class: center, middle, inverse, title-slide .title[ # Concepts: Vector Data ] --- <style type="text/css"> .remark-code{font-size: 100%} </style> # Learning Objectives - All spatial softwares (open and closed) are based on a core set of community based sofware - Vector geometry is defined by coordinate pairs in a spatial reference systems - Geometry can be associated with observations/rows in a `data.frame` allowing us to take advantage of the `data.frame` world - Geometries have a whole host of other capabilites including `measures` and `predicats` --- # Spatial Data The Spatial community is built on a few organizing communities OSgeo and OGC _and_ a few core libraries that drive EVERY spatial software you use! These include: - PROJ --> projections - GEOS --> Geometery operations - GDAL --> geodata abstraction and processing --- # Vector Data Model: - Vector features can be decomposed into three different `geometric` primitives: 1. `points` 2. `polylines` 3. `polygons` -- - Primitives can be thought of as the "building blocks" for all vector features -- - All primitives can be decomposed into set(s) of numeric X-Y coordinates with a known grid (reference system) -- - These reference systems are known as `PRØJ`ections, reference systems, ect --- # Points .pull-left[ - A point is composed of **one** coordinate pair (XY) in a specific coordinate system. - POITNS can have a Z or M diminsion as well but are out of scope here - A `POINT` has no length, no area anda dimension of 0. ```r # POINT defined as numeric vector (st_dimension(st_point(c(0,1)))) ``` ``` [1] 0 ``` ] .pull-right[ ```r ggplot() + geom_point(aes(x = c(1,2,3), y = c(3,1,2))) + labs(x = "X", y = "Y") ``` <img src="session3_files/figure-html/unnamed-chunk-3-1.png" width="432" style="display: block; margin: auto;" /> ] --- # Polyline .pull-left[ - A polyline is composed of a **ordered** sequence of two or more `POINT`s - Points in a line are called vertices/nodes and explicitly define the connection between two points. - A **LINESTRING** has a length, has no area and has a dimension of 1 (length) ```r # LINESTRING defined by matrix (st_dimension(st_linestring(matrix(1:4, nrow = 2)))) ``` ``` [1] 1 ``` ] .pull-right[ ```r ggplot() + geom_line(aes(x = c(1,2,3), y = c(3,1,2))) + geom_point(aes(x = c(1,2,3), y = c(3,1,2)), col = "red") + labs(x = "X", y = "Y") ``` <img src="session3_files/figure-html/unnamed-chunk-5-1.png" width="432" style="display: block; margin: auto;" /> ] --- # Polygon .pull-left[ - A `POLYGON` is composed of 4 or more points whose starting and ending point are the same. - A **POLYGON** is surface stored as a list of its exterior and interior rings. - **POLYGON** have length, area, and a dimension of 2. (area) ```r # POLYGON defined by LIST (interior and exterior rings) (st_dimension(st_polygon(list(matrix(c(1:4, 1,2), nrow = 3, byrow = TRUE))))) ``` ``` [1] 2 ``` ] .pull-right[ ```r ggplot() + geom_polygon(aes(x = c(1,2,3,1), y = c(3,1,2,3)), fill = "green", alpha = .5) + geom_line(aes(x = c(1,2,3), y = c(3,1,2))) + geom_point(aes(x = c(1,2,3), y = c(3,1,2)), col = "red") + labs(x = "X", y = "Y") ``` <img src="session3_files/figure-html/unnamed-chunk-7-1.png" width="432" style="display: block; margin: auto;" /> ] --- # Projections Place a numeric coordinate set in a relative/reference space: <img src="img/09-gcs-pcs.png" width="60%" style="display: block; margin: auto;" /> --- - Projections can have angular units (lat/lon) - geographic coordinate systems (GCS), They are made up of: -- - Ellipsoid (squished "ideal" sphere model) -- - geoid (mathamatical model of gravitatial feild) -- - datum the relationship between the ellipsoid and geoid -- - datums can be local (NAD27), or, -- - global (WGS84, NAD83) --- - SRS can also be projected (x-y axis, measurement units (m), false origin) -- - All PCS are based on GCS -- - Projections seek to place the 3D earth on 2D -- - Do this by defining a plane that can be conic, cylindrical or planar -- - "unfolding to this plane" creates distortion of **shape**, **area**, **distance**, or **direction** -- - The distortion is greater from point/lines of tangency or secanacy --- ```r o = data.frame(X = 0, Y = 0, name = "origin") |> st_as_sf(coords = c("X", "Y"), crs = 5070) o ``` ``` Simple feature collection with 1 feature and 1 field Geometry type: POINT Dimension: XY Bounding box: xmin: 0 ymin: 0 xmax: 0 ymax: 0 Projected CRS: NAD83 / Conus Albers name geometry 1 origin POINT (0 0) ``` <img src="session3_files/figure-html/unnamed-chunk-10-1.png" width="432" /> --- # Measure (GEOS Measures) - Measures are the questions we ask about the dimension of a geometry, once a reference has been established we can compute: -- - How long is a line or polygon perimeter (unit) -- - What is the area of a polygon (unit^2^) -- - How far are two object from one another (unit) -- - Measures come from the GEOS library -- - Measures are in the **units** of the projection --- # WKT: Well-Known-Text Well-known text is a text markup language for representing vector geometry objects. A binary equivalent, known as well-known binary, is used to transfer and store the same information in a more compact form convenient for computer processing. ```r (p1 = st_point(c(1,2))) (ls1 = st_linestring(matrix(1:10, , 2))) outer = matrix(c(0,0,10,0,10,10,0,10,0,0),ncol=2, byrow=TRUE) hole1 = matrix(c(1,1,1,2,2,2,2,1,1,1),ncol=2, byrow=TRUE) pts = list(outer, hole1) (pl1 = st_polygon(pts)) ``` --- # Simple Features Model Simple Features (officially Simple Feature Access) is both an OGC and International Organization for Standardization (ISO) standard that specifies a common storage and access model of (mostly) two-dimensional geometries. <img src="img/09-sf-model.png" width="75%" style="display: