{"cells":[{"metadata":{"_uuid":"39c5f0c6-db4c-4157-99f1-0e51bb42fa87","_cell_guid":"8a7e4073-3e13-4e3a-91f3-f140bb115b87","trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"library(tidyverse)\nlibrary(sf)\nlibrary(dplyr)\nlibrary(janitor)\nlibrary(ggplot2)\nlibrary(wordcloud)\nlibrary(RColorBrewer)\nlibrary(wordcloud2)\nlibrary(tm)\nlibrary(scales)\nlibrary(here)\nlibrary(tidytext)\nlibrary(sf)\nlibrary(spData)\nlibrary(ggsci)\nlibrary(wesanderson)\nlibrary(kableExtra)\nlibrary(igraph) \nlibrary(ggraph)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"city_2020 <- read_csv(\"../input/cdp-unlocking-climate-solutions/Cities/Cities Responses/2020_Full_Cities_Dataset.csv\")\n#summary(city_2020)\ncity_2020_us <- city_2020 %>%\n filter(Country == 'United States of America') \n\ncity_2020_us <- data.frame(city_2020_us)\nclean_names(city_2020_us) \n#city_2020_us\n#summary(city_2020_us)\n\n\n### Read other files\ncity_2020_disclosure_raw <- read_csv(\"../input/cdp-unlocking-climate-solutions/Cities/Cities Disclosing/2020_Cities_Disclosing_to_CDP.csv\")\ncity_2020_disclosure_US <- city_2020_disclosure_raw %>% \n filter(Country == 'United States of America') %>% \n clean_names()\n\ncity_2019_disclosure_raw <-read_csv(\"../input/cdp-unlocking-climate-solutions/Cities/Cities Disclosing/2019_Cities_Disclosing_to_CDP.csv\")\ncity_2019_disclosure_US <- city_2019_disclosure_raw %>% \n filter(Country == 'United States of America') %>% \n clean_names()\n\ncorp_disclosing_raw <- read_csv(\"../input/cdp-unlocking-climate-solutions/Corporations/Corporations Disclosing/Climate Change/2018_Corporates_Disclosing_to_CDP_Climate_Change.csv\")\ncorp_answer_raw <- read_csv(\"../input/cdp-unlocking-climate-solutions/Corporations/Corporations Responses/Climate Change/2020_Full_Climate_Change_Dataset.csv\")\n","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"#This section cleans up the names, extract the states, assign geometry information as much as possible\n\n#Combine the two years' dataset together\ncity_disclosure_US <- bind_rows(city_2019_disclosure_US, city_2020_disclosure_US) %>% \n  select(-year_reported_to_cdp, -reporting_authority, -access, -first_time_discloser, -population, -population_year, - last_update) %>% \n  unique()\n\n\ncity_disclosure_US_state_wrangling <- city_disclosure_US %>% \n  mutate(organization_state = case_when(\n    str_detect(organization, \", \") ~ str_extract(organization,  \"[^, ]+$\"),\n    TRUE ~ NA_character_\n    )) %>% \n  mutate(city_state = case_when(\n    str_detect(city, \", \") ~ str_extract(city,  \"[^, ]+$\"),\n    TRUE ~ NA_character_\n  )) %>% \n  mutate(city_location = str_remove(city_location, \"POINT \")) %>% \n  mutate(lat =as.numeric(str_extract(city_location, \"[^\\\\(]+[ $]\"))) %>% \n  mutate(lon = str_extract(city_location, \"[^ ]+\\\\)$\")) %>% \n  mutate(lon =as.numeric(str_remove(lon, \"\\\\)\")))\n#  mutate(point_location = st_as_sf(coords = c('lat', 'lon'), crs = 4326))\n#  mutate(geometry = st_as_sf(city_location))\n\ntmp <- city_disclosure_US_state_wrangling %>% \n  select(account_number, lat, lon) %>% \n  drop_na()\n\ncity_disclosure_US_state_sf <- st_as_sf(tmp, coords = c('lat', 'lon'), crs = 4326)\n\nus_states <-st_transform(spData::us_states, crs = 4326)\n\ntmp <- as.integer(st_intersects(city_disclosure_US_state_sf, us_states))\n\ncity_disclosure_US_state_sf_new <- bind_cols(city_disclosure_US_state_sf, tmp) %>% \n  rename(stateid = '...3') %>% \n  dplyr::mutate(state = us_states$NAME[stateid]) %>% \n  select(-stateid)\n\ncity_disclosure_US_state_wrangling_sf <- full_join(city_disclosure_US_state_wrangling, city_disclosure_US_state_sf_new) %>% \n  mutate(state_geo = state.abb[match(state, state.name)]) %>% \n  rename(NAME = state) %>% \n  mutate(state = case_when(\n    !is.na(state_geo) ~ state_geo,\n    !is.na(organization_state) ~ organization_state,\n    !is.na(city_state) ~ city_state,\n    TRUE ~ NA_character_\n  ))\n\n#Several cities' infomation need to be updated manually\ncity_disclosure_simple <- city_disclosure_US_state_wrangling_sf %>% \n  select(account_number, organization, city, state, NAME) %>% \n  mutate(state = case_when(\n    city == 'St. Petersburg' ~ \"FL\",\n    organization == 'Town of Ithaca' ~ \"NY\",\n    city == 'Omaha' ~ \"NE\",\n    city == 'Wellfleet' ~ \"MA\",\n    organization == 'Dane County' ~ \"WI\",\n    city == \"Honolulu\" ~ \"HI\",\n    city == \"Alameda\" ~ \"CA\",\n    city == \"Anchorage\" ~ \"AK\",\n    city == 'Norfolk' ~ \"VA\",\n    city == 'El Paso' ~ \"TX\",\n    city == 'Sarasota' ~ \"FL\",\n    city == 'Spokane' ~ \"WA\",\n    organization == 'Cuyahoga County' ~ \"OH\",\n    city == 'Berkeley' ~ \"CA\",\n    city == 'Des Moines' ~ \"IA\",\n    organization == \"City of Grand Rapids\" ~ \"MI\",\n    organization == \"City of Henderson\" ~ \"NV\",\n    organization == \"San Luis Obispo\" ~ \"CA\",\n    organization == \"City of Albuquerque\" ~ \"NM\",\n    TRUE ~ state\n  )) %>% \n  mutate(NAME = case_when(\n    state == \"DC\" ~ \"District of Columbia\",\n    TRUE ~ state.name[match(state, state.abb)]\n    ))\n\n#Producing clean version of the city names\ncity_disclosure_full <- left_join(city_disclosure_simple, us_states) %>% \n  select(account_number, organization, city, state, NAME, geometry) %>% \n  rename(state_abb = state,\n         state = NAME) %>% \n  mutate(organization = case_when(\n    str_detect(organization, \",\") ~ str_extract(organization, \"^.+?(?=,)\"),\n    TRUE ~ organization\n    )) %>% \n  mutate(organization_type = case_when(\n    str_detect(organization, \"City of\") ~ \"City\",\n    str_detect(organization, \"County\") ~ \"County\",\n    str_detect(organization, \"City\") ~ \"City\",\n    str_detect(organization, \"Town\") ~ \"Town/Village\",\n    str_detect(organization, \"Village\") ~ \"Town/Village\",\n    str_detect(organization, \"Township\") ~ \"Town/Village\",\n    str_detect(organization, \"Metropolitan\") ~ \"City\",\n    TRUE ~ \"City\"\n  )) %>% \n  mutate(name = case_when(\n    str_detect(city, \",\") ~ str_extract(city, \"^.+?(?