library( dplyr )
library( pander )
library( geojsonio )
library( maptools )
library( sp )

Create the Dependent Variable

Get Median Home Value from Census

library( censusapi )


censuskey <- "b431c35dad89e2863681311677d12581e8f24c24"


myvars <- makeVarlist( name="sf3", 
                       vintage=2000,
                       find="median", 
                       varsearch="label",
                       output="dataframe" )

dat.2000  <- getCensus( name="sf3", 
                        vintage=2000, 
                        key=censuskey, 
                        vars=c("H085001"), 
                        region="tract:*",
                        regionin="state:36")

dat.2000$FIPS <- paste( dat.2000$state, dat.2000$county, dat.2000$tract, sep="" )

dat.2000 <- rename( dat.2000, MHV2000 = H085001 )




# B25077_001E Median Home Value ACS5 2010, 2015

dat.2010  <- getCensus( name="acs5", 
                        vintage=2015, 
                        key=censuskey, 
                        vars=c("B25077_001E"), 
                        region="tract:*",
                        regionin="state:36")

dat.2010$FIPS <- paste( dat.2010$state, dat.2010$county, dat.2010$tract, sep="" )

dat.2010 <- rename( dat.2010, MHV2010 = B25077_001E )




dat.2015  <- getCensus( name="acs5", 
                        vintage=2015, 
                        key=censuskey, 
                        vars=c("B25077_001E"), 
                        region="tract:*",
                        regionin="state:36")

dat.2015$FIPS <- paste( dat.2015$state, dat.2015$county, dat.2015$tract, sep="" )

dat.2015 <- rename( dat.2015, MHV2015 = B25077_001E )



mhv <- merge( dat.2000, dat.2010 ) 

mhv <- merge( mhv, dat.2015 )

head( mhv ) %>% pander

state	county	tract	FIPS	MHV2000	MHV2010	MHV2015
36	001	000100	36001000100	70100	96000	96000
36	001	000200	36001000200	75100	85300	85300
36	001	000300	36001000300	88100	163600	163600
36	001	000401	36001000401	85400	154400	154400
36	001	000403	36001000403	109500	193200	193200
36	001	000404	36001000404	99400	NA	NA

# Subset by Onondaga County

mhv <- mhv[ mhv$county == "067" , ]

Adjust Prices for Inflation

Let’s represent everything in 2015 dollars.

# Adjust for Inflation

mhv$MHV2000 <- mhv$MHV2000 * 1.3845

mhv$MHV2010 <- mhv$MHV2010 * 1.0785

mhv$CHANGE <- mhv$MHV2015 - mhv$MHV2000

Descriptive Statistics

summary( mhv$MHV2000 ) %>% pander

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
33920	91270	113700	116800	134500	268500

summary( mhv$MHV2015 ) %>% pander

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
48500	87600	123200	129700	161600	324000	3

summary( mhv$CHANGE ) %>% pander

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
-20010	-2377	8849	13500	23070	144700	3

plot( mhv$MHV2000, mhv$MHV2015, bty="n", pch=19, col=gray(0.5,0.5), cex=2,
      main="Median Home Value in 2000 vs 2015", xlab="Median Home Value in 2000", ylab="Median Home Value in 2015" )

hist( mhv$MHV2015 - mhv$MHV2000, breaks=20, col="darkgray", border="white", main="CHANGE in Median Home Value 2000-2015", xlab="Change in Value")

Map Changes in Home Values

# library( geojsonio )

url <- "https://raw.githubusercontent.com/christine-brown/SyracuseLandBank/master/SHAPEFILES/SYRCensusTracts.geojson"

syr <- geojson_read( url, method="local", what="sp" )

syr <- merge( syr, mhv, by.x="GEOID10", by.y="FIPS" )

