The `pointdexter`

package labels longitudinal and latitudinal coordinates located inside a polygon. This document introduces you to `pointdexter`

's two functions:

`GetPolygonBoundaries()`

; and`LabelPointsWithinPolygons()`

.

`pointdexter`

is compatible with the two packages most useful for working with spatial data: `sf`

and `sp`

. I'll only use the `sp`

package for this vignette.

# load necessary packages ----- library(pointdexter) # label coordinate pairs in polygons library(sp) # classes and methods for spatial data library(knitr) # simple table generator

`pointdexter`

comes with built-in point and polygon data - entirely due to the awesome and accessible Chicago Data Portal - to help you label points in polygons.

The coordinate pair data comes from the Chicago Public Schools (CPS) - School Profile Information, School Year (SY) 2018-2019 data set. Each coordinate pair represents a school.

# load necessary data ---- data("cps_sy1819") # store relevant columns ---- relevant.columns <- c("school_id", "short_name" , "school_longitude", "school_latitude") # print first few rows of data ---- kable(head(cps_sy1819[, relevant.columns]) , caption = "Table 1. Examining CPS SY1819 school profile data")

To show what `pointdexter`

does, we'll be using two types of spatial data from the City of Chicago: the city boundary and community area polygons.

While the city boundary is helpful for generating city-wide statistics, researchers typically use the 77 Chicago community areas when creating local-level statistics.

`pointdexter`

makes both polygons available as `sf`

and `SpatialPolygonsDataFrame`

objects.

# load city boundary data ---- data("city_boundary_spdf") # load community area data ---- data("community_areas_spdf") # visualize polygons ----- # note: clear plot space par(mar = c(0, 0, 1, 0)) # plot city boundary plot(city_boundary_spdf , main = "City of Chicago boundary" , col = "gray85" , border = "dodgerblue4") # plot community areas plot(community_areas_spdf , main = "Chicago's 77 community areas" , col = "gray85" , border = "dodgerblue4")

`GetPolygonBoundaries()`

returns the longitudinal and latitudinal points that make up the boundary of the polygon(s). The first argument is the polygon stored in the `sf`

or `SpatialPolygonsDataFrame`

object.

If `my.polygon`

only contains one polygon, a matrix of coordinate pairs will be returned.

# create coordinate pair matrix for city of chicago boundary ---- boundary <- GetPolygonBoundaries(my.polygon = city_boundary_spdf) # print first few records ---- kable(head(boundary) , caption = "Table 2. boundary is a matrix of coordinate pairs" , col.names = c("long", "lat"))

Otherwise, a list of labeled matrices, with each matrix representing the coordinate pairs that make the boundary of each particular polygon in `my.polygon`

.

# create list of coordinate pair matrices for each community area ---- community.area.boundaries <- GetPolygonBoundaries(my.polygon = community_areas_spdf , labels = community_areas_spdf$community) # print first few records for two communities ---- kable(lapply(community.area.boundaries[c("AUSTIN", "WEST ELSDON")] , FUN = head) , caption = "Table 3. Austin (left) and West Elsdon's (right) boundaries" , col.names = c("long", "lat"))

`LabelPointsWithinPolygons()`

identifies which longitudinal and latitudinal points lie within the polygon boundaries created from `GetPolygonBoundaries()`

.

The first two arguments of `LabelPointsWithinPolygons()`

are the longitude and latitude columns that create your coordinate pairs of interest. The final argument - `polygon.boundaries`

is the object you created from `GetPolygonBoundaries()`

.

`polygon.boundaries`

is a matrixIf `polygon.boundaries`

is a coordinate pair matrix, a logical vector will be returned identifying those points which lie in the polygon.

# identify cps schools that lie in Chicago ---- cps_sy1819$in_chicago <- LabelPointsWithinPolygons(lng = cps_sy1819$school_longitude , lat = cps_sy1819$school_latitude , polygon.boundaries = boundary) # show first few records ---- kable(head(cps_sy1819[, c(relevant.columns, "in_chicago")]) , caption = "Table 4. A logical vector is returned when polygon.boundaries is a matrix")

`polygon.boundaries`

is a list of matricesOtherwise, a character vector will be returned identifying those points that lie in each polygon.

# identify the community that each cps school lies in ---- cps_sy1819$community <- LabelPointsWithinPolygons(lng = cps_sy1819$school_longitude , lat = cps_sy1819$school_latitude , polygon.boundaries = community.area.boundaries) # show first few records ---- kable(head(cps_sy1819[, c(relevant.columns, "in_chicago", "community")]) , caption = "Table 5. A character vector is returned when polygon.boundaries is a list of labeled matrices")

`pointdexter`

finds the boundaries of whatever polygon you give so that you can identify coordinate pairs that lie within it. This is useful when wanting to generate local statistics for particular communities.

# identify the school ratings for high schools in Austin ---- # filter cps schools austin.hs <- cps_sy1819[cps_sy1819$community == "AUSTIN" & cps_sy1819$is_high_school, ] # arrange data by overall rating austin.hs <- austin.hs[order(austin.hs$overall_rating), ] # show results kable(austin.hs[, c(relevant.columns , "overall_rating", "is_high_school", "community")] , caption = "Table 6. Austin's highest rank high school is YCCS - Scholastic Academy, SY1819" , row.names = FALSE)

```
sessionInfo()
```

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