class: left, middle, inverse background-image: url("https://live.staticflickr.com/65535/50362989122_a8ee154fea_k_d.jpg") background-size: cover # .green[Lines & Polygons <svg style="height:0.8em;top:.04em;position:relative;fill:limegreen;" viewBox="0 0 448 512"><path d="M384 352c-.35 0-.67.1-1.02.1l-39.2-65.32c5.07-9.17 8.22-19.56 8.22-30.78s-3.14-21.61-8.22-30.78l39.2-65.32c.35.01.67.1 1.02.1 35.35 0 64-28.65 64-64s-28.65-64-64-64c-23.63 0-44.04 12.95-55.12 32H119.12C108.04 44.95 87.63 32 64 32 28.65 32 0 60.65 0 96c0 23.63 12.95 44.04 32 55.12v209.75C12.95 371.96 0 392.37 0 416c0 35.35 28.65 64 64 64 23.63 0 44.04-12.95 55.12-32h209.75c11.09 19.05 31.49 32 55.12 32 35.35 0 64-28.65 64-64 .01-35.35-28.64-64-63.99-64zm-288 8.88V151.12A63.825 63.825 0 0 0 119.12 128h208.36l-38.46 64.1c-.35-.01-.67-.1-1.02-.1-35.35 0-64 28.65-64 64s28.65 64 64 64c.35 0 .67-.1 1.02-.1l38.46 64.1H119.12A63.748 63.748 0 0 0 96 360.88zM272 256c0-8.82 7.18-16 16-16s16 7.18 16 16-7.18 16-16 16-16-7.18-16-16zM400 96c0 8.82-7.18 16-16 16s-16-7.18-16-16 7.18-16 16-16 16 7.18 16 16zM64 80c8.82 0 16 7.18 16 16s-7.18 16-16 16-16-7.18-16-16 7.18-16 16-16zM48 416c0-8.82 7.18-16 16-16s16 7.18 16 16-7.18 16-16 16-16-7.18-16-16zm336 16c-8.82 0-16-7.18-16-16s7.18-16 16-16 16 7.18 16 16-7.18 16-16 16z"/></svg>] ### .fancy[Mere collections of points] --- # Learning Objectives <svg style="height:0.8em;top:.04em;position:relative;fill:red;" viewBox="0 0 576 512"><path d="M386.539 111.485l15.096 248.955-10.979-.275c-36.232-.824-71.64 8.783-102.657 27.997-31.016-19.214-66.424-27.997-102.657-27.997-45.564 0-82.07 10.705-123.516 27.723L93.117 129.6c28.546-11.803 61.484-18.115 92.226-18.115 41.173 0 73.836 13.175 102.657 42.544 27.723-28.271 59.013-41.721 98.539-42.544zM569.07 448c-25.526 0-47.485-5.215-70.542-15.645-34.31-15.645-69.993-24.978-107.871-24.978-38.977 0-74.934 12.901-102.657 40.623-27.723-27.723-63.68-40.623-102.657-40.623-37.878 0-73.561 9.333-107.871 24.978C55.239 442.236 32.731 448 8.303 448H6.93L49.475 98.859C88.726 76.626 136.486 64 181.775 64 218.83 64 256.984 71.685 288 93.095 319.016 71.685 357.17 64 394.225 64c45.289 0 93.049 12.626 132.3 34.859L569.07 448zm-43.368-44.741l-34.036-280.246c-30.742-13.999-67.248-21.41-101.009-21.41-38.428 0-74.385 12.077-102.657 38.702-28.272-26.625-64.228-38.702-102.657-38.702-33.761 0-70.267 7.411-101.009 21.41L50.298 403.259c47.211-19.487 82.894-33.486 135.045-33.486 37.604 0 70.817 9.606 102.657 29.644 31.84-20.038 65.052-29.644 102.657-29.644 52.151 0 87.834 13.999 135.045 33.486z"/></svg> This section covers the following topics: - Creation of `sf::LINESTRING` and `sf::MULTIPOLYGON` objects from raw data. - Loading map polygons from `get_data()` - Importing data from ESRI shapefile. - Implementing Spatial Joins. --- class: sectionTitle, inverse # .green[Lines] ### .fancy[Collections of Points <svg style="height:0.8em;top:.04em;position:relative;fill:limegreen;" viewBox="0 0 320 512"><path d="M313.553 119.669L209.587 7.666c-9.485-10.214-25.676-10.229-35.174 0L70.438 119.669C56.232 134.969 67.062 160 88.025 160H152v272H68.024a11.996 11.996 0 0 0-8.485 3.515l-56 56C-4.021 499.074 1.333 512 12.024 512H208c13.255 0 24-10.745 