              


color.function <- colorRampPalette( c( "firebrick4", "light gray", "steel blue" ) )

col.ramp <- color.function( 10 ) # number of groups you desire

col.vec <- cut( syr$MHV2000, breaks=seq( from=0, to=250000, by=25000 ), labels=col.ramp )

col.vec <- as.character( col.vec )


par( mfrow=c(1,2), mar=c(0,0,4,0) )

plot( syr, col=col.vec, main="Home Values in 2000" )



col.vec <- cut( syr$MHV2015, breaks=seq( from=0, to=250000, by=25000 ), labels=col.ramp )

col.vec <- as.character( col.vec )

plot( syr, col=col.vec, main="Home Values in 2015" )

col.ramp <- color.function( 7 ) # number of groups you desire

col.vec <- cut( syr$CHANGE, breaks=seq( from=-25000, to=150000, by=25000 ), labels=c("firebrick4","gray80","gray60","steelblue","steelblue","steelblue","blue")  )

col.vec <- as.character( col.vec )

table( col.vec ) %>% pander

blue	firebrick4	gray60	gray80	steelblue
1	29	1	15	1

par( mar=c(0,0,3,0) )

plot( syr, col=col.vec, main="Change in Median Home Values 2000-2015" )

Merge Data

Check Files in Directory

# setwd( "./DATA/AGGREGATED_DATA" )

# dir()

#  [1] "censusDataFromChris.csv"       "codeviolations_aggregated.csv" "firepolice_aggregated.csv"     "grocery_aggregated.csv"       
#  [5] "hold.csv"                      "library_aggregated.csv"        "LIHTC_aggregated.csv"          "NMTC_aggregated.csv"          
#  [9] "NPO_Data_processed.csv"        "Permits_aggregated.csv"        "Permits_processed.csv"         "publichousing_aggregated.csv" 
# [13] "syr__ed_score_per_tract.csv"   "TaxSubsidies_aggregated.csv"   "Yelp_Aggregated.csv"

Read Aggregated Files

setwd( ".." )

d1 <- read.csv( "./DATA/AGGREGATED_DATA/censusDataFromChris.csv", stringsAsFactors = F )
d2 <- read.csv( "./DATA/AGGREGATED_DATA/codeviolations_aggregated.csv", stringsAsFactors = F )
d3 <- read.csv( "./DATA/AGGREGATED_DATA/firepolice_aggregated.csv", stringsAsFactors = F )
d4 <- read.csv( "./DATA/AGGREGATED_DATA/grocery_aggregated.csv", stringsAsFactors = F )
d5 <- read.csv( "./DATA/AGGREGATED_DATA/library_aggregated.csv", stringsAsFactors = F )
d6 <- read.csv( "./DATA/AGGREGATED_DATA/LIHTC_aggregated.csv", stringsAsFactors = F )
d7 <- read.csv( "./DATA/AGGREGATED_DATA/NMTC_aggregated.csv", stringsAsFactors = F )
d8 <- read.csv( "./DATA/AGGREGATED_DATA/NPO_Data_processed.csv", stringsAsFactors = F )
# d9 <- read.csv( "Permits_aggregated.csv", stringsAsFactors = F )
d10 <- read.csv( "./DATA/AGGREGATED_DATA/Permits_processed.csv", stringsAsFactors = F )
d11 <- read.csv( "./DATA/AGGREGATED_DATA/publichousing_aggregated.csv", stringsAsFactors = F )
d12 <- read.csv( "./DATA/AGGREGATED_DATA/syr__ed_score_per_tract.csv", stringsAsFactors = F )
d13 <- read.csv( "./DATA/AGGREGATED_DATA/TaxSubsidies_aggregated.csv", stringsAsFactors = F )
d14 <- read.csv( "./DATA/AGGREGATED_DATA/Yelp_Aggregated.csv", stringsAsFactors = F )



head( d1 ) %>% pander

Table continues below
X	NAME	STATE	COUNTY	TRACT	TOTAL
1	Census Tract 1, Onondaga County, New York	36	67	1	470
2	Census Tract 1, Onondaga County, New York	36	67	1	489
3	Census Tract 1, Onondaga County, New York	36	67	1	518
4	Census Tract 1, Onondaga County, New York	36	67	1	598
5	Census Tract 1, Onondaga County, New York	36	67	1	644
6	Census Tract 10, Onondaga County, New York	36	67	10	3890

Table continues below
MEDIANHOUSEINCOME	MEDIANFAMINCOME	BLACK	ASIAN	HISPANIC	WHITE
47703	26477	10	151	12	309
47391	82500	0	87	31	402
31771	77292	74	44	11	400
49605	95104	76	65	14	457
49342	NA	79	85	19	476
28803	29638	866	31	255	2607

Table continues below
EMPLOYED	UNEMPLOYED	INLABORFORCE	POVERTY	VACANTTOTAL	OTHERVACANT
199	39	238	194	112	36
206	67	273	169	114	45
167	72	239	133	81	10
274	38	312	129	78	9
335	39	374	147	73	8
1742	208	1950	851	90	48