block; margin: auto;" /> --- # Example! ```r gages <- read.csv(system.file("extdata", "Gages_flowdata.csv", package = "awra2020spatial")) |> select(SOURCE_FEA, STATE, LAT_SITE, LON_SITE) ``` ``` Rows: 2,771 Columns: 4 $ SOURCE_FEA <int> 14361500, 14344500, 10378500, 14341500, 14343000, 13092500,… $ STATE <chr> "OR", "OR", "OR", "OR", "OR", "ID", "OR", "OR", "OR", "OR",… $ LAT_SITE <dbl> 42.43040, 42.42763, 42.42488, 42.40819, 42.40263, 42.39648,… $ LON_SITE <dbl> -123.3178, -122.6011, -119.9233, -122.6011, -122.5373, -114… ``` -- ```r gages <- gages %>% st_as_sf(coords = c("LON_SITE", "LAT_SITE"), crs = 4269) ``` ``` Rows: 2,771 Columns: 3 $ SOURCE_FEA <int> 14361500, 14344500, 10378500, 14341500, 14343000, 13092500,… $ STATE <chr> "OR", "OR", "OR", "OR", "OR", "ID", "OR", "OR", "OR", "OR",… $ geometry <POINT [°]> POINT (-123.3178 42.4304), POINT (-122.6011 42.42763)… ``` -- ```r st_distance(gages[1,], gages[2,]) ``` ``` Units: [m] [,1] [1,] 58822.95 ``` --- # data.frames and geometries Tables - In R and Python, the sf and geopandas libraries allow geometries to act as a attribute of data.frames by extending the capabilites of the data.frame object. - This allows any observation to be assocaited with attribures and geometry fot GIS applications! ``` Simple feature collection with 2771 features and 2 fields Geometry type: POINT Dimension: XY Bounding box: xmin: -124.6691 ymin: 41.42768 xmax: -110.4411 ymax: 49.00075 Geodetic CRS: NAD83 First 10 features: SOURCE_FEA STATE geometry 1 14361500 OR POINT (-123.3178 42.4304) 2 14344500 OR POINT (-122.6011 42.42763) 3 10378500 OR POINT (-119.9233 42.42488) 4 14341500 OR POINT (-122.6011 42.40819) 5 14343000 OR POINT (-122.5373 42.40263) 6 13092500 ID POINT (-114.3693 42.39648) 7 10378000 OR POINT (-120.0177 42.38322) 8 10376500 OR POINT (-119.9011 42.38322) 9 14378000 OR POINT (-123.8123 42.379) 10 14342500 OR POINT (-122.3567 42.37624) ``` -- - In shapefiles, these attributes are stored in a ".dbf" file, the `geometry` is stored in a ".shp" file, and they are related through a ".shx" file. --- # The simple feature R object - a spatial object is a `data.frame` class that extends core primitive types and structures. ```r gages[1,] ``` ``` Simple feature collection with 1 feature and 2 fields Geometry type: POINT Dimension: XY Bounding box: xmin: -123.3178 ymin: 42.4304 xmax: -123.3178 ymax: 42.4304 Geodetic CRS: NAD83 SOURCE_FEA STATE geometry 1 14361500 OR POINT (-123.3178 42.4304) ``` ```r class(gages) ``` ``` [1] "sf" "data.frame" ``` ```r typeof(gages) ``` ``` [1] "list" ``` --- # Drop the structure ```r (d = st_drop_geometry(gages) %>% head()) ``` ``` SOURCE_FEA STATE 1 14361500 OR 2 14344500 OR 3 10378500 OR 4 14341500 OR 5 14343000 OR 6 13092500 ID ``` ```r class(d) ``` ``` [1] "data.frame" ``` --- class:inverse, middle, center # R as a GIS --- ## Simple features (sf) package The `sf` package implements the Simple Features standard for R by allowing geometries to be associated with data.frame rows. -- - The `sf` package contains functions that bind - to `GDAL` for reading and writing data, - to `GEOS` for geometrical operations, and - to `PRØJ` for projection conversions and datum transformations ```r sf::sf_extSoftVersion() ``` ``` GEOS GDAL proj.4 GDAL_with_GEOS USE_PROJ_H "3.9.1" "3.4.0" "8.1.1" "true" "true" PROJ "8.1.1" ``` --- ## Simple features (sf) package - represents <span class="code">simple features</span> as records in a `data.frame` or `tibble` with a `geometry list-column` <img src="img/09-sf-depends.png" width="75%" style="display: block; margin: auto;" /> --- # Extending GDAL With respect to GDAL, some utilities are directly exposed by `sf` as can be seen in the available function signatures: ```r grep("gdal", ls("package:sf"), value = T) ``` ``` [1] "gdal_create" "gdal_crs" "gdal_extract" [4] "gdal_inv_geotransform" "gdal_metadata" "gdal_polygonize" [7] "gdal_rasterize" "gdal_read" "gdal_read_mdim" [10] "gdal_subdatasets" "gdal_utils" "gdal_write" [13] "gdal_write_mdim" ``` --- # Extending PRØJ ```r grep("proj_|crs", ls("package:sf"), value = T) ``` ``` [1] "gdal_crs" "NA_crs_" "sf_add_proj_units" [4] "sf_proj_info" "sf_proj_network" "sf_proj_pipelines" [7] "sf_proj_search_paths" "st_crs" "st_crs<-" [10] "st_set_crs" ``` --- # Extending GEOS/Simple Featrues Access ```r grep("st_", ls("package:sf"), value = T) ``` ``` [1] "st_agr" "st_agr<-" "st_area" [4] "st_as_binary" "st_as_grob" "st_as_s2" [7] "st_as_sf" "st_as_sfc" "st_as_text" [10] "st_axis_order" "st_bbox" "st_bind_cols" [13] "st_boundary" "st_buffer" "st_cast" [16] "st_centroid" "st_collection_extract" "st_combine" [19] "st_contains" "st_contains_properly" "st_convex_hull" [22] "st_coordinates" "st_covered_by" "st_covers" [25] "st_crop" "st_crosses" "st_crs" [28] "st_crs<-" "st_delete" "st_difference" [31] "st_dimension" "st_disjoint" "st_distance" [34] "st_drivers" "st_drop_geometry" "st_equals" [37] "st_equals_exact" "st_filter" "st_geometry" [40] "st_geometry_type" "st_geometry<-" "st_geometrycollection" [43] "st_graticule" "st_inscribed_circle" "st_interpolate_aw" [46] "st_intersection" "st_intersects" "st_is" [49] "st_is_empty" "st_is_longlat" "st_is_simple" [52] "st_is_valid" "st_is_within_distance" "st_jitter" [55] "st_join" "st_layers" "st_length" [58] "st_line_merge" "st_line_sample" "st_linestring" [61] "st_m_range" "st_make_grid" "st_make_valid" [64] "st_multilinestring" "st_multipoint" "st_multipolygon" [67] "st_nearest_feature" "st_nearest_points" "st_node" [70] "st_normalize" "st_overlaps" "st_point" [73] "st_point_on_surface" "st_polygon" "st_polygonize" [76] "st_precision" "st_precision<-" "st_read" [79] "st_read_db" "st_relate" "st_reverse" [82] "st_sample" "st_segmentize" "st_set_agr" [85] "st_set_crs" "st_set_geometry" "st_set_precision" [88] "st_sf" "st_sfc" "st_shift_longitude" [91] "st_simplify" "st_snap" "st_sym_difference" [94] "st_touches" "st_transform" "st_triangulate" [97] "st_union" "st_viewport" "st_voronoi" [100] "st_within" "st_wrap_dateline" "st_write" [103] "st_write_db" "st_z_range" "st_zm" ``` --- # Spatial