102 0.1==

A(A)@6) 0.;5 0.1 0.1=2

BA 0.11 0.;5 G0.1;

C9AHA(A)@6) 0.;; 0.1;5C9AH(A"H4E)HED G0.1 0.52 0.1=5

CE@@6C"A4HHHB"6HEFF 0.;=;

C)AHB 0.3 0.55= 0.1;=

C)AH(4A)FE@ 0.= 0.303

D6(A)@6)H)E6 0.105 0.;1=

D"86AHHH6"D6)"A4 0.2 G0.22;

DH)E6 0.= 0.05 G0.12

AH)A)"E 0.; 0.30

EC6HFEEDH)E6 0.3; 0.5=3 G0.1=

9E)64H@E)64 G0.12= 0.;=

E)96 0.15 0.5;

(AH(E(6) 0.3=; 0.5;5

(A"H4E)HAA6HEH6 0.5= 0.1

6"D6C6 0.=1 0.101

6"D6C6H(EC9H9A44A 0.=1 0.1 0.13=

6"D6C6HAA6 0. G0.1

6"D6)"A4HADHHFE)HB 0.=1

6)AA) 0.=5

C9EE4HH(B4"CHHB"4D" 0.55 0.1;1

C9EE4HH(B4"CHHED 0.;; 0.15=

"D6A4 0.;;3 0.20 0.2


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4oo# G1.3=;551 5.0331;=; 0.=;0;3=1

ear o$th ide G0.=2;0; 0.1;0;= G0.013=2

Armo$r

faisal_alswied_ crimes in chicago

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