Table continues below
VACANTFORRENT	SEASONALVACANT	FORSALEVACANT	HOUSEHOLDRECEIVEDSNAP
0	0	26	78
0	0	22	76
0	0	36	87
0	0	35	52
16	7	0	48
42	0	0	429

Table continues below
HOUSEHOLDS	OWNEROCCUPIED	RENTEROCCUPIED	TOTALHOUSINGUNITS
294	70	224	406
308	80	228	422
338	64	274	419
380	64	316	458
430	69	361	503
2114	688	1426	2204

Table continues below
LACKINGKITCHENFACILITIES	LACKINGPLUMBING	MEDIANMONTHLYHOUSINGCOSTS
0	0	871
0	0	871
0	0	890
0	0	998
0	0	1038
15	26	634

Table continues below
LESS18	SINGLEMOTHERBELOWPOVERTY	TRAVELTIMETOWORKMIN	ENROLLEDINSCHOOL
43	0	2395	28
13	0	NA	36
43	0	NA	89
59	0	NA	99
59	0	NA	86
783	124	26155	751

Table continues below
YEAR	GEOID	VACANT	MALEUNEMPLOYED	FEMALEUNEMPLOYED	MALELABORFORCE
2012	3.607e+10	NA	NA	NA	NA
2011	3.607e+10	NA	NA	NA	NA
2013	3.607e+10	NA	NA	NA	NA
2014	3.607e+10	NA	NA	NA	NA
2015	3.607e+10	NA	NA	NA	NA
2012	3.607e+10	NA	NA	NA	NA

FEMALELABORFORCE	TOTALFORPOVERTY	AGGREGATETRAVELTIMETOWORK	HOUSINGVALUES
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA
NA	NA	NA	NA

head( d2 ) %>% pander

TRACT	YEAR	Code.Violations
1	2012	11
1	2013	8
1	2014	7
1	2015	7
10	2012	40
10	2013	57

head( d3 ) %>% pander

TRACT	FIREPOLICE	YEAR
1	0	2017
10	0	2017
14	0	2017
15	0	2017
16	0	2017
17.01	1	2017

head( d4 ) %>% pander

TRACT	GROCERY	YEAR
1	0	2015
10	0	2015
14	0	2015
15	0	2015
16	0	2015
17.01	0	2015

head( d5 ) %>% pander

TRACT	LIBRARY	YEAR
1	0	2017
10	0	2017
14	1	2017
15	0	2017
16	0	2017
17.01	0	2017

head( d6 ) %>% pander

TRACT	YEAR	LIHTC
16	2007	1
23	2009	1
34	2008	1
34	2013	1
35	2008	1
35	2009	1

head( d7 ) %>% pander

TRACT	YEAR	NMTC_Dollars
15	2006	3015000
32	2008	8e+06
30	2011	20428080
21.01	2013	6e+06

head( d8 ) %>% pander

Table continues below
EIN	NAME	STATE	NTEE1	NTEECC
10587946	MIGHTYMEN MINISTRIES INC	NY	L	L25
10758900	SYRACUSE SHAKESPEARE FESTIVAL CORP	NY	A	A65
16177361	HERBERT S AND ELEANORE L HOWARD CHARITABLE FOUNDATION	NY	T	T20
20590821	ARC OF ONONDAGA FOUNDATION	NY	P	P82
20651844	SYRACUSE CITY SCHOOL DISTRICT EDUCATIONAL FOUNDATION	NY	B	B12
20658895	TRESNESS FAMILY FOUNDATION CORPORATION	NY	T	T22

Table continues below
ADDRESS	CITY	ZIP	zip5	FIPS
108 SUMMIT AVE	SYRACUSE	13207-1335	13207	36067
350 WEST FAYETTE ST ROOM 001	SYRACUSE	13202-1202	13202	36067
1 LINCOLN CTR	SYRACUSE	13202-1324	13202	36067
600 S WILBUR AVE	SYRACUSE	13204-2730	13204	36067
725 HARRISON STREET Q157	SYRACUSE	13210-2395	13210	36067
PO BOX 6729	SYRACUSE	13217-6729	13217	36067