filters and Joins - `data.frame` filters and joins were based on boolean conditions and relational keys - spatial filters are also based on boolean conditions - however, spatial joins are ALSO based on boolean conditions - Boolean condions for geometries are defined by spatial realtions! - Spatial relations dictated are dictated by the Interiors, Boundaries and Exteriors --- ## Interior, Boundary and Exterior <img src="session3_files/figure-html/unnamed-chunk-26-1.png" width="720" style="display: block; margin: auto;" /> --- ## DE-9IM - Dimensionally Extended 9-Intersection Model (DE-9IM) - The DE-9IM is a **topological** model and (standard) used to describe the spatial relations of two geometries -- - Used in geometry, point-set topology, geospatial topology -- - The **DE-9IM** _matrix_ provides a way to classify geometry relations using the set **{0,1,2,F}** or **{T,F}** - Here {0,1,2,F} describe the diminsion of the intersection(POINT, LINE, AREA, NONE), and {T, F} is any. --- The **DE-9IM** matrix is based on a 3x3 intersection matrix testing the following relations: - II, IE, IB - BI, BE, BB, - EI, EE, EB --- <img src="img/de9im3.jpg" width="50%" style="display: block; margin: auto;" /> --- # Spatial Predicates - With a **{T,F}** matrix domain across the I,B, E space, there are 512 possible relations that can be grouped into binary classification schemes. -- - About 10 of these, have been given a common name such as _intersects_, _touches_, and _within_. -- These are called spatial predicates and return a boolean value for each interacting geometry. -- Can be serialize as a "DE-9IM string code" representing the matrix in a single string element --- <img src="img/geos_predicates.png" width="50%" style="display: block; margin: auto;" /> --- # Spatial Joins and fitlters So... In a `data.frame`, a row is kept if the supplied boolen codition is true. Simularly `sf` provides st_filter(x,y) The critical arguement this filter is the .predicate. The default is `st_intersects` --- # Example Dataset - Geometry X is a 3 feature polygon colored in red - Cemetery Y is a 4 feature polygon colored in blue <img src="session3_files/figure-html/unnamed-chunk-29-1.png" width="432" /> --- ## Binary logical operations Returns either a *sparse* matrix ```r st_intersects(x,y) ``` ``` Sparse geometry binary predicate list of length 3, where the predicate was `intersects' 1: 1, 3 2: 2, 3 3: 3 ``` or a *dense* matrix ```r st_intersects(x, y, sparse = FALSE) ``` ``` [,1] [,2] [,3] [,4] [1,] TRUE FALSE TRUE FALSE [2,] FALSE TRUE TRUE FALSE [3,] FALSE FALSE TRUE FALSE ``` --- # Boolean, Spatial Operators ```r st_filter(x,y, .predicate = st_intersects) ``` ``` Simple feature collection with 3 features and 1 field Geometry type: POLYGON Dimension: XY Bounding box: xmin: -1.2 ymin: -1 xmax: 3 ymax: 3 CRS: NA x ID 1 POLYGON ((-1 -1, 1 -1, 1 1,... 1 2 POLYGON ((1 1, 3 1, 3 3, 1 ... 2 3 POLYGON ((-1.2 1, 0.8 1, 0.... 3 ``` ```r sf::st_filter(x,y, .predicate = st_contains) ``` ``` Simple feature collection with 1 feature and 1 field Geometry type: POLYGON Dimension: XY Bounding box: xmin: -1 ymin: -1 xmax: 1 ymax: 1 CRS: NA x ID 1 POLYGON ((-1 -1, 1 -1, 1 1,... 1 ``` ```r sf::st_join(x, y, .predicate = st_intersects) ``` ``` Simple feature collection with 5 features and 2 fields Geometry type: POLYGON Dimension: XY Bounding box: xmin: -1.2 ymin: -1 xmax: 3 ymax: 3 CRS: NA ID.x ID.y x 1 1 A POLYGON ((-1 -1, 1 -1, 1 1,... 1.1 1 C POLYGON ((-1 -1, 1 -1, 1 1,... 2 2 B POLYGON ((1 1, 3 1, 3 3, 1 ... 2.1 2 C POLYGON ((1 1, 3 1, 3 3, 1 ... 3 3 C POLYGON ((-1.2 1, 0.8 1, 0.... ``` ```r sf::st_join(y, x, .predicate = st_intersects) ``` ``` Simple feature collection with 6 features and 2 fields Geometry type: POLYGON Dimension: XY Bounding box: xmin: -0.8 ymin: -1 xmax: 2.5 ymax: 1.8 CRS: NA ID.x ID.y x 1 A 1 POLYGON ((-0.8 -0.8, 0.8 -0... 2 B 2 POLYGON ((1.6 0.3, 2.4 0.3,... 3 C 1 POLYGON ((0.2 0.2, 1.8 0.2,... 3.1 C 2 POLYGON ((0.2 0.2, 1.8 0.2,... 3.2 C 3 POLYGON ((0.2 0.2, 1.8 0.2,... 4 D NA POLYGON ((1.5 -1, 2.5 -1, 2... ``` --- # Wrap it all up! - You have been tasked to identify the population living along the Mississippi River systems - You are interested in the county level since most flood control measures and flood response efforts are enforced by county EMAs - Federal funding however is administered at the state level so you need population counts aggregated to state... --- # River Data! ```r # Global River Shapefile filtered to the Mississippi System (miss = '/vsizip/{/vsicurl/https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/physical/ne_50m_rivers_lake_centerlines.zip}/ne_50m_rivers_lake_centerlines.shp' |> read_sf() |> filter(name == "Mississippi", featurecla == "River") |> select(name)) ``` ``` Simple feature collection with 1 feature and 1 field Geometry type: MULTILINESTRING Dimension: XY Bounding box: xmin: -95.02582 ymin: 28.98133 xmax: -89.01575 ymax: 47.48206 Geodetic CRS: WGS 84 # A tibble: 1 × 2 name geometry <chr> <MULTILINESTRING [°]> 1 Mississippi ((-89.37614 28.98133, -89.25713 29.15491, -89.01575 29.20287), (-… ``` --- # Population Data! ```r (popdata = read.csv('https://www2.census.gov/programs-surveys/popest/datasets/2020-2021/counties/totals/co-est2021-alldata.csv') |> filter(STNAME != CTYNAME) |> mutate(fip_code = paste0(sprintf("%02s", STATE), sprintf("%03s", COUNTY)) ) |> select(fip_code, POPESTIMATE2021)) ``` ``` fip_code POPESTIMATE2021 1 01001 59095 2 01003 239294 3 01005 24964 4 