library(tmap)
## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(sf)
## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(tidycensus)
census_api_key(Sys.getenv("census_api"))
## To install your API key for use in future sessions, run this function with `install = TRUE`.
crime.data <- read.csv("Crime_Data.csv")
crime.data %>% head()
## OBJECTID Report.Number Report.Date Offense.Start.Date
## 1 41377 220278026 10/26/2022 4:09:13 PM
## 2 52111 221050835 10/28/2022 5:38:34 AM 4/15/2022 3:00:00 AM
## 3 52683 221320696 10/28/2022 1:08:07 PM 5/12/2022 2:29:00 PM
## 4 56853 221671725 10/24/2022 7:00:18 PM 6/16/2022 2:54:00 AM
## 5 59006 221681279 10/27/2022 10:24:22 PM 6/17/2022 6:40:00 PM
## 6 59991 221921099 10/12/2022 2:18:45 AM 10/11/2022 4:00:00 AM
## Offense.End.Date Day.of.the.week Day.Number Zone Beat
## 1 Wednesday 4 7 703
## 2 4/15/2022 12:30:00 PM Thursday 5 3 305
## 3 10/28/2022 1:00:00 PM Thursday 5 4 405
## 4 6/17/2022 9:30:00 AM Wednesday 4 6 605
## 5 6/17/2022 8:00:00 PM Friday 6 6 605
## 6 10/11/2022 4:00:00 AM Tuesday 3 3 310
## Location Location.Type
## 1 6000 N TERMINAL PKWY Air/ Bus/ Train Terminal
## 2 323 DALTON ST SE Highway/ Road/ Alley/ Street/ Sidewalk
## 3 MLK DR NW / WESTLAND BLVD NW Highway/ Road/ Alley/ Street/ Sidewalk
## 4 301 MEMORIAL DR SE Residence/ Home / Apartment / Condo
## 5 441 CAMERON ST SE Residence/ Home / Apartment / Condo
## 6 2559 JONESBORO RD SE Residence/ Home / Apartment / Condo
## NIBRS.Code NIBRS.Code.Name Crime.Against
## 1 13C Intimidation PERSON
## 2 290 Destruction/Damage/Vandalism of Property PROPERTY
## 3 26C Impersonation PROPERTY
## 4 23F Theft From Motor Vehicle PROPERTY
## 5 290 Destruction/Damage/Vandalism of Property PROPERTY
## 6 23F Theft From Motor Vehicle PROPERTY
## Was.a.firearm.involved. Press.Release Social.Media Watch Longitude
## 1 N NA NA Morning Watch -84.44328
## 2 N NA NA Morning Watch -84.37761
## 3 N NA NA Day Watch -84.47294
## 4 N NA NA Evening Watch -84.37766
## 5 N NA NA Evening Watch -84.36529
## 6 N NA NA Morning Watch -84.36694
## Latitude Neighborhood NPU Council.District UCR.Grouping Victim.Count
## 1 33.64128 NA Part II 1
## 2 33.71991 Chosewood Park Y 1 Part II 1
## 3 33.75364 Harland Terrace I 10 Part II 1
## 4 33.74650 Grant Park W 1 Part I 1
## 5 33.74235 Grant Park W 1 Part II 1
## 6 33.68364 Browns Mill Park Z 1 Part I 1
## GlobalID x y
## 1 3af90926-db32-465a-b805-ace34bf3eb4a -84.44328 33.64128
## 2 4cd37ec7-503a-46d3-9e47-063680f7dd96 -84.37761 33.71991
## 3 857c5c30-2e15-4ab8-a173-493abece6b5b -84.47294 33.75364
## 4 8f196400-ee54-4729-a9e5-201caa9cb109 -84.37766 33.74650
## 5 87e5e48c-1f3e-45ab-be66-760846fc57ab -84.36529 33.74235
## 6 bd67738b-c8a4-4482-aeaa-e6dc1afe6af4 -84.36694 33.68364
unique(crime.data$NIBRS.Code.Name)