Table continues below
MSA_NECH	PMSA	TAXPER	outnccs	OUTREAS	Filer	ZFILER	SUBSECCD
8160	.	201412	IN	NA	Y	Y	3
8160	.	201412	IN	NA	Y	Y	3
8160	.	201512	IN	NA	Y	N	3
8160	.	201412	IN	NA	Y	N	3
8160	.	201506	IN	NA	Y	N	3
8160	.	201512	IN	NA	Y	N	3

Table continues below
ntmaj5	ntmaj10	ntmaj12	NTEEconf	MAJGRPB
HU	HU	HU	B	L
AR	AR	AR	B	A
OT	PU	PU	B	T
HU	HU	HU	A	P
ED	ED	ED	A	B
OT	PU	PU	B	T

Table continues below
ASCD	INCCD	INCOME	F990REV	ASSETS	cFiler	czFiler	cTaxPer
0	0	0	0	0	Y	Y	201412
0	0	0	0	0	Y	Y	201412
6	4	198352	0	2336824	Y	N	201312
6	4	453072	324519	2319125	Y	N	201412
4	4	142088	106491	125400	Y	N	201506
4	2	20714	0	144315	Y	N	201312

Table continues below
cTotRev	cAssets	cFinSrc	RULEDATE	FNDNCD	FRCD	AFCD
.	.	submaster990n	200206	15	2	3
.	.	submaster990n	200303	15	0	3
120157	2498777	core2013pf	200207	4	0	3
324519	2319125	bmf1608c3	200206	15	1	3
106491	125400	bmf1608c3	200301	17	1	3
4281	150943	core2013pf	200309	4	0	3

Table continues below
CONTACT	SEC_NAME	EOSTATUS	LEVEL1
RENA HOLMAN		1	PC
R BELL		1	PC
BOND SCHOENECK & KING LLP	MACKIN JAMES E TTEE	1	PF
PETER CARMEN		1	PC
JAN QUINTZAU		1	PC
ANDREW TRESNESS		1	PF

Table continues below
LEVEL2	level3	level4	CLASSCD	DEDUCTCD	NTEEIRS	NAICS	ORGCD
O	HS	L	1000	1	L25	624229	1
O	AR	A	2000	1	A65	711110	1
S	ZC	T	1000	1	T20	813211	2
O	HS	P	1000	1	P82	624120	1
S	ZB	B	1000	1	B112	813219	1
S	ZC	T	1000	1	T22	813211	1

Table continues below
ACCPER	lon	lat	STATEFP10	COUNTYFP10	TRACTCE10	GEOID10
12	-76.17	43.03	36	67	5000	3.607e+10
12	-76.16	43.05	36	67	3000	3.607e+10
12	-76.15	43.05	36	67	3200	3.607e+10
12	-76.18	43.04	36	67	2901	3.607e+10
6	-76.14	43.04	36	67	4302	3.607e+10
12	-76.12	43.05	36	67	3500	3.607e+10

Table continues below
NAME10	NAMELSAD10	MTFCC10	FUNCSTAT10	ALAND10	AWATER10
50	Census Tract 50	G5020	S	1149255	45948
30	Census Tract 30	G5020	S	804722	0
32	Census Tract 32	G5020	S	1257528	0
29.01	Census Tract 29.01	G5020	S	1870601	0
43.02	Census Tract 43.02	G5020	S	1195046	0
35	Census Tract 35	G5020	S	1137202	0

INTPTLAT10	INTPTLON10	npos_yr
43.03	-76.17	2014
43.05	-76.16	2014
43.05	-76.15	2015
43.04	-76.18	2014
43.04	-76.13	2015
43.05	-76.12	2015

head( d10 ) %>% pander

Table continues below
Type	Applicant	Location
Antenna / Dish	Empire Archives Inc	405 Hiawatha Blvd E
Antenna / Dish	G&K Trucking LLC	1714 Salina St N & Exchange St
Antenna / Dish	G&K Trucking LLC	1714 Salina St N & Exchange St
Antenna / Dish	Verizon	110 Wolf St To Salina St N
Antenna / Dish	Centerline Communications	1629-35 Salina St N
Antenna / Dish	Centerline Communications	1629-35 Salina St N