01007 22477 5 01009 59041 6 01011 10320 7 01013 18884 8 01015 115972 9 01017 34541 10 01019 24996 11 01021 45274 12 01023 12533 13 01025 22760 14 01027 14190 15 01029 15103 16 01031 54174 17 01033 57474 18 01035 11328 19 01037 10450 20 01039 37524 21 01041 13083 22 01043 89496 23 01045 49342 24 01047 37619 25 01049 71813 26 01051 89304 27 01053 36699 28 01055 103162 29 01057 16148 30 01059 32013 31 01061 26701 32 01063 7629 33 01065 14754 34 01067 17459 35 01069 107458 36 01071 52773 37 01073 667820 38 01075 13689 39 01077 94043 40 01079 33090 41 01081 177218 42 01083 107517 43 01085 9965 44 01087 18895 45 01089 395211 46 01091 18996 47 01093 29246 48 01095 98228 49 01097 413073 50 01099 19648 51 01101 227434 52 01103 123668 53 01105 8355 54 01107 18801 55 01109 32991 56 01111 21989 57 01113 58722 58 01115 92748 59 01117 226902 60 01119 12164 61 01121 81524 62 01123 41023 63 01125 227007 64 01127 64818 65 01129 15147 66 01131 10446 67 01133 23652 68 02013 3398 69 02016 5059 70 02020 288121 71 02050 18557 72 02060 838 73 02063 6941 74 02066 2630 75 02068 1593 76 02070 4772 77 02090 95593 78 02100 2071 79 02105 2332 80 02110 31973 81 02122 59767 82 02130 13754 83 02150 12787 84 02158 8360 85 02164 1416 86 02170 110686 87 02180 9865 88 02185 10972 89 02188 7560 90 02195 3356 91 02198 5729 92 02220 8407 93 02230 1132 94 02240 6970 95 02275 2055 96 02282 704 97 02290 5275 98 04001 65623 99 04003 126050 100 04005 145052 101 04007 53589 102 04009 39050 103 04011 9404 104 04012 16408 105 04013 4496588 106 04015 217692 107 04017 108147 108 04019 1052030 109 04021 449557 110 04023 47883 111 04025 242253 112 04027 206990 113 05001 16722 114 05003 18674 115 05005 42144 116 05007 293692 117 05009 37830 118 05011 10408 119 05013 4741 120 05015 28435 121 05017 10019 122 05019 21321 123 05021 14350 124 05023 25015 125 05025 7514 126 05027 22672 127 05029 20873 128 05031 112218 129 05033 60378 130 05035 47525 131 05037 16681 132 05039 6308 133 05041 11090 134 05043 17110 135 05045 125106 136 05047 17173 137 05049 12145 138 05051 100330 139 05053 18090 140 05055 46317 141 05057 19694 142 05059 33148 143 05061 12698 144 05063 37723 145 05065 13911 146 05067 16811 147 05069 65861 148 05071 25845 149 05073 6163 150 05075 16292 151 05077 8619 152 05079 13037 153 05081 11944 154 05083 21215 155 05085 74722 156 05087 16960 157 05089 16978 158 05091 42649 159 05093 39661 160 05095 6683 161 05097 8611 162 05099 8187 163 05101 7204 164 05103 22306 165 05105 9964 166 05107 15906 167 05109 10066 168 05111 22660 169 05113 19353 170 05115 63789 171 05117 8135 172 05119 397821 173 05121 18865 174 05123 22739 175 05125 125233 176 05127 9822 177 05129 7880 178 05131 128400 179 05133 15783 180 05135 17622 181 05137 12481 182 05139 38340 183 05141 15694 184 05143 250057 185 05145 77207 186 05147 6116 187 05149 20155 188 06001 1648556 189 06003 1235 190 06005 41259 191 06007 208309 192 06009 46221 193 06011 21917 194 06013 1161413 195 06015 28100 196 06017 193221 197 06019 1013581 198 06021 28805 199 06023 136310 200 06025 179851 201 06027 18970 202 06029 917673 203 06031 153443 204 06033 68766 205 06035 33159 206 06037 9829544 207 06039 159410 208 06041 260206 209 06043 17147 210 06045 91305 211 06047 286461 212 06049 8661 213 06051 13247 214 06053 437325 215 06055 136207 216 06057 103487 217 06059 3167809 218 06061 412300 219 06063 19915 220 06065 2458395 221 06067 1588921 222 06069 66677 223 06071 2194710 224 06073 3286069 225 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2746 48445 11754 2747 48447 1495 2748 48449 31183 2749 48451 119411 2750 48453 1305154 2751 48455 13827 2752 48457 20077 2753 48459 41774 2754 48461 3265 2755 48463 24729 2756 48465 47564 2757 48467 61275 2758 48469 90964 2759 48471 77977 2760 48473 59781 2761 48475 11194 2762 48477 35891 2763 48479 267945 2764 48481 41721 2765 48483 4927 2766 48485 130069 2767 48487 12731 2768 48489 20316 2769 48491 643026 2770 48493 51257 2771 48495 7415 2772 48497 71714 2773 48499 45875 2774 48501 7607 2775 48503 17977 2776 48505 13908 2777 48507 9534 2778 49001 7249 2779 49003 59688 2780 49005 137417 2781 49007 20372 2782 49009 976 2783 49011 367285 2784 49013 19790 2785 49015 9967 2786 49017 5129 2787 49019 9663 2788 49021 60519 2789 49023 12155 2790 49025 7992 2791 49027 13164 2792 49029 12657 2793 49031 1487 2794 49033 2597 2795 49035 1186421 2796 49037 14489 2797 49039 29106 2798 49041 21906 2799 49043 43093 2800 49045 76640 2801 49047 36204 2802 49049 684986 2803 49051 36173 2804 49053 191226 2805 49055 2558 2806 49057 267066 2807 50001 37260 2808 50003 37312 2809 50005 30403 2810 50007 168865 2811 50009 5925 2812 50011 50325 2813 50013 7421 2814 50015 26126 2815 50017 29541 2816 50019 27546 2817 50021 60591 2818 50023 59969 2819 50025 46090 2820 50027 58196 2821 51001 33246 2822 51003 113535 2823 51005 14986 2824 51007 13268 2825 51009 31273 2826 51011 16353 2827 51013 232965 2828 51015 77563 2829 51017 4114 2830 51019 80131 2831 51021 6173 2832 51023 33866 2833 51025 15940 2834 51027 19816 2835 51029 16947 2836 51031 55492 2837 51033 31332 2838 51035 29048 2839 51036 6594 2840 51037 11448 2841 51041 370688 2842 51043 14881 2843 51045 4865 2844 51047 53596 2845 51049 9681 2846 51051 13787 2847 51053 27912 2848 51057 10573 2849 51059 1139720 2850 51061 73815 2851 51063 15566 2852 51065 27723 2853 51067 54938 2854 51069 93717 2855 51071 16562 2856 51073 39069 2857 51075 25488 2858 51077 15359 2859 51079 20968 2860 51081 11437 2861 51083 33738 2862 51085 111603 2863 51087 333554 2864 51089 50248 2865 51091 2226 2866 51093 39278 2867 51095 79882 2868 51097 6662 2869 51099 27489 2870 51101 18171 2871 51103 10928 2872 51105 21983 2873 51107 427592 2874 51109 38848 2875 51111 11926 2876 51113 13942 2877 51115 8546 2878 51117 30248 2879 51119 10781 