## [1] "Intimidation"
## [2] "Destruction/Damage/Vandalism of Property"
## [3] "Impersonation"
## [4] "Theft From Motor Vehicle"
## [5] "Simple Assault"
## [6] "Identity Theft"
## [7] "Burglary/Breaking & Entering"
## [8] "All Other Larceny"
## [9] "Motor Vehicle Theft"
## [10] "Drug/Narcotic Violations"
## [11] "Theft From Building"
## [12] "Robbery"
## [13] "Aggravated Assault"
## [14] "Rape"
## [15] "Theft of Motor Vehicle Parts or Accessories"
## [16] "False Pretenses/Swindle/Confidence Game"
## [17] "Drug Equipment Violations"
## [18] "Fondling"
## [19] "Murder & Nonnegligent Manslaughter"
## [20] "Weapon Law Violations"
## [21] "Credit Card/Automated Teller Machine Fraud"
## [22] "Shoplifting"
## [23] "Stolen Property Offenses"
## [24] "Wire Fraud"
## [25] "Pornography/Obscene Material"
## [26] "Counterfeiting/Forgery"
## [27] "Hacking/Computer Invasion"
## [28] "Arson"
## [29] "Sodomy"
## [30] "Embezzlement"
## [31] "Welfare Fraud"
## [32] "Theft From Coin-Operated Machine or Device"
## [33] "Extortion/Blackmail"
## [34] "Purse-snatching"
## [35] "Animal Cruelty"
## [36] "Kidnapping/Abduction"
## [37] "Statutory Rape"
#Categorize Violent and Nonviolent crime
crime.data <- crime.data %>%
mutate(violent = ifelse(NIBRS.Code.Name %in% c("Aggravated Assault",
"Murder & Nonnegligent Manslaughter",
"Sodomy", "Animal Cruelty", "Statutory Rape", "Rape",
"Fondling", "Arson", "Kidnapping/Abduction", "Intimidation",
"Simple Assault", "Weapon Law Violations"), 1, 0))
#Make sf Object from Latitude and Longitude
crime.sf <- st_as_sf(crime.data, coords = c("Longitude", "Latitude"), crs=4326)
#Get census data
bg <- get_acs("block_group",
variables = c(
'tot_popE' = 'B01001_001E', # Total Population
'male_popE' = 'B01001_002E', # Male Population
'female_popE' = 'B01001_026E', # Female Population
'median_incomeE' = 'B19013_001E', # Median Income
'edu_totalE' = 'B06009_001E', # Total (Educational Attainment)
'less_than_hsE' = 'B06009_002E', # Less than High School
'race_totalE' = 'B02001_001E', # Race Total
'whiteE' = 'B02001_002E', # White alone
'blackE' = 'B02001_003E', # Black alone
'nativeE' = 'B02001_004E', # American Indian and Alaska Native alone
'asianE' = 'B02001_005E', # Asian alone
'pacific_islanderE' = 'B02001_006E', # Native Hawaiian and Other Pacific Islander alone
'otherE' = 'B02001_007E' # Some Other Race alone
),
year = 2022,
state = "GA",
county = c("Fulton", "DeKalb", "Clayton", "Cobb"),
survey = "acs5",
geometry = TRUE,
output = "wide",
progress = FALSE)
## Getting data from the 2018-2022 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
bg <- bg %>%