Table continues below
Issued	Year	Valuation	Fee.Amount	Location2
09/10/2012	2012	18000	335	405 Hiawatha Blvd E , Syracuse, New York
04/25/2013	2013	22000	395	1714 Salina St N & Exchange St , Syracuse, New York
08/26/2014	2014	30000	515	1714 Salina St N & Exchange St , Syracuse, New York
03/21/2016	2016	60000	1010	110 Wolf St, Syracuse, New York
06/02/2014	2014	5000	140	1629-35 Salina St N , Syracuse, New York
05/01/2015	2015	30000	515	1629-35 Salina St N , Syracuse, New York

lon	lat	Tract
-76.16	43.07	NA
-76.17	43.07	NA
-76.17	43.07	NA
-76.17	43.07	NA
-76.16	43.07	NA
-76.16	43.07	NA

head( d11 ) %>% pander

YEAR	TRACT	Subsidized.units.available
2010	2	34
2010	6	139
2010	16	175
2010	29	11
2010	30	500
2010	39	6

head( d12 ) %>% pander

X	YEAR	TRACT	SCHOOL_SCORE
1	2005	3	0.7994
2	2005	4	0.859
3	2005	9	0.8369
4	2005	14	0.8009
5	2005	17.02	0.5847
6	2005	18	0.8378

head( d13 ) %>% pander

X	TRACT	YEAR	NUMBER_OF_SUBSIDIES	TOTAL_AMOUNT
1	1	2003	3	4919450
2	1	2004	4	12058091
3	1	2005	5	8749272
4	1	2006	6	3401458
5	1	2007	4	3413215
6	1	2009	5	959108

head( d14 ) %>% pander

Table continues below
X	TRACT	YEAR	RATING	REVIEWS	PRICE	ESTABLISHMENTS	BARS
1	3.607e+10	2017	4.2	3	NA	2	NA
2	3.607e+10	2017	1.688	12	NA	2	NA
3	3.607e+10	2017	3.71	7	NA	3	NA
4	3.607e+10	2017	3.117	19	NA	20	NA
5	3.607e+10	2017	2.162	4.167	NA	6	NA
6	3.607e+10	2017	2.183	5.6	NA	5	NA

RESTAURANTS	STARS_4_5	STARS_3_4	STARS_2_3	STARS_1_2	STARS_0_1
2	1	1	NA	NA	NA
2	NA	1	NA	NA	1
3	1	1	1	NA	NA
20	5	9	2	1	3
6	1	1	2	1	1
5	2	NA	1	NA	2

Analysis

Census Variables

### DEFINE VARIABLE


d1$POVRATE <- d1$POVERTY / d1$TOTAL
d1$PBLACK <- d1$BLACK / d1$TOTAL
d1$PASIAN <- d1$ASIAN / d1$TOTAL
d1$PUNEMPLOYED <- d1$UNEMPLOYED / d1$TOTAL
d1$PSNAP <- d1$HOUSEHOLDRECEIVEDSNAP / d1$HOUSEHOLDS
d1$PVACANT <- d1$VACANTTOTAL / d1$TOTALHOUSINGUNITS
d1$POWNER <- d1$OWNEROCCUPIED / d1$TOTALHOUSINGUNITS

Nice Correlation Tables

# x is a matrix containing the data
# method : correlation method. "pearson"" or "spearman"" is supported
# removeTriangle : remove upper or lower triangle
# results :  if "html" or "latex"
# the results will be displayed in html or latex format
corstars <-function(x, method=c("pearson", "spearman"), removeTriangle=c("upper", "lower"),
                    result=c("none", "html", "latex")){
  #Compute correlation matrix
  require(Hmisc)
  x <- as.matrix(x)
  correlation_matrix<-rcorr(x, type=method[1])
  R <- correlation_matrix$r # Matrix of correlation coeficients
  p <- correlation_matrix$P # Matrix of p-value 
  
  ## Define notions for significance levels; spacing is important.
  mystars <- ifelse(p < .001, "****", ifelse(p < .001, "*** ", ifelse(p < .01, "**  ", ifelse(p < .05, "*   ", "    "))))
  
  ## trunctuate the correlation matrix to two decimal
  R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
  
  ## build a new matrix that includes the correlations with their apropriate stars
  Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
  diag(Rnew) <- paste(diag(R), " ", sep="")
  rownames(Rnew) <- colnames(x)
  colnames(Rnew) <- paste(colnames(x), "", sep="")
  
  ## remove upper triangle of correlation matrix
  if(removeTriangle[1]=="upper"){
    Rnew <- as.matrix(Rnew)
    Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
    Rnew <- as.data.frame(Rnew)
  }
  