2880 51121 98473 2881 51125 14790 2882 51127 23897 2883 51131 12085 2884 51133 12029 2885 51135 15594 2886 51137 37188 2887 51139 23807 2888 51141 17602 2889 51143 59972 2890 51145 31136 2891 51147 21932 2892 51149 42880 2893 51153 484472 2894 51155 33759 2895 51157 7407 2896 51159 9017 2897 51161 96589 2898 51163 22641 2899 51165 84394 2900 51167 25550 2901 51169 21419 2902 51171 44752 2903 51173 29477 2904 51175 18006 2905 51177 143676 2906 51179 160877 2907 51181 6530 2908 51183 10763 2909 51185 39925 2910 51187 40925 2911 51191 53635 2912 51193 18731 2913 51195 35647 2914 51197 28178 2915 51199 70915 2916 51510 154706 2917 51520 17054 2918 51530 6601 2919 51540 45672 2920 51550 251269 2921 51570 18273 2922 51580 5717 2923 51590 42215 2924 51595 5667 2925 51600 24276 2926 51610 14493 2927 51620 8217 2928 51630 28367 2929 51640 6660 2930 51650 137746 2931 51660 51430 2932 51670 23140 2933 51678 7456 2934 51680 79009 2935 51683 42708 2936 51685 17002 2937 51690 13517 2938 51700 184587 2939 51710 235089 2940 51720 3666 2941 51730 33429 2942 51735 12574 2943 51740 97840 2944 51750 16499 2945 51760 226604 2946 51770 98865 2947 51775 25373 2948 51790 25661 2949 51800 96194 2950 51810 457672 2951 51820 22550 2952 51830 15590 2953 51840 28136 2954 53001 20621 2955 53003 22397 2956 53005 210025 2957 53007 79646 2958 53009 78209 2959 53011 511404 2960 53013 4042 2961 53015 111524 2962 53017 43696 2963 53019 7273 2964 53021 98268 2965 53023 2346 2966 53025 100297 2967 53027 76841 2968 53029 87432 2969 53031 33605 2970 53033 2252305 2971 53035 274314 2972 53037 45499 2973 53039 23118 2974 53041 84398 2975 53043 11232 2976 53045 67615 2977 53047 42634 2978 53049 23948 2979 53051 13886 2980 53053 925708 2981 53055 18557 2982 53057 130696 2983 53059 12170 2984 53061 833540 2985 53063 546040 2986 53065 47426 2987 53067 297977 2988 53069 4582 2989 53071 62682 2990 53073 228831 2991 53075 47873 2992 53077 256035 2993 54001 15468 2994 54003 126069 2995 54005 21312 2996 54007 12247 2997 54009 22140 2998 54011 93418 2999 54013 6176 3000 54015 7892 3001 54017 7735 3002 54019 39927 3003 54021 7377 3004 54023 10983 3005 54025 32608 3006 54027 23302 3007 54029 28656 3008 54031 14160 3009 54033 65158 3010 54035 27738 3011 54037 58370 3012 54039 177952 3013 54041 16892 3014 54043 20126 3015 54045 31909 3016 54047 18363 3017 54049 56001 3018 54051 30115 3019 54053 25157 3020 54055 59097 3021 54057 26857 3022 54059 23005 3023 54061 106387 3024 54063 12332 3025 54065 17221 3026 54067 24300 3027 54069 41776 3028 54071 6142 3029 54073 7601 3030 54075 7841 3031 54077 34358 3032 54079 57260 3033 54081 73771 3034 54083 27806 3035 54085 8383 3036 54087 13898 3037 54089 11908 3038 54091 16492 3039 54093 6672 3040 54095 8155 3041 54097 23791 3042 54099 38498 3043 54101 8249 3044 54103 14170 3045 54105 5063 3046 54107 83624 3047 54109 21051 3048 55001 20875 3049 55003 16107 3050 55005 46719 3051 55007 16320 3052 55009 269591 3053 55011 13302 3054 55013 16744 3055 55015 52539 3056 55017 66865 3057 55019 34746 3058 55021 58488 3059 55023 16075 3060 55025 563951 3061 55027 89313 3062 55029 30369 3063 55031 44203 3064 55033 45547 3065 55035 106452 3066 55037 4593 3067 55039 104362 3068 55041 9258 3069 55043 52110 3070 55045 36988 3071 55047 19229 3072 55049 23756 3073 55051 6178 3074 55053 21121 3075 55055 84943 3076 55057 26802 3077 55059 168732 3078 55061 20543 3079 55063 120433 3080 55065 16784 3081 55067 19502 3082 55069 28541 3083 55071 81505 3084 55073 137648 3085 55075 41875 3086 55077 15792 3087 55078 4289 3088 55079 928059 3089 55081 46193 3090 55083 39356 3091 55085 38259 3092 55087 191545 3093 55089 92497 3094 55091 7364 3095 55093 42587 3096 55095 45431 3097 55097 70468 3098 55099 14050 3099 55101 196896 3100 55103 17212 3101 55105 164381 3102 55107 14123 3103 55109 95044 3104 55111 65697 3105 55113 18295 3106 55115 40859 3107 55117 117747 3108 55119 19923 3109 55121 30724 3110 55123 30915 3111 55125 23520 3112 55127 106799 3113 55129 16752 3114 55131 137175 3115 55133 408756 3116 55135 51570 3117 55137 24828 3118 55139 171623 3119 55141 74070 3120 56001 37608 3121 56003 11632 3122 56005 46401 3123 56007 14649 3124 56009 13672 3125 56011 7315 3126 56013 39336 3127 56015 12537 3128 56017 4597 3129 56019 8623 3130 56021 100863 3131 56023 20153 3132 56025 79555 3133 56027 2438 3134 56029 30108 3135 56031 8699 3136 56033 31646 3137 56035 8697 3138 56037 41614 3139 56039 23575 3140 56041 20635 3141 56043 7705 3142 56045 6745 ``` --- # County Data! ```r (us_counties = AOI::aoi_get(state = "conus", county = "all") |> left_join(popdata, by = "fip_code")) ``` ``` Simple feature collection with 3108 features and 15 fields Geometry type: MULTIPOLYGON Dimension: XY Bounding box: xmin: -124.8485 ymin: 24.39631 xmax: -66.88544 ymax: 49.38448 Geodetic CRS: WGS 84 First 10 features: state_region state_division feature_code state_name state_abbr name 1 3 6 0161526 Alabama AL Autauga 2 3 6 0161527 Alabama AL Baldwin 3 3 6 0161528 Alabama AL Barbour 4 3 6 0161529 Alabama AL Bibb 5 3 6 0161530 Alabama AL Blount 6 3 6 0161531 Alabama AL Bullock 7 3 6 0161532 Alabama AL Butler 8 3 6 0161533 Alabama AL Calhoun 9 3 6 0161534 Alabama AL Chambers 10 3 6 0161535 Alabama AL Cherokee fip_class tiger_class combined_area_code metropolitan_area_code 1 H1 G4020 388 <NA> 2 H1 G4020 380 <NA> 3 H1 G4020 NA <NA> 4 H1 G4020 142 <NA> 5 H1 G4020 142 <NA> 6 H1 G4020 NA <NA> 7 H1 G4020 NA <NA> 8 H1 G4020 NA <NA> 9 H1 G4020 122 <NA> 10 H1 G4020 NA <NA> functional_status land_area water_area fip_code POPESTIMATE2021 1 A 1539634184 25674812 01001 59095 2 A 4117656514 1132955729 01003 239294 3 A 2292160149 50523213 01005 24964 4 A 1612188717 9572303 01007 22477 5 A 1670259090 14860281 01009 59041 6 A 1613083467 6030667 01011 10320 7 A 2012002546 2701199 01013 18884 8 A 1569246126 16536293 01015 115972 9 A 1545085601 16971700 01017 34541 10 A 1433620850 120310807 01019 24996 geometry 1 MULTIPOLYGON (((-86.81491 3... 