select(GEOID, NAME, geometry, ends_with("E")) %>%
mutate(
pop_den = tot_popE / st_area(.),
white_ratio = whiteE / race_totalE,
black_ratio = blackE / race_totalE,
other_ratio = (nativeE + asianE + pacific_islanderE + otherE) / race_totalE,
less_than_hs_ratio = less_than_hsE / edu_totalE
)
#Spatial Join
bg <- bg %>% st_transform(crs=4326)
crime.sf <- crime.sf %>% st_transform(crs=4326)
bg_crime <- st_join(bg, crime.sf %>% mutate(count = ifelse(violent == 0, 1, 10000)))
#Group by geoid and summarize the count
bg_crime_count <- bg_crime %>%
group_by(GEOID) %>%
summarise(count = sum(count, na.rm = T)) %>%
st_drop_geometry()
bg <- bg %>%
left_join(bg_crime_count, by = "GEOID") %>%
mutate(violent_count = count %/% 10000,
nonviolent_count = count %% 10000)
bg <- bg %>% filter((!count == 0 | GEOID %in% c("131210010022", "130899800001", "131210053001", "131210065001",
"131210098042", "131210099005", "131210100042", "131210100071",
"131210100033", "131210095014", "131210094081", "131210089073",
"131210091041", "131210091052", "131210011012", "131210001001",
"131210102112", "131210080004", "131210053003", "131210068011",
"131210043002")))
tm_basemap(providers$CartoDB.Positron) +
tm_shape(bg) + tm_polygons(col = "pop_den", style = "jenks") +
#tm_shape(atlanta) + tm_polygons(alpha=0, border.col = "red") +
tm_shape(crime.sf) + tm_dots(size = 0.01)
t1 <- tm_basemap(providers$CartoDB.Positron) +
tm_shape(bg) + tm_polygons(col = "violent_count", style = "jenks", alpha = 0.3) +
tm_shape(crime.sf %>% filter(violent == 1)) + tm_dots(col = "red", size = 0.01)
t2 <- tm_basemap(providers$CartoDB.Positron) +
tm_shape(bg) + tm_polygons(col = "nonviolent_count", style = "jenks", alpha = 0.3) +
tm_shape(crime.sf %>% filter(violent == 0)) + tm_dots(col = "grey", size = 0.01)
tmap_arrange(t1, t2, sync = T)
#st_write(bg, "bg.geojson", driver = "GeoJSON")
zoning <- read.csv("Official_Zoning_Districts_-_Open_Data.csv")
zoning <- zoning %>% sort_by(.,zoning$ZONECLASS)
zoning %>% head()
## OBJECTID ZONECLASS
## 1 10709 C-1
## 4 10712 C-1
## 5 10713 C-1
## 7 10715 C-1
## 8 10716 C-1
## 28 10739 C-1
## ZONEDESC
## 1 https://library.municode.com/ga/atlanta/codes/code_of_ordinances?nodeId=PTIIICOORANDECO_PT16ZO_CH11COBUDIRE
## 4 https://library.municode.com/ga/atlanta/codes/code_of_ordinances?nodeId=PTIIICOORANDECO_PT16ZO_CH11COBUDIRE
## 5 https://library.municode.com/ga/atlanta/codes/code_of_ordinances?nodeId=PTIIICOORANDECO_PT16ZO_CH11COBUDIRE
## 7 https://library.municode.com/ga/atlanta/codes/code_of_ordinances?nodeId=PTIIICOORANDECO_PT16ZO_CH11COBUDIRE
## 8 https://library.municode.com/ga/atlanta/codes/code_of_ordinances?nodeId=PTIIICOORANDECO_PT16ZO_CH11COBUDIRE
## 28 