  ## remove lower triangle of correlation matrix
  else if(removeTriangle[1]=="lower"){
    Rnew <- as.matrix(Rnew)
    Rnew[lower.tri(Rnew, diag = TRUE)] <- ""
    Rnew <- as.data.frame(Rnew)
  }
  
  ## remove last column and return the correlation matrix
  Rnew <- cbind(Rnew[1:length(Rnew)-1])
  if (result[1]=="none") return(Rnew)
  else{
    if(result[1]=="html") print(xtable(Rnew), type="html")
    else print(xtable(Rnew), type="latex") 
  }
} 




d1.sub <- d1[ c("MEDIANHOUSEINCOME","POVRATE","PBLACK","PASIAN",
                "PSNAP","PVACANT","POWNER","PUNEMPLOYED") ]

library( xtable )

corstars( d1.sub, result="html" )

	MEDIANHOUSEINCOME	POVRATE	PBLACK	PASIAN	PSNAP	PVACANT	POWNER
MEDIANHOUSEINCOME
POVRATE	-0.67****
PBLACK	-0.35****	0.40****
PASIAN	-0.20****	0.13*	-0.26****
PSNAP	-0.67****	0.66****	0.57****	0.07
PVACANT	-0.51****	0.44****	0.28****	0.29****	0.44****
POWNER	0.86****	-0.61****	-0.23****	-0.41****	-0.62****	-0.56****
PUNEMPLOYED	-0.36****	0.34****	0.44****	0.15*	0.56****	0.29****	-0.39****

d1.sub <- d1[ c("GEOID","MEDIANHOUSEINCOME","POVRATE","PBLACK","PASIAN",
                "PSNAP","PVACANT","POWNER","PUNEMPLOYED") ]

# d1.2000 <- d1[ d1$YEAR == 2000 , ]
d1.2015 <- d1[ d1$YEAR == 2015 , ]

dat <- merge( mhv, d1.2015, by.x="FIPS", by.y="GEOID" )

dat.sub <- dat[ c("MHV2000","CHANGE","MEDIANHOUSEINCOME","POVRATE","PBLACK","PASIAN",
                "PSNAP","PVACANT","POWNER","PUNEMPLOYED") ]

## put (absolute) correlations on the upper panels,
## with size proportional to the correlations.
panel.cor <- function(x, y, digits = 2, prefix = "", cex.cor, ...)
{
    usr <- par("usr"); on.exit(par(usr))
    par(usr = c(0, 1, 0, 1))
    r <- abs(cor(x, y, use="pairwise.complete.obs"))
    txt <- format(c(r, 0.123456789), digits = digits)[1]
    txt <- paste0(prefix, txt)
    if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
    text(0.5, 0.5, txt, cex = cex.cor * r)
}

panel.smooth <- function (x, y, col = par("col"), bg = NA, pch = par("pch"), 
    cex = 1, col.smooth = "red", span = 2/3, iter = 3, ...) 
{
    points(x, y, pch=19, col=gray(0.5,0.2), bg=bg, cex=cex )
    ok <- is.finite(x) & is.finite(y)
    if (any(ok)) 
        lines(stats::lowess(x[ok], y[ok], f = span, iter = iter), 
            col = col.smooth, ...)
}

pairs( dat.sub, lower.panel = panel.smooth, upper.panel = panel.cor)

# examine bivariate relationships
bivPlot <- function( x1, x2, lab1, lab2 )
{

    plot( x1, x2,
          pch=19, 
          col=gray(0.6, alpha = 0.3), 
          cex=3.5,  
          bty = "n",
          xlab=lab1, 
          ylab=lab2, cex.lab=1.5 )
  
  ok <- is.finite(x1) & is.finite(x2)
    lines( lowess(x2[ok]~x1[ok]), col="red", lwd=3 )

}

par( mfrow=c(2,2) )

bivPlot( x1=dat.sub$PUNEMPLOYED, x2=dat.sub$CHANGE, lab1="PERCENT UNEMPLOYED", lab2="CHANGE IN MHV" )
bivPlot( x1=dat.sub$PBLACK, x2=dat.sub$CHANGE, lab1="PERCENT BLACK", lab2="CHANGE IN MHV" )
bivPlot( x1=dat.sub$MEDIANHOUSEINCOME, x2=dat.sub$CHANGE, lab1="MEDIAN INCOME", lab2="CHANGE IN MHV" )
bivPlot( x1=dat.sub$PVACANT, x2=dat.sub$CHANGE, lab1="PERCENT VACANT", lab2="CHANGE IN MHV" )