2 MULTIPOLYGON (((-87.59883 3... 3 MULTIPOLYGON (((-85.41644 3... 4 MULTIPOLYGON (((-87.01916 3... 5 MULTIPOLYGON (((-86.5778 33... 6 MULTIPOLYGON (((-85.65767 3... 7 MULTIPOLYGON (((-86.4482 31... 8 MULTIPOLYGON (((-85.79605 3... 9 MULTIPOLYGON (((-85.59315 3... 10 MULTIPOLYGON (((-85.51361 3... ``` --- # County Data! ```r # Which counties intersect this system misscount = st_filter(us_counties, miss, .predicate = st_intersects) # Find all counties in the intersecting states counties = filter(us_counties, state_name %in% c(misscount$state_name) ) ``` --- count: false ##Intersecting Counties .panel1-q11-auto[ ```r *ggplot() ``` ] .panel2-q11-auto[ <img src="session3_files/figure-html/q11_auto_01_output-1.png" width="432" /> ] --- count: false ##Intersecting Counties .panel1-q11-auto[ ```r ggplot() + * geom_sf(data = counties, lty = 3) ``` ] .panel2-q11-auto[ <img src="session3_files/figure-html/q11_auto_02_output-1.png" width="432" /> ] --- count: false ##Intersecting Counties .panel1-q11-auto[ ```r ggplot() + geom_sf(data = counties, lty = 3) + * geom_sf(data = misscount, fill = 'red', alpha = .5) ``` ] .panel2-q11-auto[ <img src="session3_files/figure-html/q11_auto_03_output-1.png" width="432" /> ] --- count: false ##Intersecting Counties .panel1-q11-auto[ ```r ggplot() + geom_sf(data = counties, lty = 3) + geom_sf(data = misscount, fill = 'red', alpha = .5) + * geom_sf(data = miss, col = "blue") ``` ] .panel2-q11-auto[ <img src="session3_files/figure-html/q11_auto_04_output-1.png" width="432" /> ] --- count: false ##Intersecting Counties .panel1-q11-auto[ ```r ggplot() + geom_sf(data = counties, lty = 3) + geom_sf(data = misscount, fill = 'red', alpha = .5) + geom_sf(data = miss, col = "blue") + * theme_linedraw() ``` ] .panel2-q11-auto[ <img src="session3_files/figure-html/q11_auto_05_output-1.png" width="432" /> ] <style> .panel1-q11-auto { color: black; width: 38.8%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-q11-auto { color: black; width: 58.2%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-q11-auto { color: black; width: 0%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false ##Intersecting Populations .panel1-q12-auto[ ```r *filter(misscount) ``` ] .panel2-q12-auto[ ``` Simple feature collection with 112 features and 15 fields Geometry type: MULTIPOLYGON Dimension: XY Bounding box: xmin: -95.60286 ymin: 28.85513 xmax: -88.81289 ymax: 48.54021 Geodetic CRS: WGS 84 First 10 features: state_region state_division feature_code state_name state_abbr name 1 3 7 0069160 Arkansas AR Chicot 2 3 7 0069162 Arkansas AR Crittenden 3 3 7 0066850 Arkansas AR Desha 4 3 7 0066866 Arkansas AR Lee 5 3 7 0069899 Arkansas AR Mississippi 6 3 7 0069172 Arkansas AR Phillips 7 2 3 0424202 Illinois IL Adams 8 2 3 0424203 Illinois IL Alexander 9 2 3 0424208 Illinois IL Calhoun 10 2 3 0424209 Illinois IL Carroll fip_class tiger_class combined_area_code metropolitan_area_code 1 H1 G4020 NA <NA> 2 H1 G4020 368 <NA> 3 H1 G4020 NA <NA> 4 H1 G4020 NA <NA> 5 H1 G4020 NA <NA> 6 H1 G4020 NA <NA> 7 H1 G4020 448 <NA> 8 H1 G4020 164 <NA> 9 H1 G4020 476 <NA> 10 H1 G4020 NA <NA> functional_status land_area water_area fip_code POPESTIMATE2021 1 A 1649764677 140413175 05017 10019 2 A 1587312625 81689126 05035 47525 3 A 1919735554 185180909 05041 11090 4 A 1553493435 57858191 05077 8619 5 A 2336409933 72224609 05093 39661 6 A 1788024160 95966884 05107 15906 7 A 2214826185 41754318 17001 64954 8 A 609667767 44639203 17003 5030 9 A 657415184 77051402 17013 4369 10 A 1153597401 55881822 17015 15698 geometry 1 MULTIPOLYGON (((-91.16615 3... 2 MULTIPOLYGON (((-90.30836 3... 3 MULTIPOLYGON (((-91.55766 3... 4 MULTIPOLYGON (((-90.40854 3... 5 MULTIPOLYGON (((-89.9097 35... 6 MULTIPOLYGON (((-90.58737 3... 7 MULTIPOLYGON (((-90.91197 4... 8 MULTIPOLYGON (((-89.17208 3... 9 MULTIPOLYGON (((-90.60357 3... 10 MULTIPOLYGON (((-90.15198 4... ``` ] --- count: false ##Intersecting Populations .panel1-q12-auto[ ```r filter(misscount) |> * st_drop_geometry() ``` ] .panel2-q12-auto[ ``` state_region state_division feature_code state_name state_abbr 1 3 7 0069160 Arkansas AR 2 3 7 0069162 Arkansas AR 3 3 7 0066850 Arkansas AR 4 3 7 0066866 Arkansas AR 5 3 7 0069899 Arkansas AR 6 3 7 0069172 Arkansas AR 7 2 3 0424202 Illinois IL 8 2 3 0424203 Illinois IL 9 2 3 0424208 Illinois IL 10 2 3 0424209 Illinois IL 11 2 3 0424235 Illinois IL 12 2 3 0424237 Illinois IL 13 2 3 0424240 Illinois IL 14 2 3 0424244 Illinois IL 15 2 3 0424258 Illinois IL 16 2 3 1784750 Illinois IL 17 2 3 1784865 Illinois IL 18 2 3 1784941 Illinois IL 19 2 3 1784967 Illinois IL 20 2 3 0424282 Illinois IL 21 2 3 1785113 Illinois IL 22 2 4 0465192 Iowa IA 23 2 4 0465210 Iowa IA 24 2 4 0465211 Iowa IA 25 2 4 0465217 Iowa IA 26 2 4 0465219 Iowa IA 27 2 4 0465237 Iowa IA 28 2 4 0465244 Iowa IA 29 2 4 0465246 Iowa IA 30 2 4 0465258 Iowa IA 31 2 4 0465270 Iowa IA 32 3 6 0516866 Kentucky KY 33 3 6 0516884 Kentucky KY 34 3 6 0516899 Kentucky KY 35 3 7 0558403 Louisiana LA 36 3 7 0558515 Louisiana LA 37 3 7 0558530 Louisiana LA 38 3 7 0558534 Louisiana LA 39 3 7 0558542 Louisiana LA 40 3 7 0558582 Louisiana LA 41 3 7 0558027 Louisiana LA 42 3 7 0558088 Louisiana LA 43 3 7 0558113 Louisiana LA 44 3 7 0558125 Louisiana LA 45 3 7 0559449 Louisiana LA 46 3 7 1629449 Louisiana LA 47 3 7 1629464 Louisiana LA 48 3 7 1629472 Louisiana LA 49 3 7 0559509 Louisiana LA 50 3 7 0559570 Louisiana LA 51 3 7 0559584 Louisiana LA 52 2 4 0663198 Minnesota MN 53 2 4 0659447 Minnesota MN 54 2 4 0659449 Minnesota MN 55 2 4 0659450 Minnesota MN 56 2 4 0659456 Minnesota MN 57 2 4 0659463 Minnesota MN 58 