24-24V160h63.966c20.878 0 31.851-24.969 17.587-40.331z"/></svg>] --- # Data Sources: *De novo* Creation It is possible to make data from scratch by entering coordinates directly. ```r df <- data.frame( id = c(rep("Rodney",5), rep("Sarah",5)), Data = rnorm( 10, 42, 42), Longitude = rnorm(10, -78, 1 ), Latitude = rnorm(10, 37, 1) ) df ``` ``` ## id Data Longitude Latitude ## 1 Rodney 48.692540 -77.61316 38.60222 ## 2 Rodney 17.418585 -76.51469 36.46820 ## 3 Rodney -15.416593 -77.84600 35.92128 ## 4 Rodney 52.846328 -78.17813 36.91184 ## 5 Rodney -12.350596 -77.49683 38.35502 ## 6 Sarah 7.175368 -77.16956 38.04888 ## 7 Sarah 21.013859 -75.95531 36.37788 ## 8 Sarah 20.233248 -78.62658 36.26965 ## 9 Sarah 73.728392 -75.95117 38.86707 ## 10 Sarah 17.330654 -78.31971 38.01433 ``` --- # Data Sources: *De novo* Creation To Make to `sf` as `POINT` objects as normal. ```r df %>% st_as_sf( coords = c("Longitude", "Latitude"), crs=4326 ) -> pts pts ``` ``` ## Simple feature collection with 10 features and 2 fields ## geometry type: POINT ## dimension: XY ## bbox: xmin: -78.62658 ymin: 35.92128 xmax: -75.95117 ymax: 38.86707 ## geographic CRS: WGS 84 ## id Data geometry ## 1 Rodney 48.692540 POINT (-77.61316 38.60222) ## 2 Rodney 17.418585 POINT (-76.51469 36.4682) ## 3 Rodney -15.416593 POINT (-77.846 35.92128) ## 4 Rodney 52.846328 POINT (-78.17813 36.91184) ## 5 Rodney -12.350596 POINT (-77.49683 38.35502) ## 6 Sarah 7.175368 POINT (-77.16956 38.04888) ## 7 Sarah 21.013859 POINT (-75.95531 36.37788) ## 8 Sarah 20.233248 POINT (-78.62658 36.26965) ## 9 Sarah 73.728392 POINT (-75.95117 38.86707) ## 10 Sarah 17.330654 POINT (-78.31971 38.01433) ``` --- # Plotting Points .pull-left[ ### GGPLot ```r ggplot( pts ) + geom_sf( aes(color = id, size=Data)) + coord_sf() ``` ] .pull-right[ <img src="slides_files/figure-html/unnamed-chunk-4-1.png" width="504" style="display: block; margin: auto;" /> ] --- # Data Sources: *De novo* Creation To make `sf` as `LINESTRING` we can group by the id value and then we need to summarize the `Data` component, then cast it to a Linestring. ```r df %>% st_as_sf( coords = c("Longitude", "Latitude"), crs=4326 ) %>% group_by( id ) %>% summarize( Data = mean( Data ) ) %>% st_cast("LINESTRING") -> lines lines ``` ``` ## Simple feature collection with 2 features and 2 fields ## geometry type: LINESTRING ## dimension: XY ## bbox: xmin: -78.62658 ymin: 35.92128 xmax: -75.95117 ymax: 38.86707 ## geographic CRS: WGS 84 ## # A tibble: 2 x 3 ## id Data geometry ## <chr> <dbl> <LINESTRING [°]> ## 1 Rodney 18.2 (-78.17813 36.91184, -77.846 35.92128, -77.61316 38.60222, -77.4… ## 2 Sarah 27.9 (-78.62658 36.26965, -78.31971 38.01433, -77.16956 38.04888, -75… ``` --- # Visualizing Line Objects .pull-left[ ```r plot( lines["Data"], col=c("red","green"), lwd = 2) ``` ] .pull-right[ <img src="slides_files/figure-html/unnamed-chunk-7-1.png" width="504" style="display: block; margin: auto;" /> ] --- # Operations on `LINESTRING` Objects ```r st_length( lines ) ``` ``` ## Units: [m] ## [1] 668132.3 787347.3 ``` ```r st_bbox( lines ) ``` ``` ## xmin ymin xmax ymax ## -78.62658 35.92128 -75.95117 38.86707 ``` ```r library( units ) lines %>% st_union() %>% st_convex_hull() %>% st_area() %>% set_units( km^2 ) ``` ``` ## 60594.98 [km^2] ``` --- # Textual Versions of `geometry` Objects While it is easy to go from text `\(\to\)` `POLYGON`, the same thing can be done going in the opposite direction. ```r lines$geometry[1] %>% st_as_text() ``` ``` ## [1] "LINESTRING (-78.17813 36.91184, -77.846 35.92128, -77.61316 38.60222, -77.49683 38.35502, -76.51469 36.4682)" ``` --- class: sectionTitle, inverse # .green[Polygons] ### .fancy[The snake that is eating its tail. <svg style="height:0.8em;top:.04em;position:relative;fill:limegreen;" viewBox="0 0 512 512"><path d="M288 39.056v16.659c0 10.804 7.281 20.159 17.686 23.066C383.204 100.434 440 171.518 440 256c0 101.689-82.295 184-184 184-101.689 0-184-82.295-184-184 0-84.47 56.786-155.564 134.312-177.219C216.719 75.874 224 66.517 224 55.712V39.064c0-15.709-14.834-27.153-30.046-23.234C86.603 43.482 7.394 141.206 8.003 257.332c.72 137.052 111.477 246.956 248.531 246.667C393.255 503.711 504 392.788 504 256c0-115.633-79.14-212.779-186.211-240.236C302.678 11.889 288 23.456 288 39.056z"/></svg>] --- # Polygons .red[Polygons are simply lines whose first and last coordinate are exactly the same] ```r df.p <- df[ c(1:5,1, 6:10,6),] df.p ``` ``` ## id Data Longitude Latitude ## 1 Rodney 48.692540 -77.61316 38.60222 ## 2 Rodney 17.418585 -76.51469 36.46820 ## 3 Rodney -15.416593 -77.84600 35.92128 ## 4 Rodney 52.846328 -78.17813 36.91184 ## 5 Rodney -12.350596 -77.49683 38.35502 ## 1.1 Rodney 48.692540 -77.61316 38.60222 ## 6 Sarah 7.175368 -77.16956 38.04888 ## 7 Sarah 21.013859 -75.95531 36.37788 ## 8 Sarah 20.233248 -78.62658 36.26965 ## 9 Sarah 73.728392 -75.95117 38.86707 ## 10 Sarah 17.330654 -78.31971 38.01433 ## 6.1 Sarah 7.175368 -77.16956 38.04888 ``` --- # Polygon *de novo* Creation ```r df %>% st_as_sf( coords = c("Longitude", "Latitude"), crs=4326 ) %>% group_by( id ) %>% summarize( Data = mean( Data ) ) %>% st_cast("POLYGON") -> polygons polygons ``` ``` ## Simple feature collection with 2 features and 2 fields ## geometry type: POLYGON ## dimension: XY ## bbox: xmin: -78.62658 ymin: 35.92128 xmax: -75.95117 ymax: 38.86707 ## geographic CRS: WGS 84 ## # A tibble: 2 x 3 ## id Data geometry ## <chr> <dbl> <POLYGON [°]> ## 1 Rodney 18.2 ((-78.17813 36.91184, -77.846 35.92128, -77.61316 38.60222, -77.… ## 2 Sarah 27.9 ((-78.62658 36.26965, -78.31971 38.01433, -77.16956 38.04888, -7… ``` --- # Visualizing Polygon Objects ```r ggplot( polygons ) + geom_sf( aes( fill = Data ), alpha=0.75 ) + coord_sf() ``` <img src="slides_files/figure-html/unnamed-chunk-14-1.png" width="504" style="display: block; margin: auto;" /> --- # Polygons From Data Frames ```r map_data( "county", "virginia") %>% dplyr::select( Longitude = long, Latitude = lat, group, County = subregion) -> va_counties head( va_counties ) ``` ``` ## Longitude Latitude group County ## 1 -75.27519 38.03867 1 accomack ## 2 -75.21790 38.02721 1 accomack ## 3 -75.21790 38.02721 1 accomack ## 4 -75.24655 37.99283 1 accomack ## 5 -75.30384 37.94127 1 accomack ## 6 -75.31530 37.92981 1 accomack ``` --- ```r ggplot( va_counties, aes( Longitude, Latitude) ) + geom_point( size=0.25 ) + coord_quickmap() ``` <img