Outlier Removal

dat.sub$CHANGE[ which.max(dat.sub$CHANGE) ] <- NA

par( mfrow=c(2,2) )

bivPlot( x1=dat.sub$PUNEMPLOYED, x2=dat.sub$CHANGE, lab1="PERCENT UNEMPLOYED", lab2="CHANGE IN MHV" )
bivPlot( x1=dat.sub$PBLACK, x2=dat.sub$CHANGE, lab1="PERCENT BLACK", lab2="CHANGE IN MHV" )
bivPlot( x1=dat.sub$MEDIANHOUSEINCOME, x2=dat.sub$CHANGE, lab1="MEDIAN INCOME", lab2="CHANGE IN MHV" )
bivPlot( x1=dat.sub$PVACANT, x2=dat.sub$CHANGE, lab1="PERCENT VACANT", lab2="CHANGE IN MHV" )

pairs( dat.sub, lower.panel = panel.smooth, upper.panel = panel.cor )

Merge Census Files

d1.2000 <- d1[ d1$YEAR == 2000 , ]
# d1.2010 <- d1[ d1$YEAR == 2010 , ]
# d1.2015 <- d1[ d1$YEAR == 2015 , ]


dat <- merge( mhv, d1.2000, by.x="FIPS", by.y="GEOID", all.x=T )

head( dat )