=,)\"),\n    is.na(city) ~ organization,\n    TRUE ~ city\n  )) %>% \n  mutate(name = case_when(\n    str_detect(name, \"City of \") ~ str_remove(name, \"City of \"),\n    str_detect(name, \" County\") ~ str_remove(name, \" County\"),\n    str_detect(name, \"Town of \") ~ str_remove(name, \"Town of \"),\n    str_detect(name, \"Village of \") ~ str_remove(name, \"Village of \"),\n    TRUE ~ name\n  ))","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true,"_kg_hide-output":true},"cell_type":"code","source":"#where are they?\n#Finished, this chunk will output a map showing how many cities in each state has feedback\n\nus_states_clean <- us_states %>% \n  rename(state = NAME) %>% \n  clean_names()\n\ncity_disclosure_full_sf <- city_disclosure_full %>% \n  group_by(state) %>% \n  summarise(city_count = n()) %>% \n  mutate(state_abb = case_when(\n    state == \"District of Columbia\" ~ \"DC\",\n    TRUE ~ state.abb[match(state, state.name)]\n    ))\n\nstate_tmp <- data.frame(state.name, state.abb)\nnames(state_tmp) <- c(\"state\", \"state_abb\")\nstate_dc <- data.frame(\"District of Columbia\", \"DC\")\nnames(state_dc) <- c(\"state\", \"state_abb\")\n\nstate_tmp <- bind_rows(state_tmp, state_dc)\n\ncity_disclosure_full_sf <- full_join(city_disclosure_full_sf, state_tmp)\n\ncity_disclosure_full_sf$city_count <- replace_na(city_disclosure_full_sf$city_count, 0)\n\ncity_count_sf <- left_join(us_states_clean, city_disclosure_full_sf) %>% \n  filter(state_abb != \"AK\",\n         state_abb != \"HI\")\n\ncity_count_sf <- st_as_sf(city_count_sf)\n\nggplot(city_count_sf)+\n  geom_sf(aes(fill = city_count))+\n  scale_fill_gradient(low = \"white\", high = \"green\", na.value = NA)\n  theme_classic()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"#Match US cities and counties \ncity_county <- read_csv(\"../input/farmers-markets-in-the-united-states/farmers_markets_from_usda.csv\")\n\ncity_county <- city_county%>% \n    select(State, County, city)%>%\n    mutate(name = city, state = State)%>%\n    select(-city, -State)\n\ncity_county_full<- left_join(city_disclosure_full, city_county, by = c(\"state\", \"name\"))\n\ncity_county_full <- city_county_full %>%\n    select(account_number, organization_type,state, County, name)%>%\n    distinct()%>%\n    rename(Account.Number= account_number)\n\n#city_county_full ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"city_county_full<-city_county_full %>%\n    filter(County != \"Kent County\") %>%\n    filter(County != \"Albany County\")%>%\n    filter(County != \"Hennepin County\")%>%\n    filter(County != \"Baltimore City\")%>%\n    filter(County != \"EL Paso\")%>%\n    filter(County != \"Bexar County\")%>%\n    filter(County != \"Richmond City\")\n\n\ncity_county_full$County[city_county_full$name %in% \"Key West\"] <- \"Monroe\"\ncity_county_full$County[city_county_full$name %in% \"Snoqualmie\"] <- \"King\"\ncity_county_full$County[city_county_full$name %in% \"Miramar\"] <- \"Broward\"\ncity_county_full$County[city_county_full$name %in% \"Winter Park\"] <- \"Orange\"\n\ncity_county_full$County[city_county_full$name %in% \"St. Petersburg\"] <- \"Pinellas\"\ncity_county_full$County[city_county_full$name %in% \"South Barrington\"] <- \"Cook\"\n\ncity_county_full$County[city_county_full$name %in% \"LaGrange\"] <- \"Lewis\"\ncity_county_full$County[city_county_full$name %in% \"Cambridge\"] <- \"Isanti\"\ncity_county_full$County[city_county_full$name %in% \"San José\"] <- \"Santa Clara\"\ncity_county_full$County[city_county_full$name %in% \"Plano\"] <- \"Collin\"\ncity_county_full$County[city_county_full$name %in% \"Saint Paul\"] <- \"Ramsey\"\ncity_county_full$County[city_county_full$name %in% \"Piedmont\"] <- \"Alameda\"\ncity_county_full$County[city_county_full$name %in% \"Hallandale Beach\"] <- \"Broward\"\ncity_county_full$County[city_county_full$name %in% \"Natchez\"] <- \"Natchez\"\ncity_county_full$County[city_county_full$name %in% \"Emeryville\"] <- \"Alameda\"\ncity_county_full$County[city_county_full$name %in% \"Dubuque\"] <- \"Jo Daviess\"\n","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"#######################\n### KPI Development ###\n#######################","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"### 1. Reporting Index\n\n#1.1 Climate Targets\n#This part is to evaluate whether cities have set climate targets\ncity_2020_us_goal <- city_2020_us %>%\n    filter(Question.Number == '1.0') %>%\n    mutate(index_rep_goal = case_when(\n        Response.Answer == \"Yes\" ~ 20,\n        Response.Answer == \"Not intending to incorporate\" ~ 0,\n        Response.Answer == \"In progress\" ~ 15,\n        Response.Answer == \"Intending to incorporate in the next 2 years\" ~ 10,\n        Response.Answer == \"Do not know\" ~ 0))%>%\n    select(Account.Number, index_rep_goal)\n\n#city_2020_us_goal","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"### 1.2 GHG emissions\n#This part is to evalaute the status of cities' GHG reporting\n\ncity_2020_us_cap <-  city_2020_us %>%\n    filter(Question.Number == '0.5') %>%\n    filter(Column.Number == '1') %>%\n    mutate(\n    Population = Response.Answer,\n    City = Organization)%>%\n    select(Account.Number,City, Population)\n\n#city_2020_us_cap\n\ncity_2020_us_ghg <- city_2020_us %>%\n    filter(Question.Number == '4.6c') %>%\n    filter(Column.Number == '6'|Column.Number == '8'|Column.Number == '13')%>%\n    #filter(Response.Answer != 'Question not applicable') %>%\n    select(Account.Number, Organization,Column.Number, Response.Answer)\n\ncity_2020_us_ghg_scp1 <- city_2020_us_ghg%>%\n    filter(Column.Number == '6')%>%\n    select(-Column.Number)\n    \n\ncity_2020_us_ghg_scp2 <-city_2020_us_ghg%>%\n    filter(Column.Number == '8')%>%\n    select(-Column.Number)\ncity_2020_us_ghg_scp3 <- city_2020_us_ghg%>%\n    filter(Column.Number == '13')%>%\n    select(-Column.Number)\n\ncity_2020_us_ghg_sum <- merge(city_2020_us_ghg_scp1, city_2020_us_ghg_scp2, by = \"Account.Number\")%>%\n    merge(city_2020_us_ghg_scp3, by = \"Account.Number\")%>%\n    merge(city_2020_us_cap, by = \"Account.Number\")%>%\n    