https://library.municode.com/ga/atlanta/codes/code_of_ordinances?nodeId=PTIIICOORANDECO_PT16ZO_CH11COBUDIRE
## BASEELEV HEIGHT LASTUPDATE LASTEDITOR SPI SUBAREA TSA STATUS CASEIN CASEOUT
## 1 0 0 Current
## 4 0 0 Current
## 5 0 0 Current
## 7 0 0 Current
## 8 0 0 Current
## 28 0 0 Current
## SUNRISE SUNSET CREATEDATE CREATEDBY
## 1 1900/01/01 00:00:00+00 2999/12/31 00:00:00+00
## 4 1900/01/01 00:00:00+00 2999/12/31 00:00:00+00
## 5 1900/01/01 00:00:00+00 2999/12/31 00:00:00+00
## 7 1900/01/01 00:00:00+00 2999/12/31 00:00:00+00
## 8 1900/01/01 00:00:00+00 2999/12/31 00:00:00+00
## 28 1900/01/01 00:00:00+00 2999/12/31 00:00:00+00
## CREATED_USER CREATED_DATE LAST_EDITED_USER LAST_EDITED_DATE
## 1 GIS 2018/04/19 00:00:00+00 GIS 2020/07/25 00:00:00+00
## 4 GIS 2018/04/19 00:00:00+00 GIS 2020/07/25 00:00:00+00
## 5 GIS 2018/04/19 00:00:00+00 GIS 2020/07/25 00:00:00+00
## 7 GIS 2018/04/19 00:00:00+00 GIS 2020/07/25 00:00:00+00
## 8 GIS 2018/04/19 00:00:00+00 GIS 2020/07/25 00:00:00+00
## 28 GIS 2018/04/19 00:00:00+00 GIS 2020/07/25 00:00:00+00
## GLOBALID ACRES ZONING EDITTED REFERENCE
## 1 {580C6D97-2A8F-487B-BB3A-67A6D0651D8B} 1.2923615 C-1 R3371
## 4 {D8BA20DC-E1A4-4FFE-B8B0-7DB3B984CA21} 0.8383994 C-1 R1249
## 5 {6E322071-34B4-4817-86DA-DD32D304EB7F} 5.8933400 C-1 R1983
## 7 {8503437E-AE71-489C-A316-BE97EA19FBE3} 21.6859544 C-1 R999
## 8 {3F33761C-5C4D-490B-B21D-42C3FBA80BC6} 0.6387836 C-1 R640
## 28 {8839DAC8-5014-44B7-AA0A-16008F13D6A8} 0.7497545 C-1 R392
## SHAPE_Area SHAPE_Length
## 1 56295.04 1050.4486
## 4 36520.53 1172.7321
## 5 256712.86 3362.3735
## 7 944636.40 6673.8263
## 8 27825.30 766.6279
## 28 32659.18 730.4584
unique(zoning$ZONECLASS)
## [1] "C-1" "C-1-C" "C-2"
## [4] "C-2-C" "C-3" "C-3-C"
## [7] "C-4" "C-4-C" "C-5"
## [10] "C-5-C" "FCR-3" "HC-20A SA1"
## [13] "HC-20A SA2" "HC-20A SA3" "HC-20A SA4"
## [16] "HC-20A SA4-C" "HC-20A SA5" "HC-20B"
## [19] "HC-20C SA1" "HC-20C SA2" "HC-20C SA3"
## [22] "HC-20C SA3-C" "HC-20C SA4" "HC-20D"
## [25] "HC-20E" "HC-20N SA1" "HC-20N SA2"
## [28] "I-1" "I-1-C" "I-2"
## [31] "I-2-C" "I-MIX" "I-Mix-C"
## [34] "I-MIX-C" "LD Briarcliff Plaza" "LD Mean Street"
## [37] "LD Pratt- Pullman" "LW" "LW-C"
## [40] "MR-1" "MR-1-C" "MR-2"
## [43] "MR-2-C" "MR-3" "MR-3-C"
## [46] "MR-3A-C" "MR-4-C" "MR-4A"
## [49] "MR-4A-C" "MR-4B" "MR-4B-C"
## [52] "MR-5A-C" "MR-MU" "MRC-1"
## [55] "MRC-1-C" "MRC-2" "MRC-2-C"
## [58] "MRC-3" "MRC-3-C" "MRC-4B-C"
## [61] "NC-1" "NC-10 SA1" "NC-10 SA2"
## [64] "NC-11" "NC-12 SA1" "NC-12 SA2"
## [67] "NC-13" "NC-14" "NC-15"
## [70] "NC-2" "NC-3" "NC-4"
## [73] "NC-5" "NC-5-C" "NC-6"
## [76] "NC-7" "NC-7-C" "NC-8"
## [79] "NC-9" "O-I" "O-I-C"
## [82] "PD-H" "PD-H1" "PD-H2"