d1$GEOID

##   [1] 36067000100 36067000100 36067000100 36067000100 36067000100
##   [6] 36067001000 36067001000 36067001000 36067001000 36067001000
##  [11] 36067001400 36067001400 36067001400 36067001400 36067001400
##  [16] 36067001500 36067001500 36067001500 36067001500 36067001500
##  [21] 36067001600 36067001600 36067001600 36067001600 36067001600
##  [26] 36067001701 36067001701 36067001701 36067001701 36067001701
##  [31] 36067001702 36067001702 36067001702 36067001702 36067001702
##  [36] 36067001800 36067001800 36067001800 36067001800 36067001800
##  [41] 36067001900 36067001900 36067001900 36067001900 36067001900
##  [46] 36067000200 36067000200 36067000200 36067000200 36067000200
##  [51] 36067002000 36067002000 36067002000 36067002000 36067002000
##  [56] 36067002101 36067002101 36067002101 36067002101 36067002101
##  [61] 36067002300 36067002300 36067002300 36067002300 36067002300
##  [66] 36067002400 36067002400 36067002400 36067002400 36067002400
##  [71] 36067002700 36067002700 36067002700 36067002700 36067002700
##  [76] 36067002901 36067002901 36067002901 36067002901 36067002901
##  [81] 36067000300 36067000300 36067000300 36067000300 36067000300
##  [86] 36067003000 36067003000 36067003000 36067003000 36067003000
##  [91] 36067003200 36067003200 36067003200 36067003200 36067003200
##  [96] 36067003400 36067003400 36067003400 36067003400 36067003400
## [101] 36067003500 36067003500 36067003500 36067003500 36067003500
## [106] 36067003601 36067003601 36067003601 36067003601 36067003601
## [111] 36067003602 36067003602 36067003602 36067003602 36067003602
## [116] 36067003800 36067003800 36067003800 36067003800 36067003800
## [121] 36067003900 36067003900 36067003900 36067003900 36067003900
## [126] 36067000400 36067000400 36067000400 36067000400 36067000400
## [131] 36067004000 36067004000 36067004000 36067004000 36067004000
## [136] 36067004200 36067004200 36067004200 36067004200 36067004200
## [141] 36067004301 36067004301 36067004301 36067004301 36067004301
## [146] 36067004302 36067004302 36067004302 36067004302 36067004302
## [151] 36067004400 36067004400 36067004400 36067004400 36067004400
## [156] 36067004500 36067004500 36067004500 36067004500 36067004500
## [161] 36067004600 36067004600 36067004600 36067004600 36067004600
## [166] 36067004800 36067004800 36067004800 36067004800 36067004800
## [171] 36067004900 36067004900 36067004900 36067004900 36067004900
## [176] 36067000501 36067000501 36067000501 36067000501 36067000501
## [181] 36067005000 36067005000 36067005000 36067005000 36067005000
## [186] 36067005100 36067005100 36067005100 36067005100 36067005100
## [191] 36067005200 36067005200 36067005200 36067005200 36067005200
## [196] 36067005300 36067005300 36067005300 36067005300 36067005300
## [201] 36067005400 36067005400 36067005400 36067005400 36067005400
## [206] 36067005500 36067005500 36067005500 36067005500 36067005500
## [211] 36067005601 36067005601 36067005601 36067005601 36067005601
## [216] 36067005602 36067005602 36067005602 36067005602 36067005602
## [221] 36067005700 36067005700 36067005700 36067005700 36067005700
## [226] 36067005800 36067005800 36067005800 36067005800 36067005800
## [231] 36067005900 36067005900 36067005900 36067005900 36067005900
## [236] 36067000600 36067000600 36067000600 36067000600 36067000600
## [241] 36067006000 36067006000 36067006000 36067006000 36067006000
## [246] 36067006101 36067006101 36067006101 36067006101 36067006101
## [251] 36067006102 36067006102 36067006102 36067006102 36067006102
## [256] 36067006103 36067006103 36067006103 36067006103 36067006103
## [261] 36067000700 36067000700 36067000700 36067000700 36067000700
## [266] 36067000800 36067000800 36067000800 36067000800 36067000800
## [271] 36067000900 36067000900 36067000900 36067000900 36067000900
## [276]          NA          NA          NA          NA          NA
## [281]          NA          NA          NA          NA          NA
## [286]          NA          NA          NA          NA          NA
## [291]          NA          NA          NA          NA          NA
## [296]          NA          NA          NA          NA          NA
## [301]          NA          NA          NA          NA          NA
## [306]          NA          NA          NA          NA          NA
## [311]          NA          NA          NA          NA          NA
## [316]          NA          NA          NA          NA          NA
## [321]          NA          NA          NA          NA          NA
## [326]          NA          NA          NA          NA          NA
## [331]          NA          NA          NA          NA          NA
## [336]          NA          NA          NA          NA          NA
## [341]          NA          NA          NA          NA          NA
## [346]          NA          NA          NA          NA          NA
## [351]          NA          NA          NA          NA          NA
## [356]          NA          NA          NA          NA          NA
## [361]          NA          NA          NA          NA          NA
## [366]          NA          NA          NA          NA          NA
## [371]          NA          NA          NA          NA          NA
## [376]          NA          NA          NA          NA          NA
## [381]          NA          NA          NA          NA          NA
## [386]          NA          NA          NA          NA          NA
## [391]          NA          NA          NA          NA          NA
## [396]          NA          NA          NA          NA          NA
## [401]          NA          NA          NA          NA          NA
## [406]          NA          NA          NA          NA          NA
## [411]          NA          NA          NA          NA          NA
## [416]          NA          NA          NA          NA          NA
## [421]          NA          NA          NA          NA          NA
## [426]          NA          NA          NA          NA          NA
## [431]          NA          NA          NA          NA          NA
## [436]          NA          NA          NA          NA          NA
## [441]          NA          NA          NA          NA          NA
## [446]          NA          NA          NA

ntmaj5	ntmaj10	ntmaj12	NTEEconf	MAJGRPB
HU	HU	HU	B	L
AR	AR	AR	B	A
OT	PU	PU	B	T
HU	HU	HU	A	P
ED	ED	ED	A	B
OT	PU	PU	B	T

ntmaj5	ntmaj10	ntmaj12	NTEEconf	MAJGRPB
HU	HU	HU	B	L
AR	AR	AR	B	A
OT	PU	PU	B	T
HU	HU	HU	A	P
ED	ED	ED	A	B
OT	PU	PU	B	T

Compile Data

Create the Dependent Variable

Get Median Home Value from Census

Adjust Prices for Inflation

Descriptive Statistics

Map Changes in Home Values

Merge Data

Check Files in Directory

Read Aggregated Files

Analysis

Census Variables

Nice Correlation Tables

Outlier Removal

Merge Census Files

ntmaj5	ntmaj10	ntmaj12	NTEEconf	MAJGRPB
HU	HU	HU	B	L
AR	AR	AR	B	A
OT	PU	PU	B	T
HU	HU	HU	A	P
ED	ED	ED	A	B
OT	PU	PU	B	T