select(Account.Number, City, Population,Response.Answer.x,Response.Answer.y, Response.Answer)\n\ncolnames(city_2020_us_ghg_sum) <- c(\"Account.Number\",\"City\", \"Population\",\"Scp1\", \"Scp2\",\"Scp3\")\n\ncity_2020_us_ghg_sum_score<- city_2020_us_ghg_sum %>%\n    mutate(sc_1 = ifelse(Scp1 != 'Question not applicable' & !is.na(Scp1), 20, 0),\n          sc_2 = ifelse(Scp2 != 'Question not applicable' & !is.na(Scp2), 20, 0),\n          sc_3 = ifelse(Scp3 != 'Question not applicable' & !is.na(Scp3), 20, 0),\n          index_rep_ghg = sc_1+sc_2+sc_3)\n\n","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"### 1.3 Climate-related risks\n#This part is to evaluate whether cities disclosed their climate-related risks\ncity_2020_us_risk <- city_2020_us %>%\n    filter(Question.Number == '2.0') %>%\n    mutate(index_rep_risk = case_when(\n        Response.Answer == \"Yes\" ~ 20,\n        Response.Answer == \"Not intending to undertake\" ~ 0,\n        Response.Answer == \"In progress\" ~ 15,\n        Response.Answer == \"Intending to undertake in the next 2 years\" ~ 10,\n        Response.Answer == \"Do not know\" ~ 0))%>%\n    select(Account.Number, index_rep_risk)\n#city_2020_us_risk","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"index_rep <- city_2020_us_goal%>%\n    merge(city_2020_us_ghg_sum_score, by = \"Account.Number\")%>%\n    merge(city_2020_us_risk, by = \"Account.Number\")%>%\n    mutate(index_rep = index_rep_goal+ index_rep_ghg+ index_rep_risk)%>%\n    select(Account.Number,index_rep, index_rep_goal, index_rep_ghg, index_rep_risk)\n#index_rep ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"ggplot(index_rep, aes(x = index_rep))+\n  geom_histogram()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"### 2. Mitigation index \n###Effectiveness of reduction measures of each policy\ncity_2020_us_5_4 <- city_2020_us %>%\n    filter(Question.Number == '5.4') %>%\n    select(Account.Number, Organization,Row.Number,Column.Number, Response.Answer)\n\n\ncity_2020_us_5_4_GHG <- city_2020_us_5_4%>%\n    filter(Column.Number =='1'| Column.Number =='2'| Column.Number =='5'| Column.Number == '12')%>%\n    spread(Column.Number, Response.Answer)%>%    \n    separate('1', into = c(\"Category_pre\",\"Subcategory\"),  sep = \">\")\n\ncity_2020_us_5_4_GHG$Category<- trimws(city_2020_us_5_4_GHG$Category_pre, which = c(\"right\"))\n\ncity_2020_us_5_4_GHG <- city_2020_us_5_4_GHG %>%\n    select(-Category_pre)\n\ncolnames(city_2020_us_5_4_GHG) <- c(\"Account.Number\",\"City\" ,\"Row.Number\", \"Subcategory\", \"Action\",\"Reduction\", \"Cost\",\"Category\")\n\ncity_2020_us_5_4_GHG$Reduction <- as.numeric(as.character(city_2020_us_5_4_GHG$Reduction))\ncity_2020_us_5_4_GHG$Cost <- as.numeric(as.character(city_2020_us_5_4_GHG$Cost))\n\n#city_2020_us_5_4_GHG \n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"###Create a rubric for mitigation actions\nCategory <- c(\"Buildings\",\"Community-Scale Development\", \"Energy Supply\", \"Finance and Economic Development\", \"Food and Agriculture\", \"Mass Transit\", \"Outdoor Lighting\", \"Private Transport\",\"Waste\", \"Water\")\n#Score <- c(\"3\",\"2\",\"1\",\"2\",\"1\",\"2\",\"2\",\"2\",\"1\",\"1\")\nScore <- c(3,2,1,2,1,2,2,2,1,1)\n#as.numeric(Score)\n\nindex_mit_rub <- data.frame(as.character(Category), Score)\nindex_mit_rub <- data.frame(lapply(index_mit_rub, as.character), stringsAsFactors=FALSE)\n\ncolnames(index_mit_rub) <- c(\"Category\",\"Score\")\n#index_mit_rub\n\n\n### Scoring cities based on mitigation action\nindex_mit <- merge(city_2020_us_5_4_GHG, index_mit_rub, by = \"Category\", all = TRUE)\nindex_mit$Score[is.na(index_mit$Score)] <- 0\nindex_mit$Score <- as.numeric(as.character(index_mit$Score))\n\n#index_mit\n\nindex_mit <- index_mit %>%\n    group_by(Account.Number)%>%\n    summarise(score_sum = sum(Score))%>%\n    mutate(index_mit = case_when(   \n        score_sum <= 5  ~ 20,\n        score_sum <= 15 & score_sum > 5 ~ 40,\n        score_sum <= 25 & score_sum > 15 ~ 60,\n        score_sum <= 35 & score_sum > 25 ~ 80,\n        score_sum > 35 & score_sum >0 ~ 100\n    ))\n#index_mit","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"ggplot(index_mit, aes(x = index_mit))+\n  geom_histogram()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"disaster_raw <- read_csv(\"../input/openfema-dataset-disaster-declarations-summaries/DisasterDeclarationsSummaries.csv\")\n\ndisaster_clean <- disaster_raw %>% \n  select(femaDeclarationString, disasterNumber, state, declarationType, fyDeclared, incidentType, fipsStateCode) %>% \n  distinct()\n\ndisaster_byState <- disaster_clean %>% \n  filter(fyDeclared > 1980,\n         fyDeclared <= 2020) %>% \n  group_by(state, incidentType, fipsStateCode) %>% \n  summarise(disaster_count = n()) %>% \n  rename(state_abb = state)\n#disaster_byState","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"#Use the R built-in date sets to create a state data frame\nstate_tmp <- data.frame(state.name, state.abb)\nstate_dc <- data.frame(\"District of Columbia\", \"DC\")\nnames(state_dc) <- c(\"state.name\", \"state.abb\")\nstate_tmp <- bind_rows(state_tmp, state_dc) %>% \n  rename(state_abb = state.abb) %>% \n  rename(state = state.name)\n#Join the disaster df and the state df to create a new df\ndisaster_full <- full_join(disaster_byState, state_tmp)\n#Replace the none reported states' value to 0, since no disaster was reported\ndisaster_full$disaster_count <- replace_na(disaster_full$disaster_count, 0)\n\ndisaster_frequent <- disaster_full %>% \n  filter(disaster_count >= 5) %>% \n  drop_na() %>% \n  filter(incidentType != \"Earthquake\" & incidentType != \"Biological\") %>% \n  mutate(hazard_category = case_when(\n    incidentType == \"Fire\" ~ \"Wild fire\",\n    incidentType == \"Flood\" ~ \"Flood and sea level rise\",\n    incidentType == \"Severe Storm(s)\" ~ \"Storm and wind\",\n    incidentType == \"Hurricane\" ~ \"Storm and wind\",\n    incidentType == \"Severe Ice Storm\" ~ \"Extreme cold temperature\",\n    incidentType == \"Tornado\" ~ \"Storm and wind\",\n    incidentType == \"Snow\" ~ \"Extreme Precipitation\"\n  )) %>% \n  ungroup() %>% \n  select(-incidentType) %>% \n  rename(state_fips = fipsStateCode) %>% \n  group_by(state_abb, state, state_fips, hazard_category) %>% \n  summarise(disaster_count = sum(disaster_count)) %>% \n  drop_na()","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"city_answer_raw <- read_csv(\"../input/cdp-unlocking-climate-solutions/Cities/Cities Responses/2020_Full_Cities_Dataset.csv\")\n\ncity_answer_clean <- city_answer_raw %>% \n  clean_names() %>% \n  filter(country == \"United States of America\") %>% \n  select(-questionnaire, -year_reported_to_cdp, -country, -cdp_region, -last_update)\n\nquestion_3.0 <- city_answer_clean %>% \n  filter(question_number == \"3.0\") %>% \n  select(-row_name, -comments, -file_name, -parent_section, -section, -column_number, -question_number, -question_name) %>% \n  filter(column_name %in% c(\"Climate hazards\", \"Action\", \"Status of action\")) %>% \n  drop_na() %>% \n  mutate(account_row = paste0(as.character(account_number), \"_\", as.character(row_number)))\n\nquestion_30_tmp1 <- question_3.0 %>% \n  select(-organization, -account_row) %>% \n  spread(column_name, response_answer) %>%\n  clean_names() %>% \n  mutate(climate_hazard_highlevel = str_extract(climate_hazards, \"^.+?(?= \\\\>)\")) \n\nquestion_30_hazard_detection <- question_30_tmp1 %>% \n  select(account_number, climate_hazard_highlevel) %>% \n  drop_na() %>% \n  rename(hazard_category = climate_hazard_highlevel)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"#Calculate KPI from now on, consider a for loop\n\naccount_col <- c()\nscore_col <- c()\n\nfor (i in city_disclosure_full$account_number) {\n  row_id <- match(i, city_disclosure_full$account_number)\n  action_subset <- question_30_hazard_detection %>% \n    filter(account_number == i)\n  action_subset_vec <- unique(as.vector(action_subset$hazard_category))\n  hazard_subset <- disaster_frequent %>% \n    filter(state_abb == city_disclosure_full$state_abb[row_id])\n  hazard_subset_vec <- unique(as.vector(hazard_subset$hazard_category))\n  if (i %in% question_30_hazard_detection$account_number) {\n    if (length(intersect(action_subset_vec, hazard_subset_vec)) == 0) {\n      score_i = 50\n      } else if (all(hazard_subset_vec %in% action_subset_vec)) {\n        score_i = 100\n        } else {\n          score_i = 75\n          }\n  } else {score_i = 25\n    }\n  account_col <- append(account_col, i)\n  score_col <- append(score_col, score_i)\n}\n\n#Up to this point, cities answered question 3.0 in the year of 2020 have a score assigned already. However, NA values are applied to two cases. 1. The city answered the questionnaire of 2020, but did not answer question 3.0; 2. The city did not answer the questionnaire at all. \n\n#Give a score 0 to the situation 1. in the previous comment\n\nreaction_KPI <- data.frame(account_col, score_col)\ncolnames(reaction_KPI) <- c('account_number', 'reaction_score_tmp')\n\nindex_ada <- reaction_KPI %>% \n  mutate(index_ada = case_when(\n    (account_number %in% city_2020_disclosure_US$account_number) & !(is.na(reaction_score_tmp)) ~ reaction_score_tmp,\n    (account_number %in% city_2020_disclosure_US$account_number) & (is.na(reaction_score_tmp)) ~ 0,\n    TRUE ~ NA_real_\n  )) %>% \n  select(-reaction_score_tmp)%>% \n    rename(Account.Number = account_number)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"ggplot(index_ada, aes(x = index_ada))+\n  geom_histogram()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"###4. Justice index - Poverty and Linguistic isolation\nSVI_raw <- read_csv(\"../input/cdp-unlocking-climate-solutions/Supplementary Data/CDC Social Vulnerability Index 2018/SVI2018_US_COUNTY.csv\")\n\nSVI <- SVI_raw %>%\n    select(STATE, COUNTY, EP_NOHSDP, EP_LIMENG, EP_POV)%>%\n    rename (State = STATE, County = COUNTY,  edu = EP_NOHSDP, ling = EP_LIMENG, pov = EP_POV)\n\ncity_county_full$State = toupper(city_county_full$state)\n\n#We select poverty, education attainment and linguistic isolation from SVI dataset\nsvi_sum <- city_county_full%>%\n    merge(SVI, by = c(\"State\",\"County\"), all.y = TRUE)%>%\n    group_by(Account.Number)%>%\n    summarise(\n        edu_test = mean(edu),\n        ling_test = mean(ling),\n        pov_test = mean(pov)\n             )%>%\n    mutate(index_jus_edu = case_when(\n        edu_test < 5 ~ 25,\n        edu_test >= 5 & edu_test <10 ~ 20,\n        edu_test >=10 & edu_test <15 ~ 15,\n        edu_test >=15  ~ 10),\n          index_jus_ling = case_when(\n          ling_test < 5 ~ 25,\n          ling_test >=5 & ling_test <10 ~ 20,\n          ling_test >=10 ~ 10),\n        index_jus_pov = case_when(\n        pov_test < 5 ~ 25,\n        pov_test >=5 & pov_test <10 ~ 20,\n        pov_test >=10 & pov_test <15 ~ 15,\n        pov_test >=15  ~ 10)\n        )\n          \n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"### Justice index - housing\nhousing <- read_csv(\"../input/comprehensive-housing-affordability-strategy/Table3.csv\")\n\nhousing_clean <- housing %>%\n    mutate(burden = T3_est4+T3_est10+T3_est16+T3_est22+T3_est28+\nT3_est34+\nT3_est40+\nT3_est47+\nT3_est53+\nT3_est59+\nT3_est65+\nT3_est71+\nT3_est77+\nT3_est83,\npercent = burden/T3_est1) %>%\n    select(name,T3_est1, burden, percent)%>%\n    separate(name, into = c(\"County\", \"State\"), sep = ' County')%>%\n    separate(State, into = c(\"x\",\"state\"),sep = ', ')%>%\n    select(-x)\n\nindex_jus_housing <- housing_clean%>%\n    merge(city_county_full, by = c(\"state\", \"County\"), all.y = TRUE)%>%\n    group_by(Account.Number)%>%\n    mutate(index_jus_housing_percent = mean(percent),\n          index_jus_housing = case_when(\n              index_jus_housing_percent <= 0.1 ~ 25,\n              index_jus_housing_percent <=0.15 & index_jus_housing_percent >0.1 ~ 20,\n              index_jus_housing_percent <=0.2 & index_jus_housing_percent >0.15 ~ 15,\n              index_jus_housing_percent <=0.3 & index_jus_housing_percent >0.2 ~ 10,\n              index_jus_housing_percent >0.3 ~ 5\n          ))%>%\n    select(Account.Number,index_jus_housing)%>%\n    distinct()\n#index_jus_housing\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"index_jus <- svi_sum %>%\n    merge(index_jus_housing, by = \"Account.Number\")%>%\n    mutate(index_jus = index_jus_edu+ index_jus_ling+index_jus_pov+ index_jus_housing)%>%\n    select(Account.Number, index_jus_edu, index_jus_ling, index_jus_pov, index_jus_housing, index_jus)%>%\n    distinct()\n\n#index_jus","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"ggplot(index_jus, aes (x = index_jus))+\ngeom_histogram()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"#### KPI Final\ncity_score <- index_rep %>%\n    merge(index_ada, by = \"Account.Number\", all = TRUE)%>%\n    merge(index_mit, by = \"Account.Number\")%>%\n    merge(index_jus, by = \"Account.Number\")%>%\n    merge(city_county_full, by = \"Account.Number\", all.y = TRUE)%>%\n    mutate(index = index_rep + index_mit + index_ada + index_jus)%>%\n    select(Account.Number, state, index_rep,index_mit,index_ada,index_jus, index,name)%>%\n    distinct()%>%\n    na.omit()\n\n#city_score\n#city_score[order(-city_score$index),]","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"### Text mining and analysis\n\n########################\n#### Sorting and cleaning corporate data\n\ncorp_disclosing <- corp_disclosing_raw %>% \n filter(country == \"United States of America\") %>% \n select(account_number, organization, primary_sector, primary_industry) %>% \n drop_na() %>% \n unique()\n\ncorp_risk_raw <- corp_answer_raw %>% \n filter(question_number == \"C2.3a\") %>% \n select(account_number, organization, column_number, column_name, row_number, response_value) %>% \n filter(column_name %in% c(\n \"C2.3a_C3Risk type & Primary climate-related risk driver\",\n \"C2.3a_C16Description of response and explanation of cost calculation\",\n \"C2.3a_C3Risk type & Primary climate-related risk driver_G\"\n )) %>% \n select(-column_number) %>% \n spread(column_name, response_value) %>% \n select(-row_number)\n\ncolnames(corp_risk_raw) <- c(\"account_number\", \"organization\", \"text_box\", \"risk_type\", \"risk_driver\")\n\ncorp_oppo_raw <- corp_answer_raw %>% \n filter(question_number == \"C2.4a\") %>% \n select(account_number, organization, column_number, column_name, row_number, response_value) %>% \n filter(column_name %in% c(\n \"C2.4a_C4Primary climate-related opportunity driver\",\n \"C2.4a_C3Opportunity type\",\n \"C2.4a_C6Company-specific description\"\n )) %>% \n select(-column_number) %>% \n spread(column_name, response_value) %>% \n select(-row_number) %>% \n drop_na()\n\ncolnames(corp_oppo_raw) <- c(\"account_number\", \"organization\", \"oppo_type\", \"oppo_driver\", \"text_box\")\n\ncorp_oppo_raw <- full_join(corp_oppo_raw, corp_disclosing) %>% \n drop_na()\n\n###############\n#### Plotting risk drivers for corporates\n\ncorp_risk_driver <- corp_risk_raw %>% \n select(account_number, organization, risk_driver) %>% \n drop_na() %>% \n unique()\n\ncorp_risk_driver <- full_join(corp_risk_driver, corp_disclosing) %>% \n select(-primary_sector) %>% \n drop_na()\n\ncorp_risk_driver_count <- corp_risk_driver %>% \n group_by(primary_industry, risk_driver) %>% \n select(-account_number, -organization) %>% \n summarise(driver_count = n())\n\ncorp_disclosing_count <- corp_disclosing %>% \n group_by(primary_industry) %>%\n summarise(industry_count = n())\n\ncorp_risk_driver_percentage <- full_join(corp_risk_driver_count, corp_disclosing_count) %>% \n mutate(percentage = driver_count/industry_count) %>% \n filter(primary_industry != \"International bodies\") %>% \n mutate(primary_industry = str_wrap(primary_industry, width = 20))\n\ncorp_risk_graph <- ggplot(corp_risk_driver_percentage, aes(x = risk_driver, y = percentage, color = risk_driver, fill = risk_driver)) +\n geom_col()+\n facet_wrap(~primary_industry)+\n theme_minimal()+\n theme(axis.ticks.x = element_blank(),\n axis.text.x = element_blank())+\n scale_color_manual(name = \"Risk Driver\", values = c(\"#FF0000\", \"#00A08A\", \"#F2AD00\", \"#F98400\", \"#5BBCD6\", \"#ECCBAE\", \"#046C9A\", \"#D69C4E\", \"#ABDDDE\", \"#000000\"))+\n scale_fill_manual(name = \"Risk Driver\",values = c(\"#FF0000\", \"#00A08A\", \"#F2AD00\", \"#F98400\", \"#5BBCD6\", \"#ECCBAE\", \"#046C9A\", \"#D69C4E\", \"#ABDDDE\", \"#000000\"))+\n scale_y_continuous(labels = percent)+\n xlab(\"Risk Driver\")+\n ylab(\"Percentage\")\n\ncorp_risk_graph\n\n#####################\n#### Plot opportunities for corporates\n\ncorp_oppo_driver_count <- corp_oppo_raw %>% \n select(account_number, primary_industry, oppo_type) %>% \n unique() %>% \n group_by(primary_industry, oppo_type) %>% \n summarise(driver_count = n())\n\ncorp_oppo_driver_percentage <- full_join(corp_oppo_driver_count, corp_disclosing_count) %>% \n mutate(percentage = driver_count/industry_count) %>% \n filter(primary_industry != \"International bodies\") %>% \n mutate(primary_industry = str_wrap(primary_industry, width = 20))\n\ncorp_oppo_graph <- ggplot(corp_oppo_driver_percentage, aes(x = oppo_type, y = percentage, color = oppo_type, fill = oppo_type)) +\n geom_col()+\n facet_wrap(~primary_industry)+\n theme_minimal()+\n theme(axis.title.x = element_blank(),\n axis.ticks.x = element_blank(),\n axis.text.x = element_blank())+\n scale_color_manual(name = \"Opportunity Type\", values = wes_palette(\"Cavalcanti1\"))+\n scale_fill_manual(name = \"Opportunity Type\", values = wes_palette(\"Cavalcanti1\"))+\n scale_y_continuous(labels = percent)\n\ncorp_oppo_graph\n\n#######################\n#### Sorting and cleaning city data\n\ncity_answer <- city_2020 %>% \n clean_names() %>% \n filter(country == \"United States of America\") %>% \n select(account_number, organization, question_number, column_name, row_number, response_answer)\n\n\ncity_risk_raw <- city_answer %>%\n filter(question_number == \"2.2\") %>% \n select(-question_number) %>% \n filter(column_name != \"Level of degree to which factor challenges/supports the adaptive capacity of your city\") %>% \n spread(column_name, response_answer)\n\ncolnames(city_risk_raw) <- c(\"account_number\", \"organization\", \"row_number\", \"factor\", \"indicator\", \"text_box\")\n\ncity_number <- city_answer %>% \n clean_names() %>% \n select(account_number) %>% \n unique()\n\n###########################\n#### Plotting supporting and challenging factors for cities\n\ncity_risk_support <- city_risk_raw %>% \n filter(indicator == \"Supports\") %>% \n filter(!