src="slides_files/figure-html/unnamed-chunk-16-1.png" width="864" style="display: block; margin: auto;" /> .footnote[`coord_quickmap()` is quick coordinate space projection.] --- ```r va_counties %>% filter( County %in% c("hanover","henrico") ) %>% ggplot( aes(Longitude, Latitude) ) + geom_polygon( aes( fill = County), alpha=0.1 ) + geom_point( aes( color = County) ) + coord_quickmap() ``` <img src="slides_files/figure-html/unnamed-chunk-17-1.png" width="504" style="display: block; margin: auto;" /> --- ```r ggplot( va_counties, aes( Longitude, Latitude) ) + geom_polygon( aes( group=group ), fill="grey80", color = "black", size = 0.25) + coord_quickmap() ``` <img src="slides_files/figure-html/unnamed-chunk-18-1.png" width="720" style="display: block; margin: auto;" /> --- ```r ggplot() + geom_polygon( aes( Longitude, Latitude, group=group ), fill="grey80", color = "black", size = 0.25, data=va_counties) + geom_sf( aes( color=Data), lwd=3, data=lines) + coord_sf() ``` <img src="slides_files/figure-html/unnamed-chunk-19-1.png" width="504" style="display: block; margin: auto;" /> --- class: sectionTitle, inverse # .green[Shapefiles 🤷] ### .fancy[The standard format that is neither standard or a file. ️] --- # Shapefiles The ESRI *Shapefile* is a common format for packaging vector based data (`POINT`, `LINESTRING`, `POLYGON`, etc.). However, it is not actually a file but it is a collection of files, which may (or may not) expand directly in the folder or within a subfolder.  --- # Archived ZIP Files. I've uploaded some shapefiles to the [Github Repository](https://github.com/dyerlab/ENVS-Lectures) for this class. These are from the Richmond City GIS Department and represent the centerlines of roads in the city as well as the zoning districts. Here are the URL's for both of these files. ```r roads_url <- "https://github.com/dyerlab/ENVS-Lectures/raw/master/data/Centerlines-shp.zip" district_url <- "https://github.com/dyerlab/ENVS-Lectures/raw/master/data/Zoning_Districts-shp.zip" ``` --- # Downloading Files Using R You can use your browser and copy and paste those addresses in to download the files to your computer. Then you'll have to move those archives to your PROJECT FOLDER (you are using Projects right?). -- `OR` You can have `R` download the file and save it locally. ```r download.file( district_url , destfile = "./Districts.zip") download.file( roads_url, destfile = "./Roads.zip") ``` --- # Unziping Files You can open your computer finder and have the OS unzip the archives. `OR` You can have `R` do it. ```r unzip("Districts.zip") unzip("Roads.zip") ``` --- # File Components   --- # Loading A Shapefile You can load it in using `sf::st_read()` and passing it the `.shp` file path to it. ```r st_read("Zoning_Districts.shp") -> districts ``` ``` ## Reading layer `Zoning_Districts' from data source `/Users/rodney/Documents/github/classes/ENVS-Lectures/lectures/spatial/spatial_polygons/Zoning_Districts.shp' using driver `ESRI Shapefile' ## Simple feature collection with 634 features and 14 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 11743500 ymin: 3687944 xmax: 11806060 ymax: 3744741 ## projected CRS: NAD83 / Virginia South (ftUS) ``` --- # Shapefiles Convert to `sf` Objects By default, the data associated with a vector object are put into a `data.frame` and the `geometry` object is properly created. ```r head( districts, n=2 ) ``` ``` ## Simple feature collection with 2 features and 14 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 11773600 ymin: 3730159 xmax: 11789510 ymax: 3731016 ## projected CRS: NAD83 / Virginia South (ftUS) ## OBJECTID Name Ordinance OrdinanceP Conditiona AdoptionDa Comment ## 1 1 RO-2 <NA> <NA> No 2000-01-01 <NA> ## 2 2 B-2 <NA> <NA> No 2000-01-01 <NA> ## CreatedBy CreatedDat EditBy EditDate ## 1 richard.morton_cor 2020-08-24 richard.morton_cor 2020-08-24 ## 2 richard.morton_cor 2020-08-24 richard.morton_cor 2020-08-24 ## GlobalID Shape__Are Shape__Len ## 1 334799f0-fe38-46bf-97c2-260f5a036559 60150.29 983.6815 ## 2 558df9cd-4f9c-4248-a689-bc2d9c79d060 56987.01 971.8832 ## geometry ## 1 MULTIPOLYGON (((11773598 37... ## 2 MULTIPOLYGON (((11789222 37... ``` --- # Simplifying Data ```r names( districts ) ``` ``` ## [1] "OBJECTID" "Name" "Ordinance" "OrdinanceP" "Conditiona" ## [6] "AdoptionDa" "Comment" "CreatedBy" "CreatedDat" "EditBy" ## [11] "EditDate" "GlobalID" "Shape__Are" "Shape__Len" "geometry" ``` -- ```r districts %>% dplyr::select( -Comment, -CreatedBy, -CreatedDat, -EditBy, -EditDate ) %>% dplyr::select( -Shape__Are, -Shape__Len) -> districts head( districts, n=1) ``` ``` ## Simple feature collection with 1 feature and 7 fields ## geometry type: MULTIPOLYGON ## dimension: XY ## bbox: xmin: 11773600 ymin: 3730159 xmax: 11773940 ymax: 3730493 ## projected CRS: NAD83 / Virginia South (ftUS) ## OBJECTID Name Ordinance OrdinanceP Conditiona AdoptionDa ## 1 1 RO-2 <NA> <NA> No 2000-01-01 ## GlobalID geometry ## 1 334799f0-fe38-46bf-97c2-260f5a036559 MULTIPOLYGON (((11773598 37... ``` --- # District Codes ```r districts$Name %>% unique() %>% sort() ``` ``` ## [1] "B-1" "B-2" "B-3" "B-4" "B-5" "B-6" "B-7" ## [8] "CM" "DCC" "I" "M-1" "M-2" "OS" "R-1" ## [15] "R-2" "R-3" "R-4" "R-43" "R-48" "R-5" "R-53" ## [22] "R-6" "R-63" "R-7" "R-73" "R-8" "R-MH" "RF-1" ## [29] "RF-2" "RO-1" "RO-2" "RO-3" "RO2-PE2" "RO2-PE4" "RP" ## [36] "TOD-1" "UB" "UB-2" "UB-PE2" "UB-PE3" "UB-PE4" "UB-PE5" ## [43] "UB-PE6" "UB-PE7" "UB-PE8" "UB-PO1" "UB-PO2" "UB-PO3" "UB-PO4" ## [50] "UB2-PE8" ``` --- # Visualizing ```r plot( districts["Name"] ) ``` <img src="slides_files/figure-html/unnamed-chunk-28-1.png" width="504" style="display: block; margin: auto;" /> --- # Visualizing Overlays ```r plot( st_geometry( districts )) plot( st_geometry( districts[districts$OBJECTID==530,] ), col='red', add=TRUE ) ``` <img src="slides_files/figure-html/unnamed-chunk-29-1.png" width="504" style="display: block; margin: auto;" /> --- class: sectionTitle, inverse # .green[Vector Operations] ### .fancy[Spatial Joins & Other Fun <svg style="height:0.8em;top:.04em;position:relative;fill:orange;" viewBox="0 0 448 512"><path d="M448 384c-28.02 0-31.26-32-74.5-32-43.43 0-46.825 32-74.75 32-27.695 0-31.454-32-74.75-32-42.842 0-47.218 32-74.5 32-28.148 0-31.202-32-74.75-32-43.547 0-46.653 32-74.75 32v-80c0-26.5 21.5-48 48-48h16V112h64v144h64V112h64v144h64V112h64v144h16c26.5 