2 4 0659464 Minnesota MN 59 2 4 0659470 Minnesota MN 60 2 4 0659472 Minnesota MN 61 2 4 0659473 Minnesota MN 62 2 4 0659474 Minnesota MN 63 2 4 0659476 Minnesota MN 64 2 4 0659494 Minnesota MN 65 2 4 0659507 Minnesota MN 66 2 4 0659515 Minnesota MN 67 2 4 0659517 Minnesota MN 68 2 4 0659523 Minnesota MN 69 2 4 0659526 Minnesota MN 70 2 4 0659529 Minnesota MN 71 2 4 0659530 Minnesota MN 72 3 6 0695726 Mississippi MS 73 3 6 0695730 Mississippi MS 74 3 6 0695735 Mississippi MS 75 3 6 0695738 Mississippi MS 76 3 6 0695741 Mississippi MS 77 3 6 0695751 Mississippi MS 78 3 6 0695755 Mississippi MS 79 3 6 0695792 Mississippi MS 80 3 6 0695795 Mississippi MS 81 3 6 0695796 Mississippi MS 82 3 6 0695799 Mississippi MS 83 2 4 0758470 Missouri MO 84 2 4 0758477 Missouri MO 85 2 4 0758504 Missouri MO 86 2 4 0758510 Missouri MO 87 2 4 0758511 Missouri MO 88 2 4 0758517 Missouri MO 89 2 4 0758521 Missouri MO 90 2 4 0758526 Missouri MO 91 2 4 0758532 Missouri MO 92 2 4 0758533 Missouri MO 93 2 4 0758536 Missouri MO 94 2 4 0758541 Missouri MO 95 2 4 0758546 Missouri MO 96 2 4 0765806 Missouri MO 97 2 4 0758549 Missouri MO 98 2 4 0758553 Missouri MO 99 2 4 0767557 Missouri MO 100 3 6 1639741 Tennessee TN 101 3 6 1639762 Tennessee TN 102 3 6 1639763 Tennessee TN 103 3 6 1639790 Tennessee TN 104 3 6 1639795 Tennessee TN 105 2 3 1581065 Wisconsin WI 106 2 3 1581071 Wisconsin WI 107 2 3 1581081 Wisconsin WI 108 2 3 1581091 Wisconsin WI 109 2 3 1581106 Wisconsin WI 110 2 3 1581107 Wisconsin WI 111 2 3 1581120 Wisconsin WI 112 2 3 1581121 Wisconsin WI name fip_class tiger_class combined_area_code 1 Chicot H1 G4020 NA 2 Crittenden H1 G4020 368 3 Desha H1 G4020 NA 4 Lee H1 G4020 NA 5 Mississippi H1 G4020 NA 6 Phillips H1 G4020 NA 7 Adams H1 G4020 448 8 Alexander H1 G4020 164 9 Calhoun H1 G4020 476 10 Carroll H1 G4020 NA 11 Hancock H1 G4020 161 12 Henderson H1 G4020 161 13 Jackson H1 G4020 NA 14 Jo Daviess H1 G4020 NA 15 Madison H1 G4020 476 16 Mercer H1 G4020 209 17 Monroe H1 G4020 476 18 Pike H1 G4020 NA 19 Randolph H1 G4020 NA 20 Rock Island H1 G4020 209 21 Union H1 G4020 NA 22 Allamakee H1 G4020 NA 23 Clayton H1 G4020 NA 24 Clinton H1 G4020 209 25 Des Moines H1 G4020 161 26 Dubuque H1 G4020 NA 27 Jackson H1 G4020 NA 28 Lee H1 G4020 161 29 Louisa H1 G4020 NA 30 Muscatine H1 G4020 209 31 Scott H1 G4020 209 32 Carlisle H1 G4020 NA 33 Fulton H1 G4020 NA 34 Hickman H1 G4020 NA 35 Ascension H1 G4020 NA 36 Concordia H1 G4020 NA 37 East Baton Rouge H6 G4020 NA 38 East Carroll H1 G4020 NA 39 East Feliciana H1 G4020 NA 40 Iberville H1 G4020 NA 41 Jefferson H1 G4020 406 42 Madison H1 G4020 NA 43 Orleans H6 G4020 406 44 Plaquemines H1 G4020 406 45 Pointe Coupee H1 G4020 NA 46 St. Charles H1 G4020 406 47 St. James H1 G4020 406 48 St. John the Baptist H1 G4020 406 49 Tensas H1 G4020 NA 50 West Baton Rouge H1 G4020 NA 51 West Feliciana H1 G4020 NA 52 Aitkin H1 G4020 NA 53 Anoka H1 G4020 378 54 Beltrami H1 G4020 NA 55 Benton H1 G4020 378 56 Cass H1 G4020 NA 57 Crow Wing H1 G4020 NA 58 Dakota H1 G4020 378 59 Goodhue H1 G4020 378 60 Hennepin H1 G4020 378 61 Houston H1 G4020 NA 62 Hubbard H1 G4020 NA 63 Itasca H1 G4020 NA 64 Morrison H1 G4020 NA 65 Ramsey H1 G4020 378 66 Sherburne H1 G4020 378 67 Stearns H1 G4020 378 68 Wabasha H1 G4020 462 69 Washington H1 G4020 378 70 Winona H1 G4020 NA 71 Wright H1 G4020 378 72 Adams H1 G4020 NA 73 Bolivar H1 G4020 185 74 Claiborne H1 G4020 NA 75 Coahoma H1 G4020 NA 76 DeSoto H1 G4020 368 77 Issaquena H1 G4020 NA 78 Jefferson H1 G4020 NA 79 Tunica H1 G4020 368 80 Warren H1 G4020 298 81 Washington H1 G4020 NA 82 Wilkinson H1 G4020 NA 83 Cape Girardeau H1 G4020 164 84 Clark H1 G4020 161 85 Jefferson H1 G4020 476 86 Lewis H1 G4020 448 87 Lincoln H1 G4020 476 88 Marion H1 G4020 448 89 Mississippi H1 G4020 NA 90 New Madrid H1 G4020 NA 91 Pemiscot H1 G4020 NA 92 Perry H1 G4020 NA 93 Pike H1 G4020 NA 94 Ralls H1 G4020 448 95 St. Charles H1 G4020 476 96 Ste. Genevieve H1 G4020 NA 97 St. Louis H1 G4020 476 98 Scott H1 G4020 164 99 St. Louis C7 G4020 476 100 Dyer H1 G4020 NA 101 Lake H1 G4020 NA 102 Lauderdale H1 G4020 NA 103 Shelby H1 G4020 368 104 Tipton H1 G4020 368 105 Buffalo H1 G4020 NA 106 Crawford H1 G4020 NA 107 Grant H1 G4020 NA 108 La Crosse H1 G4020 NA 109 Pepin H1 G4020 NA 110 Pierce H1 G4020 378 111 Trempealeau H1 G4020 NA 112 Vernon H1 G4020 NA metropolitan_area_code functional_status land_area water_area fip_code 1 <NA> A 1649764677 140413175 05017 2 <NA> A 1587312625 81689126 05035 3 <NA> A 1919735554 185180909 05041 4 <NA> A 1553493435 57858191 05077 5 <NA> A 2336409933 72224609 05093 6 <NA> A 1788024160 95966884 05107 7 <NA> A 2214826185 41754318 17001 8 <NA> A 609667767 44639203 17003 9 <NA> A 657415184 77051402 17013 10 <NA> A 1153597401 55881822 17015 11 <NA> A 2055798695 53563360 17067 12 <NA> A 981068796 42565932 17071 13 <NA> A 1511439594 48312243 17077 14 <NA> A 1556385347 45789931 17085 15 <NA> A 1853175882 63522153 17119 16 <NA> A 1453604391 19411909 17131 17 <NA> A 997924046 33755286 17133 18 <NA> A 2153190724 45251530 17149 19 <NA> A 1490194098 56150220 17157 20 <NA> A 1107209785 61633874 17161 21 <NA> A 1070731267 22540420 17181 22 <NA> A 1655115641 51094064 19005 23 <NA> A 2016295985 36695624 19043 24 <NA> A 1799934695 39410955 19045 25 <NA> A 1077769233 35341938 19057 26 <NA> A 1575487976 21138781 19061 27 <NA> A 1647344929 35129054 19097 28 <NA> A 1340448559 55266767 19111 29 <NA> A 1040596801 40784321 19115 30 <NA> A 1132964480 30262337 19139 31 <NA> A 1186463531 26196368 19163 32 <NA> A 490647294 24707972 21039 33 <NA> A 533351953 64091345 21075 34 <NA> A 627487969 27882023 21105 35 <NA> A 751016878 32969822 22005 36 <NA> A 1805360346 130322878 22029 37 <NA> B 1179744891 38443657 22033 38 <NA> A 1090092484 56411688 22035 39 <NA> A 1174022934 6150603 