## [85] "PD-MU" "PD-OC" "Poncey-Highland SA1"
## [88] "Poncey-Highland SA2" "Poncey-Highland SA3" "Poncey-Highland SA4"
## [91] "Poncey-Highland SA5" "Poncey-Highland SA6" "Poncey-Highland SA7"
## [94] "R-1" "R-2" "R-2A"
## [97] "R-2A-C" "R-2B" "R-3"
## [100] "R-3-C" "R-3A" "R-4"
## [103] "R-4-C" "R-4A" "R-4A-C"
## [106] "R-4B" "R-4B-C" "R-5"
## [109] "R-5-C" "R-LC" "R-LC-C"
## [112] "RG-1" "RG-1-C" "RG-2"
## [115] "RG-2-C" "RG-3" "RG-3-C"
## [118] "RG-4" "RG-4-C" "RG-5"
## [121] "RG-5-C" "SPI-1 SA1" "SPI-1 SA2"
## [124] "SPI-1 SA3" "SPI-1 SA4" "SPI-1 SA5"
## [127] "SPI-1 SA6" "SPI-1 SA7" "SPI-11 SA2"
## [130] "SPI-11 SA6" "SPI-11 SA8" "SPI-11 SA9"
## [133] "SPI-12 SA1" "SPI-12 SA1-C" "SPI-12 SA2"
## [136] "SPI-12 SA2-C" "SPI-12 SA3" "SPI-12 SA3-C"
## [139] "SPI-12 SA4" "SPI-15 SA1" "SPI-15 SA2"
## [142] "SPI-15 SA3" "SPI-15 SA4" "SPI-15 SA5"
## [145] "SPI-15 SA6" "SPI-15 SA7" "SPI-15 SA8"
## [148] "SPI-15 SA9" "SPI-16 SA1" "SPI-16 SA1C"
## [151] "SPI-16 SA2" "SPI-16 SA2 JSTA" "SPI-16 SA3"
## [154] "SPI-17 SA1" "SPI-17 SA2" "SPI-17 SA3"
## [157] "SPI-17 SA4" "SPI-18 SA1" "SPI-18 SA10"
## [160] "SPI-18 SA2" "SPI-18 SA3" "SPI-18 SA4"
## [163] "SPI-18 SA5" "SPI-18 SA6" "SPI-18 SA7"
## [166] "SPI-18 SA8" "SPI-18 SA9" "SPI-19 SA1"
## [169] "SPI-19 SA10" "SPI-19 SA11" "SPI-19 SA2"
## [172] "SPI-19 SA3" "SPI-19 SA4" "SPI-19 SA5"
## [175] "SPI-19 SA6" "SPI-19 SA7" "SPI-19 SA8"
## [178] "SPI-2 SA1" "SPI-2 SA2" "SPI-2 SA3"
## [181] "SPI-2 SA4" "SPI-2 SA5" "SPI-20 SA1"
## [184] "SPI-20 SA2" "SPI-20 SA3" "SPI-20 SA4"
## [187] "SPI-20 SA5" "SPI-20 SA6" "SPI-21 SA1"
## [190] "SPI-21 SA10" "SPI-21 SA2" "SPI-21 SA3"
## [193] "SPI-21 SA4" "SPI-21 SA5" "SPI-21 SA7"
## [196] "SPI-21 SA8" "SPI-21 SA9" "SPI-22 SA1"
## [199] "SPI-22 SA2" "SPI-22 SA3" "SPI-22 SA4"
## [202] "SPI-22 TSA" "SPI-3 SA1" "SPI-3 SA2"
## [205] "SPI-3 SA3" "SPI-3 SA3-C" "SPI-3 SA4"
## [208] "SPI-3 SA5" "SPI-3 SA5-C" "SPI-3 SA6"
## [211] "SPI-3 SA7" "SPI-3 SA8" "SPI-3 SA9"
## [214] "SPI-4 SA 11" "SPI-4 SA1" "SPI-4 SA10"
## [217] "SPI-4 SA11" "SPI-4 SA12" "SPI-4 SA13"
## [220] "SPI-4 SA2" "SPI-4 SA3" "SPI-4 SA4"
## [223] "SPI-4 SA5" "SPI-4 SA6" "SPI-4 SA7"
## [226] "SPI-4 SA8" "SPI-4 SA9" "SPI-5 SA1"
## [229] "SPI-5 SA2" "SPI-5 SA3" "SPI-7 SA1"
## [232] "SPI-7 SA2A" "SPI-7 SA2B" "SPI-7 SA2C"
## [235] "SPI-7 SA3" "SPI-9-C SA1" "SPI-9 SA1"
## [238] "SPI-9 SA2" "SPI-9 SA2-C" "SPI-9 SA3"
## [241] "SPI-9 SA4"
zoning <- zoning %>%
mutate(class = case_when(
str_detect(ZONECLASS, "^(R-(1|2|3|4|5)|PD-H|FCR-)") ~ "Low-density Residential",
str_detect(ZONECLASS, "^(R-LC|MRC-|PD-MU)") ~ "Residential-Commercial",
str_detect(ZONECLASS, "^(RG-|MR-)") ~ "High-density Residential",
str_detect(ZONECLASS, "^I-") ~ "Industrial",