(str_detect(factor, \"Other\"))) %>% \n select(account_number, factor) %>% \n unique() %>% \n count(factor) %>% \n mutate(total = length(city_number$account_number),\n percentage = n/total) %>% \n arrange(percentage) %>% \n slice_max(percentage, n = 9)\n\nrisk_support_graph <- ggplot(city_risk_support, aes(x = fct_reorder(factor, percentage), y = percentage, color = fct_reorder(factor, -percentage), fill = fct_reorder(factor, -percentage))) +\n geom_col()+\n theme_minimal()+\n theme(legend.position = \"none\")+\n ylab(\"Percentage\")+\n xlab(\"Top 9 Supporting Factors for Cities' Climate Adaptation\")+\n scale_y_continuous(labels = percent)+\n coord_flip()+\n scale_color_manual(values = c(\"#FF0000\", \"#00A08A\", \"#F2AD00\", \"#F98400\", \"#5BBCD6\", \"#ECCBAE\", \"#046C9A\", \"#D69C4E\", \"#ABDDDE\", \"#000000\"))+\n scale_fill_manual(values = c(\"#FF0000\", \"#00A08A\", \"#F2AD00\", \"#F98400\", \"#5BBCD6\", \"#ECCBAE\", \"#046C9A\", \"#D69C4E\", \"#ABDDDE\", \"#000000\"))\n\n\n\nrisk_support_graph\n\ncity_risk_challenge <- city_risk_raw %>% \n filter(indicator == \"Challenges\") %>% \n filter(!(str_detect(factor, \"Other\"))) %>% \n select(account_number, factor) %>% \n unique() %>% \n count(factor) %>% \n mutate(total = length(city_number$account_number),\n percentage = n/total) %>% \n arrange(percentage) %>% \n slice_max(percentage, n = 9)\n\nrisk_challenge_graph <- ggplot(city_risk_challenge, aes(x = fct_reorder(factor, percentage), y = percentage, color = fct_reorder(factor, -percentage), fill = fct_reorder(factor, -percentage))) +\n geom_col()+\n theme_minimal()+\n theme(legend.position = \"none\")+\n ylab(\"Percentage\")+\n xlab(\"Top 9 Challenging Factors for Cities' Climate Adaptation\")+\n scale_y_continuous(labels = percent)+\n coord_flip()+\n scale_color_manual(values = c(\"#FF0000\", \"#00A08A\", \"#F2AD00\", \"#F98400\", \"#5BBCD6\", \"#ECCBAE\", \"#046C9A\", \"#D69C4E\", \"#ABDDDE\", \"#000000\"))+\n scale_fill_manual(values = c(\"#FF0000\", \"#00A08A\", \"#F2AD00\", \"#F98400\", \"#5BBCD6\", \"#ECCBAE\", \"#046C9A\", \"#D69C4E\", \"#ABDDDE\", \"#000000\"))\n\nrisk_challenge_graph\n\n###########################\n#### Create city response dataframe with region information, and plotting a map with regional information.\n\naccNum_and_hazard <- question_30_tmp1 %>% \n  select(account_number, climate_hazard_highlevel) %>% \n  unique() %>% \n  full_join(city_disclosure_full) %>% \n  drop_na()\n\nregion_raw <- as.data.frame(spData::us_states) %>% \n  select(NAME, REGION) %>% \n  rename(state = NAME,\n         region = REGION)\naccNum_and_hazard <- accNum_and_hazard %>% \n  full_join(region_raw) %>% \n  full_join(us_states_clean) %>% \n  st_as_sf()\n  \n  \nregion_map <- ggplot(accNum_and_hazard)+\n  geom_sf(aes(fill = region))+\n  theme_minimal()+\n  scale_fill_manual(name = \"Region\", values = wes_palette(\"FantasticFox1\"))\n\nregion_map\n\n#################################\n#### Calculate the risk percentage in each region and plot\n\nregion_city_count <- as.data.frame(city_disclosure_full) %>% \n  full_join(region_raw) %>% \n  select(account_number, region) %>% \n  unique() %>% \n  group_by(region) %>% \n  summarise(total_city = n()) %>% \n  drop_na()\n\ncity_risk_percentage <- as.data.frame(accNum_and_hazard) %>% \n  select(climate_hazard_highlevel, account_number, region) %>% \n  group_by(climate_hazard_highlevel, region) %>% \n  summarise(region_total = n()) %>% \n  full_join(region_city_count) %>% \n  mutate(percentage = region_total/total_city) %>% \n  drop_na()\n\ncity_risk_graph <- ggplot(city_risk_percentage, aes(x = percentage, y = climate_hazard_highlevel, fill = climate_hazard_highlevel))+\n  geom_col()+\n  facet_wrap(~region)+\n  theme_minimal()+\n  xlab(\"Percentage\")+\n  ylab(\"Reported Risks\")+\n  theme(legend.position = \"none\")+\n  scale_fill_manual(values = c(\"#9986A5\", \"#79402E\", \"#CCBA72\", \"#0F0D0E\", \"#D9D0D3\", \"#8D8680\", \"#EAD3BF\", \"#AA9486\", \"#B6854D\", \"#39312F\", \"#1C1718\"))+\n  scale_x_continuous(labels = percent)\n\ncity_risk_graph\n#################################\n#### Filter out physical risks reported by corps\ncorp_risk_detail <- corp_risk_raw %>% \n  filter(risk_driver == \"Chronic physical\" | risk_driver == \"Acute physical\") %>% \n  select(account_number, risk_type) %>% \n  mutate(risk_type_clean = case_when(\n    str_detect(risk_type, \"Other, please specify:\") ~ \"Other\",\n    TRUE ~ risk_type\n  )) %>% \n  select(-risk_type) %>% \n  mutate(risk_type = case_when(\n    risk_type_clean == \"Changes in precipitation patterns and extreme variability in weather patterns\" ~ \"Changing weather patterns\",\n    risk_type_clean == \"Increased severity and frequency of extreme weather events such as cyclones and floods\" ~ \"Increased extreme events\",\n    risk_type_clean == \"Increased likelihood and severity of wildfires\" ~ \"Increased wild fires\",\n    TRUE ~ risk_type_clean\n  )) %>% \n  select(-risk_type_clean) %>% \n  unique()\n\n#################################\n#### Calculate percentage and plot\n\nrisk_percentage <- corp_risk_detail %>% \n full_join(corp_disclosing) %>% \n drop_na() %>% \n select(-organization, -primary_sector) %>% \n group_by(primary_industry, risk_type) %>% \n mutate(n = n()) %>% \n full_join(corp_disclosing_count) %>% \n select(-account_number) %>% \n unique() %>% \n mutate(percentage = n/industry_count) %>% \n filter(primary_industry != \"International bodies\") %>% \n mutate(primary_industry = str_wrap(primary_industry, width = 20))\n\nrisk_percentage_graph <- ggplot(risk_percentage, aes(x = percentage, y = risk_type, fill = risk_type))+\n geom_col()+\n facet_wrap(~primary_industry)+\n scale_fill_manual(values = wes_palette(\"IsleofDogs1\"))+\n theme_minimal()+\n theme(legend.position = \"none\")+\n scale_x_continuous(labels = percent)+\n xlab(\"Percentage\")+\n ylab(\"Reported Risk Type\")\n\nrisk_percentage_graph","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"#First, let's sort out and clean the corp list\ncorp_disclosing <- corp_disclosing_raw %>% \n  filter(country == \"United States of America\") %>% \n  select(account_number, organization, primary_sector, primary_industry) %>% \n  drop_na() %>% \n  unique()\n\n#Filter out all the textboxs.