22037 40 <NA> A 1602476038 88659446 22047 41 <NA> A 779455288 883412483 22051 42 <NA> A 1616604801 68460376 22065 43 <NA> C 439001414 467095943 22071 44 <NA> A 2020676390 4628435537 22075 45 <NA> A 1442300231 86716680 22077 46 <NA> A 719347893 308635080 22089 47 <NA> A 616057915 38773983 22093 48 <NA> A 555484721 506440074 22095 49 <NA> A 1561795836 99432220 22107 50 <NA> A 498174685 29604699 22121 51 <NA> A 1044584261 58751920 22125 52 <NA> A 4717323601 449370012 27001 53 <NA> A 1092881706 62440664 27003 54 <NA> A 6487154459 1426287777 27007 55 <NA> A 1057483361 12286984 27009 56 <NA> A 5235592066 1016598358 27021 57 <NA> A 2585732031 409718952 27035 58 <NA> A 1456981753 62989474 27037 59 <NA> A 1959853268 59175413 27049 60 <NA> A 1434949247 136626454 27053 61 <NA> A 1429966790 43313922 27055 62 <NA> A 2398345766 190324762 27057 63 <NA> A 6909414890 673890473 27061 64 <NA> A 2913980726 73275528 27097 65 <NA> A 394323902 46008725 27123 66 <NA> A 1121230367 46911208 27141 67 <NA> A 3477910045 122236877 27145 68 <NA> A 1354362715 68875473 27157 69 <NA> A 996415260 99954115 27163 70 <NA> A 1621791138 40051870 27169 71 <NA> A 1712394405 137711642 27171 72 <NA> A 1197464268 65273639 28001 73 <NA> A 2270141682 81446732 28011 74 <NA> A 1262400025 35711134 28021 75 <NA> A 1432526489 77525442 28027 76 <NA> A 1233730200 50234809 28033 77 <NA> A 1069614257 73664788 28055 78 <NA> A 1346620554 18988092 28063 79 <NA> A 1176191638 57012297 28143 80 <NA> A 1524286572 77759819 28149 81 <NA> A 1875077589 94257003 28151 82 <NA> A 1756287539 25018736 28157 83 <NA> A 1498351219 20284964 29031 84 <NA> A 1306924727 18695976 29045 85 <NA> A 1700186581 20302343 29099 86 <NA> A 1307988621 14965606 29111 87 <NA> A 1622789067 36002285 29113 88 <NA> A 1131623305 19113754 29127 89 <NA> A 1066029407 44515175 29133 90 <NA> A 1747940899 56236293 29143 91 <NA> A 1275841039 53915406 29155 92 <NA> A 1228574128 25394864 29157 93 <NA> A 1736428525 36976721 29163 94 <NA> A 1216743025 36381069 29173 95 <NA> A 1451692082 82663655 29183 96 <NA> A 1292800352 19739769 29186 97 <NA> A 1315390296 39612404 29189 98 <NA> A 1087808789 15175233 29201 99 <NA> F 159896485 11563208 29510 100 <NA> A 1326982067 35839863 47045 101 <NA> A 429381241 72799687 47095 102 <NA> A 1222203226 80251266 47097 103 <NA> A 1969826791 51308328 47157 104 <NA> A 1180026940 32703276 47167 105 <NA> A 1750290818 87549529 55011 106 <NA> A 1477970925 73976386 55023 107 <NA> A 2970402286 94521527 55043 108 <NA> A 1170175008 73150469 55063 109 <NA> A 600894491 43241432 55091 110 <NA> A 1486700521 48452323 55093 111 <NA> A 1898383310 23321910 55121 112 <NA> A 2050136139 64289089 55123 POPESTIMATE2021 1 10019 2 47525 3 11090 4 8619 5 39661 6 15906 7 64954 8 5030 9 4369 10 15698 11 17400 12 6312 13 52565 14 21939 15 264490 16 15582 17 34932 18 14618 19 30142 20 142909 21 16923 22 13926 23 16998 24 46463 25 38491 26 98718 27 19368 28 33215 29 10749 30 42688 31 174170 32 4791 33 6512 34 4424 35 128369 36 18376 37 453301 38 7220 39 19338 40 29824 41 433688 42 9799 43 376971 44 23303 45 20356 46 52282 47 19742 48 42094 49 4043 50 27792 51 15494 52 15887 53 367018 54 46380 55 41459 56 30639 57 67270 58 442038 59 47968 60 1267416 61 18778 62 21715 63 45070 64 33992 65 543257 66 99074 67 158947 68 21509 69 272256 70 49630 71 144845 72 28742 73 30308 74 8908 75 20810 76 188633 77 1280 78 7205 79 9696 80 43579 81 43687 82 8315 83 82113 84 6736 85 227771 86 10000 87 61586 88 28518 89 12538 90 16035 91 15236 92 18922 93 17761 94 10361 95 409981 96 18588 97 997187 98 37840 99 293310 100 36615 101 7128 102 25108 103 924454 104 61004 105 13302 106 16075 107 52110 108 120433 109 7364 110 42587 111 30724 112 30915 ``` ] --- count: false ##Intersecting Populations .panel1-q12-auto[ ```r filter(misscount) |> st_drop_geometry() |> * group_by(state_name) ``` ] .panel2-q12-auto[ ``` # A tibble: 112 × 15 # Groups: state_name [10] state_region state_division feature_code state_name state_abbr name <int> <int> <chr> <chr> <chr> <chr> 1 3 7 0069160 Arkansas AR Chicot 2 3 7 0069162 Arkansas AR Crittenden 3 3 7 0066850 Arkansas AR Desha 4 3 7 0066866 Arkansas AR Lee 5 3 7 0069899 Arkansas AR Mississippi 6 3 7 0069172 Arkansas AR Phillips 7 2 3 0424202 Illinois IL Adams 8 2 3 0424203 Illinois IL Alexander 9 2 3 0424208 Illinois IL Calhoun 10 2 3 0424209 Illinois IL Carroll # … with 102 more rows, and 9 more variables: fip_class <fct>, # tiger_class <fct>, combined_area_code <int>, metropolitan_area_code <chr>, # functional_status <fct>, land_area <dbl>, water_area <dbl>, fip_code <chr>, # POPESTIMATE2021 <int> ``` ] --- count: false ##Intersecting Populations .panel1-q12-auto[ ```r filter(misscount) |> st_drop_geometry() |> group_by(state_name) |> * summarize(pop = sum(POPESTIMATE2021)) ``` ] .panel2-q12-auto[ ``` # A tibble: 10 × 2 state_name pop <chr> <int> 1 Arkansas 132820 2 Illinois 707863 3 Iowa 494786 4 Kentucky 15727 5 Louisiana 1681992 6 Minnesota 3735148 7 Mississippi 391163 8 Missouri 2264483 9 Tennessee 1054309 10 Wisconsin 313510 ``` ] --- count: false ##Intersecting Populations .panel1-q12-auto[ ```r filter(misscount) |> st_drop_geometry() |> group_by(state_name) |> summarize(pop = sum(POPESTIMATE2021)) |> * arrange(-pop) ``` ] .panel2-q12-auto[ ``` # A tibble: 10 × 2 state_name pop <chr> <int> 1 Minnesota 3735148 2 Missouri 2264483 3 Louisiana 1681992 4 Tennessee 1054309 5 Illinois 707863 6 Iowa 494786 7 Mississippi 391163 8 Wisconsin 313510 9 Arkansas 132820 10 Kentucky 15727 ``` ] <style> .panel1-q12-auto { color: black; width: 38.8%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-q12-auto { color: black; width: 58.2%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-q12-auto { color: black; width: 0%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style>