str_detect(ZONECLASS, "^(C-|LD|LW|NC|PD-OC)") ~ "Commercial",
str_detect(ZONECLASS, "^O-") ~ "Institutional",
#Historical regions
str_detect(ZONECLASS, "^(HC-20A SA1|HC-20C SA4|HC-20N|Poncey-Highland SA(3|4|5))") ~ "Residential-Commercial",
str_detect(ZONECLASS, "^(HC-20A SA3|HC-20C SA(1|2)|HC-20B|Poncey-Highland SA(7))") ~ "Low-density Residential",
str_detect(ZONECLASS, "^(HC-20A SA2|Poncey-Highland SA(1|2|6))") ~ "High-density Residential",
str_detect(ZONECLASS, "^(HC-20A SA(4|5))") ~ "Commercial",
str_detect(ZONECLASS, "^(HC-20C SA3|HC-20D|HC-20E)") ~ "Institutional",
#Special Areas
str_detect(ZONECLASS, "^(SPI-1 SA(1|2|3|7)|SPI-9(-C| SA1)|SPI-11 SA(2|9)|SPI-12 SA1|SPI-15 SA(1|2|3|4|9)|SPI-18 SA3|SPI-19 SA(2|10)|SPI-20 SA(1|2)|SPI-21 SA(1|2|3|4|10)|SPI-22 SA4)") ~ "Commercial",
str_detect(ZONECLASS, "^(SPI-1 SA(5|6)|SPI-2 SA5|SPI-3 SA(4|5|6|7|8|9)|SPI-4 SA(3|4|7|10|13)|SPI-9 SA(2|3|4)|SPI-16 SA1|SPI-18 SA(1|2|10)|SPI-19 SA(1|4|8|9|11)|SPI-20 SA(3|4)|SPI-22 TSA)") ~ "Residential-Commercial",
str_detect(ZONECLASS, "^(SPI-1 SA4|SPI-3 SA3|SPI-4 SA(2|5|11| 11)|SPI-5 SA(2|3)|SPI-7 SA3|SPI-11 SA8|SPI-12 SA2|SPI-15 SA(5|6|7|8)|SPI-16 SA(2|3)|SPI-17|SPI-18 SA(4|9)|SPI-19 SA7|SPI-20 SA5|SPI-21 SA5|SPI-22 SA3)") ~ "High-density Residential",
str_detect(ZONECLASS, "^(SPI-2 SA(1|2|3|4)|SPI-3 SA(1|2)|SPI-4 SA(6|9)|SPI-5 SA1|SPI-7 SA(1|2)|SPI-11 SA6|SPI-12 SA3|SPI-18 SA(5|6)|SPI-19 SA(5|6)|SPI-20 SA6|SPI-21 SA7|SPI-22 SA2)") ~ "Low-density Residential",
str_detect(ZONECLASS, "^(SPI-4 SA8|SPI-18 SA(7|8)|SPI-21 SA9|SPI-22 SA1)") ~ "Industrial",
str_detect(ZONECLASS, "^(SPI-4 SA12|SPI-12 SA4|SPI-19 SA3|SPI-21 SA8)") ~ "Institutional",
str_detect(ZONECLASS, "SPI-4 SA1") ~ "Low-density Residential",
TRUE ~ ""
))
zoning.shape <- st_read("Official_Zoning_Data.geojson")
## Reading layer `Official_Zoning_Districts_-_Open_Data' from data source
## `C:\Users\benso\Documents\Georgia Tech\CP6025\Final Project\Official_Zoning_Data.geojson'
## using driver `GeoJSON'
## Simple feature collection with 2856 features and 28 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY, XYZ
## Bounding box: xmin: -84.55086 ymin: 33.64798 xmax: -84.28962 ymax: 33.88687
## z_range: zmin: 0 zmax: 0
## Geodetic CRS: WGS 84
zoning.sf <- zoning.shape %>%
select(OBJECTID) %>%
left_join(zoning, by = "OBJECTID") %>%
st_make_valid()
tmap_mode("view")
## tmap mode set to interactive viewing
tm_basemap(providers$CartoDB.Positron) +
tm_shape(zoning.sf) + tm_polygons(col = "class",
palette = c("dodgerblue", "forestgreen", "gold", "mediumpurple1", "yellowgreen", "lightseagreen"),
popup.vars=c("ZONECLASS" = "ZONECLASS", "class" = "class")) +
tm_tiles(providers$CartoDB.Positron, alpha=0.4)