0 48 21.5 48 48v80zm0 128H0v-96c43.356 0 46.767-32 74.75-32 27.951 0 31.253 32 74.75 32 42.843 0 47.217-32 74.5-32 28.148 0 31.201 32 74.75 32 43.357 0 46.767-32 74.75-32 27.488 0 31.252 32 74.5 32v96zM96 96c-17.75 0-32-14.25-32-32 0-31 32-23 32-64 12 0 32 29.5 32 56s-14.25 40-32 40zm128 0c-17.75 0-32-14.25-32-32 0-31 32-23 32-64 12 0 32 29.5 32 56s-14.25 40-32 40zm128 0c-17.75 0-32-14.25-32-32 0-31 32-23 32-64 12 0 32 29.5 32 56s-14.25 40-32 40z"/></svg> ️] --- # Spatial Joins In the topic covering [Relational Operations], we used *Primary* and *Foreign* Keys to join `data.frame` objects and combine data. We can do simliar operations using geospatial positions to perform `spatial joins`. There are a wide variety of operations available: .center[.red[[Check out the Cheetsheet](https://github.com/rstudio/cheatsheets/blob/master/sf.pdf)]] --- # Secondary Vector Data Set ```r road.shapefile <- st_read("Centerlines-shp/tran_Carriageway.shp") ``` ``` ## Reading layer `tran_Carriageway' from data source `/Users/rodney/Documents/github/classes/ENVS-Lectures/lectures/spatial/spatial_polygons/Centerlines-shp/tran_Carriageway.shp' using driver `ESRI Shapefile' ## Simple feature collection with 29081 features and 25 fields ## geometry type: LINESTRING ## dimension: XY ## bbox: xmin: 11734060 ymin: 3682790 xmax: 11817490 ymax: 3751927 ## projected CRS: NAD83 / Virginia South (ftUS) ``` -- ```r names( road.shapefile ) ``` ``` ## [1] "FID" "Carriagewa" "AssetID" "StreetType" "Functional" ## [6] "FIPS" "LeftFromAd" "LeftToAddr" "RightFromA" "RightToAdd" ## [11] "PrefixDire" "ProperName" "SuffixType" "SuffixDire" "FullName" ## [16] "RouteName" "OneWay" "PostedSpee" "CADRouteSp" "CreatedBy" ## [21] "CreatedDat" "EditBy" "EditDate" "GlobalID" "SHAPE_Leng" ## [26] "geometry" ``` --- # Check Projections .red[Dyer's First Rule: Make Sure Projections Are the Same] ```r st_crs( road.shapefile ) == st_crs( districts ) ``` ``` ## [1] TRUE ``` -- If they differed, one could do something like: ```r road.shapefile %>% st_transform( crs = st_crs( districts ) ) -> road.shapefile ``` --- # Data Cleanup ```r road.shapefile %>% dplyr::select( FullName, OneWay, StreetType, SpeedLimit = PostedSpee, Length = SHAPE_Leng, geometry) %>% mutate( OneWay = factor( OneWay ), StreetType = factor( StreetType) ) -> roads summary( roads ) ``` ``` ## FullName OneWay StreetType SpeedLimit ## Length:29081 1 : 2 Alley : 3139 Min. : 0.00 ## Class :character FT : 3225 Artery : 5201 1st Qu.:25.00 ## Mode :character TF : 3148 Highway : 384 Median :25.00 ## NA's:22706 Highway Interchange: 93 Mean :25.17 ## Private : 1540 3rd Qu.:25.00 ## Ramp : 378 Max. :55.00 ## Secondary :18346 NA's :7959 ## Length geometry ## Min. : 2.943 LINESTRING :29081 ## 1st Qu.: 164.849 epsg:2284 : 0 ## Median : 292.517 +proj=lcc ...: 0 ## Mean : 379.338 ## 3rd Qu.: 461.146 ## Max. :17851.326 ## ``` --- # Visualizing .pull-left[ ### Filter Highways & Plot ```r roads %>% filter( StreetType %in% c("Highway", "Highway Interchange", "Ramp")) -> highways plot( highways["SpeedLimit"], col=heat.colors(56) ) ``` ] .pull-right[ <img src="slides_files/figure-html/unnamed-chunk-36-1.png" width="504" style="display: block; margin: auto;" /> ] --- # Visualizing A Single Entity .pull-left[ ### Using Normal Filtering ```r roads %>% filter( FullName == "Three Chopt Road") %>% ggplot() + geom_sf( aes(color=SpeedLimit), lwd=2) + coord_sf() ``` ] .pull-right[ <img src="slides_files/figure-html/unnamed-chunk-38-1.png" width="504" style="display: block; margin: auto;" /> ] --- # Intersection Plotting Let's grab a bit of zoning from *The Fan* ```r districts %>% filter( OBJECTID == 368 ) %>% st_buffer(dist = 1500) %>% st_bbox() -> fan_bbox districts %>% st_crop( fan_bbox ) -> theFan ``` --- ```r plot( theFan["Name"]) ``` <img src="slides_files/figure-html/unnamed-chunk-40-1.png" width="504" style="display: block; margin: auto;" /> --- # Add Auxillary Data ```r zone_url <- "https://raw.githubusercontent.com/dyerlab/ENVS-Lectures/master/data/DistrictCodes.csv" theFan %>% left_join( read_csv( zone_url ), by="Name") %>% mutate( Category = factor( Category) ) %>% select( OBJECTID, Name, Category, everything() ) -> theFan ``` --- ```r ggplot( theFan ) + geom_sf( aes( fill=Category)) + scale_fill_brewer( type="qual", palette = "Set3") ``` <img src="slides_files/figure-html/unnamed-chunk-42-1.png" width="864" style="display: block; margin: auto;" /> --- # Cropping Roads ```r roads %>% st_crop( st_bbox( theFan )) -> fanRoads plot( st_geometry( fanRoads )) ``` <img src="slides_files/figure-html/unnamed-chunk-43-1.png" width="504" style="display: block; margin: auto;" /> --- # Selecting a Single Zone .pull-left[ Add an attribute to the POLYGON's indicating if the district is that big one in the middle of the plot. ```r theFan %>% mutate( Target = ifelse( OBJECTID == 368, TRUE, FALSE) ) -> theFan theFan %>% ggplot() + geom_sf( aes(fill=Target) ) + geom_sf_text( aes(label=OBJECTID), size=3 ) + coord_sf() ``` ] .pull-right[ <img src="slides_files/figure-html/unnamed-chunk-45-1.png" width="504" style="display: block; margin: auto;" /> ] --- # Isolate the District ```r target <- theFan[ theFan$OBJECTID == 368, ] plot( st_geometry( target ) ) ``` <img src="slides_files/figure-html/unnamed-chunk-46-1.png" width="504" style="display: block; margin: auto;" /> --- ```r fanRoads %>% filter( st_intersects( fanRoads, target, sparse = FALSE ) == TRUE ) %>% as_data_frame() %>% select( `Street Name` = FullName ) %>% arrange( `Street Name`) %>% unique() ``` ``` ## # A tibble: 39 x 1 ## `Street Name` ## <chr> ## 1 Allison St ## 2 Birch St ## 3 Boyd St ## 4 Floyd Ave ## 5 Grove Ave ## 6 Hanover Ave ## 7 Kensington Ave ## 8 Lombardy Pl ## 9 Madumbie Lane ## 10 Monument Ave ## # … with 29 more rows ``` --- ```r fanRoads %>% filter( st_intersects( fanRoads, target, sparse = FALSE ) == TRUE ) %>% ggplot() + geom_sf( data=target ) + geom_sf( color="darkgreen" ) ``` <img src="slides_files/figure-html/unnamed-chunk-48-1.png" width="504" style="display: block; margin: auto;" /> --- class: middle background-image: url("images/contour.png") background-position: right background-size: auto .center[ # Questions?  ] <p>&nbsp;</p> .bottom[ If you have any questions for about the content presented herein, please feel free to [submit them to me](mailto://rjdyer@vcu.edu) and I'll get back to you as soon as possible.]