An Interactive Crime Prediction Map around Camden City, London: Rstudio + Mapview + Raster

Import csv file and view data structure

LondonCrime =read.csv("LondonCrime/On_Street_Crime_In_Camden.csv", header=T,sep=",")

Create spatial object

coordinates(LondonCrime_final_df) = ~Longitude + Latitude
class(LondonCrime_final_df)

## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"

proj4string(LondonCrime_final_df) = CRS("+init=epsg:4124")
extent(LondonCrime_final_df)

## class      : Extent 
## xmin       : -0.212576 
## xmax       : -0.105902 
## ymin       : 51.51286 
## ymax       : 51.57252

(LondonCrime_final_df)

## class       : SpatialPointsDataFrame 
## features    : 2981 
## extent      : -0.212576, -0.105902, 51.51286, 51.57252  (xmin, xmax, ymin, ymax)
## coord. ref. : +init=epsg:4124 +proj=longlat +ellps=bessel +towgs84=414.1,41.3,603.1,-0.855,2.141,-7.023,0 +no_defs 
## variables   : 14
## # A tibble: 2,981 x 14
##        X Location Street.ID Ward.Name Easting Northing Bicycle.theft
##    <int> <fct>        <int> <fct>       <dbl>    <dbl>         <int>
##  1     1 (51.512~    956440 Holborn ~ 530004.  180986.             1
##  2     2 (51.512~    956590 Holborn ~ 529998.  181001.             1
##  3     3 (51.512~    956590 Holborn ~ 529995.  181002.             1
##  4     4 (51.513~    956439 Holborn ~ 529982.  181008.             2
##  5     5 (51.513~    956587 Holborn ~ 530043.  181013.             3
##  6     6 (51.513~    956587 Holborn ~ 530040.  181014.             3
##  7     7 (51.513~    956444 Holborn ~ 530055.  181024.             5
##  8     8 (51.513~    956444 Holborn ~ 530058.  181023.             5
##  9     9 (51.513~    956447 Holborn ~ 530012.  181028.             6
## 10    10 (51.513~    956447 Holborn ~ 530015.  181027.             6
## # ... with 2,971 more rows, and 7 more variables: Burglary <int>,
## #   Drugs <int>, Robbery <int>, Shoplifting <int>,
## #   Theft.from.the.person <int>, Violence <int>,
## #   Violence.and.sexual.offences <int>

IDW interpolation

max_Longitude = max(LondonCrime$Longitude)
min_Longitude = min(LondonCrime$Longitude)

max_Latitude = max(LondonCrime$Latitude)
min_Latitude = min(LondonCrime$Latitude)

x_range = as.numeric(c(min_Longitude, max_Longitude))
y_range = as.numeric(c(min_Latitude, max_Latitude))

Create an empty grid of values ranging from the xmin-xmax, ymin-ymax

grd = expand.grid(x = seq(from = x_range[1],to = x_range[2],  by = 1/2000),
                  y = seq(from = y_range[1],to = y_range[2],  by = 1/2000))


coordinates(grd) = ~x+ y
gridded(grd) =  TRUE
proj4string(grd) = CRS("+init=epsg:4124")

Interpolate LondonCrime_Drugs data points

idw_LondonCrime_Drugs = idw(formula = Drugs ~ 1, locations = LondonCrime_final_df, newdata = grd, idp = 1)

## [inverse distance weighted interpolation]

plot(idw_LondonCrime_Drugs, col =  terrain.colors(50, rev = TRUE) )

idw_LondonCrime_Drugs_raster = raster(idw_LondonCrime_Drugs)

Interpolate LondonCrime_Violence data points

idw_LondonCrime_Violence = idw(formula = Violence ~ 1, locations = LondonCrime_final_df, newdata = grd, idp = 1)

## [inverse distance weighted interpolation]

plot(idw_LondonCrime_Violence, col =  heat.colors(50, rev = TRUE) )

idw_LondonCrime_Violence_raster = raster(idw_LondonCrime_Violence)

Interpolate LondonCrime_Robbery data points

idw_LondonCrime_Robbery = idw(formula = Robbery ~ 1, locations = LondonCrime_final_df, newdata = grd, idp = 1)

## [inverse distance weighted interpolation]

plot(idw_LondonCrime_Robbery, col =  cm.colors(50) )

idw_LondonCrime_Robbery_raster = raster(idw_LondonCrime_Robbery)

An Interactive Crime Prediction Map: Crime in Camden City, London

#mapview(basemaps =  c("CartoDB.Positron","OpenStreetMap","OpenTopoMap" ))
mapview(idw_LondonCrime_Drugs_raster, col = terrain.colors(50 , rev = TRUE), alpha.regions = 0.3  ) + mapview(idw_LondonCrime_Violence_raster, col = heat.colors(50, rev = TRUE), alpha.regions = 0.3  ) + mapview(idw_LondonCrime_Robbery_raster, col = cm.colors(50), alpha.regions = 0.3  ) + viewExtent(idw_LondonCrime_Drugs_raster, alpha.regions = 0.1 )