\n\ncorp_risk_raw <- corp_answer_raw %>% \n  select(account_number, organization, column_number, column_name, row_number, response_value) %>% \n  filter(column_name %in% c(\n    \"C12.1a_C7Impact of engagement, including measures of success\",\n    \"C12.1a_C8Comment\",\n    \"C12.1b_C6Please explain the rationale for selecting this group of customers and scope of engagement\",\n    \"C12.1d\",\n    \"C12.3a_C3Details of engagement\",\n    \"C12.3a_C4Proposed legislative solution\",\n    \"C2.2a_C2Please explain\",\n    \"C2.3a_C14Explanation of financial impact figure\",\n    \"C2.4a_C14Explanation of financial impact figure\",\n    \"C12.1b_C2Details of engagement\",\n    \"C12.1b_C7Impact of engagement, including measures of success\",\n    \"C2.3a_C16Description of response and explanation of cost calculation\",\n    \"C2.4a_C16Strategy to realize opportunity and explanation of cost calculation\",\n    \"C7.3c_C1Activity\",\n    \"C2.4a_C6Company-specific description\"\n  )) %>% \n  select(-column_number) %>% \n  select(response_value, account_number) %>% \n  unique()\n#Creating bigrams\ncorp_bigram <- corp_risk_raw %>% \n  drop_na() %>% \n  full_join(corp_disclosing) %>% \n  unnest_tokens(bigram, response_value, token = \"ngrams\", n = 2) %>% \n  separate(bigram, c(\"word1\", \"word2\"), sep = \" \") %>% \n  filter(!word1 %in% stop_words$word) %>%\n  filter(!word2 %in% stop_words$word) %>% \n  filter(str_detect(word1, \"^[a-zA-Z]+\")) %>% \n  filter(str_detect(word2, \"^[a-zA-Z]+\")) %>% \n  unite(bigram, word1, word2, sep = \" \")\n\n\n#Calculating tf_idf\ncorp_bigram_tf_idf <- corp_bigram %>% \n  count(primary_industry, bigram) %>% \n  bind_tf_idf(bigram, primary_industry, n) %>% \n  arrange(desc(tf_idf))\n\n#Plot tf_idf\ncorp_bigram_tf_idf %>% \n  group_by(primary_industry) %>% \n  filter(primary_industry != \"International bodies\") %>% \n  slice_max(tf_idf, n = 8) %>% \n  ggplot(aes(tf_idf, fct_reorder(bigram, tf_idf), fill = primary_industry)) +\n  geom_col(show.legend = FALSE) +\n  facet_wrap(~primary_industry, scales = \"free\") +\n  labs(x = \"tf-idf\", y = NULL)\n\n#Plot tf_idf for manufacturing and services as an example\n\ncorp_bigram_tf_idf_manufacturing_service <- corp_bigram_tf_idf %>% \n  group_by(primary_industry) %>% \n  filter(primary_industry == \"Manufacturing\" | primary_industry == \"Services\") %>% \n  slice_max(tf_idf, n = 20) %>% \n  ggplot(aes(tf_idf, fct_reorder(bigram, tf_idf), fill = primary_industry)) +\n  geom_col(show.legend = FALSE) +\n  facet_wrap(~primary_industry, scales = \"free\") +\n  labs(x = \"tf-idf\", y = NULL)+\n  theme_minimal()+\n  scale_fill_manual(values = wes_palette(\"FantasticFox1\"))\n  \n\ncorp_bigram_tf_idf_manufacturing_service\n\n#Plot a word network for services industry\n\nset.seed(0108)\n\na <- grid::arrow(type = \"closed\", length = unit(.15, \"inches\"))\n\nbigram_graph_services <- corp_bigram %>%\n  count(primary_industry, bigram) %>% \n  filter(primary_industry == \"Services\") %>% \n  separate(bigram, c(\"word1\", \"word2\"), sep = \" \") %>% \n  select(word1, word2, n) %>% \n  filter(n > 80) %>%\n  graph_from_data_frame() %>% \n  ggraph(layout = \"fr\") +\n  geom_edge_link(aes(edge_alpha = n), show.legend = FALSE,\n                 arrow = a, end_cap = circle(.07, 'inches')) +\n  geom_node_point(color = \"#FD6467\", size = 3) +\n  geom_node_text(aes(label = name), vjust = 1, hjust = 1) +\n  theme_void()\n\nbigram_graph_services\n\n#Plot a word network for manufacturing industry\n\nset.seed(1994)\n\na <- grid::arrow(type = \"closed\", length = unit(.15, \"inches\"))\n\nbigram_graph_manufacturing <- corp_bigram %>%\n  count(primary_industry, bigram) %>% \n  filter(primary_industry == \"Manufacturing\") %>% \n  separate(bigram, c(\"word1\", \"word2\"), sep = \" \") %>% \n  select(word1, word2, n) %>% \n  filter(n > 60) %>%\n  graph_from_data_frame() %>% \n  ggraph(layout = \"fr\") +\n  geom_edge_link(aes(edge_alpha = n), show.legend = FALSE,\n                 arrow = a, end_cap = circle(.07, 'inches')) +\n  geom_node_point(color = \"#F1BB7B\", size = 3) +\n  geom_node_text(aes(label = name), vjust = 1, hjust = 1) +\n  theme_void()\n\nbigram_graph_manufacturing","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Climate Vulnerability Screening Analysis and CDP Climate Action Index\n* [Introduction](#section-one)\n* [Data Sources and Purpose](#section-two)\n    - [Provided datasets](#Provided-datasets)\n    - [Additional datasets](#Additional-datasets)\n* [Methodology](#section-three)\n    - [KPI development](#KPI-development)\n        - [Reporting Index](#Reporting-Index)\n        - [Mitigation Index](#Mitigation-Index)\n        - [Adaptation Index](#Adaptation-Index)\n        - [Justice Index](#Justice-Index)\n    - [Performance-assessment](#Performance-assessment)\n    - [Collaboration Opportunity between Cities and Corporates](#Collaboration)\n* [Result and Discussion ](#section-four)\n    - [KPI](#kpi)\n    - [Collaboration Opportunity](#Opportunity)\n    - [Uncertainty](#unc)"},{"metadata":{},"cell_type":"markdown","source":"<a id=\"section-one\"></a>\n## Introduction\n\nClimate change has been an emerging issue for cities and corporations globally. Currently, cities account for 70% of global GHG emissions. CDP, as a global non-profit organization, houses the world’s largest, most comprehensive dataset on environmental actions, and wishes to apply the disclosure information to find the solutions to the most pressing problems related to climate change and social justice. This project aims to evaluate climate performance of cities reporting to CDP by developing a climate action key performance index (KPI). Only cities in the United States of America are included in this project since many existing datasets of the country can be utilized in the analysis. The analysis is based on the year of 2020.\n\nThis KPI will try to address the following questions. However, additional data analysis techniques that are not used to develop the KPI will also be included to better answer these questions. \n\n* How do you help cities adapt to a rapidly changing climate amidst a global pandemic, but do it in a way that is socially equitable?\n\n* What are the projects that can be invested in that will help pull cities out of a recession, mitigate climate issues, but not perpetuate racial/social inequities?\n\n* What are the practical and actionable points where city and corporate ambition join, i.e. where do cities have problems that corporations affected by those problems could solve, and vice versa?\n\n* How can we measure the intersection between environmental risks and social equity, as a contributor to resiliency?\n\n<a id=\"section-two\"></a>\n## Data Sources and Purpose\n<a id=\"Provided-datasets\"></a>\n### Provided datasets\n* 2020 Cities Disclosing\n* 2020 Cities Responses\n* 2020 Corporations Disclosing\n* 2020 Corporations Climate Change Responses\n\n<a id=\"Additional-datasets\"></a>\n### Additional datasets:\n* OpenFEMA Dataset: Disaster Declarations Summaries - v1 ([www.fema.gov/openfema-data-page/disaster-declarations-summaries-v1](http://)): the dataset is used to analyze the major disasters reported to Federal Emergency Management Agency (FEMA) on a state level during the past 40 years.\n* The contiguous United States data from the US Census Bureau with a few variables from American Community Survey (ACS) (spData::us_states): the polygon shape file for the US states; this dataset is included in the R package spData.\n* Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter): ([www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.html](https://)) the education, poverty, and linguistic isolation data is used to analyze the socio-economic status of cities. \n* Comprehensive Housing Affordability Strategy ([www.huduser.gov/portal/datasets/cp.html#2006-2017_data](https://)): this data is tabulated from the American Community Survey from the U.S, Census Bureau. This dataset demonstrates the extent of housing problems and housing needs, particularly for low-income households\n\n<a id=\"section-three\"></a>\n## Methodology\n\n<a id=\"KPI-development\"></a>\n### KPI development\nThe KPI model is based on multiple indicators. These indicators fall into four broad groups: reporting, mitigation, adaptation, and justice.\n* **Reporting index** is to evaluate whether/how cities respond to the most important questions in the CDP Climate Change Questionnaire\n* **Mitigation index** is to assess cities’ climate mitigation actions to reduce GHG emissions\n* **Adaptation index** is to evaluate whether cities are able to respond to local climate hazards and adopt effective actions accordingly.\n* **Justice index** reflects conditions that affect communities’ vulnerability and sensitivity to climate change.\n\nEach indicator is scored separately with available data from CDP Climate Change Questionnaire or external dataset. The final KPI score is calculated by adding up scores of four indexes.\n\n![CDP%20Climate%20Action%20Index.png](attachment:CDP%20Climate%20Action%20Index.png)\n**Figure 1.** Framework of Climate Action Index\n\n<a id=\"Reporting-Index\"></a>\n#### Reporting Index\nWe can not manage climate risk if we do not measure and report it. Climate and sustainability disclosure is essential to reveal the hidden challenges and develop effective management strategies. \n\nHere, we selected the three most important information disclosed by cities: \n* whether cities adopt climate targets in urban planning\n* whether cities quantify and report their Scope 1, 2 and 3 GHG emissions\n* whether cities conduct vulnerability assessment and report climate-related risks. \n\nClimate targets are critical for cities to identify the emission reduction pathway. Calculating and reporting Scope 1, 2 and 3 emissions reveals the status of cities’ climate impacts. Disclosing climate-related risks requires the city to conduct a climate vulnerability assessment and risk screening analysis. Therefore three metrics are also useful indicators to track cities efforts to combat climate change \n<a id=\"Mitigation-Index\"></a>\n#### Mitigation Index\nWe reviewed literature's recommendations on the cost-effectiveness of city climate action plans for GHG mitigation (source: https://doi.org/10.1016/j.scs.2018.06.023). Using the pre-developed multi-parameter scoring rubric, we divided cities' climate mitigation actions into Essential, Priority, and Additional actions based on response to Question C5.4 in City Climate Change Questionnaire. Generally speaking, mitigation policies which are highly effective in reducing GHG emissions and can promote city policies that provide greater co-benefits (cohesiveness). See table below for greater details. 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id=\"Adaptation-Index\"></a>\n\n#### Adaptation Index\nFEMA data is used to identify the major climate disasters reported during the past 40 years by region in the United States. According to question C3.0 in the City Climate Change Questionnaire, we matched the reported climate hazards with the FEMA data. If the city has taken active adaptation actions for region-specific disasters identified by FEMA, cities will get full scores for the adaptation index. If cities have not responded to the disasters identified by FEMA but have planned other adaptation activities, the adaptation score will be 20% off. \n\n<a id=\"Justice-Index\"></a>\n#### Justice Index\nSocial justice has a profound impact on the community. An increasing amount of literature provides evidence of the vulnerability of people of lower socioeconomic status to environmental hazards and climate change. Socioeconomic factors are selected based on literature reviews and data that provides quantitative measurements and available for counties in the U.S.\n\nThe affordability of housing has been reported as an important determinant of city well-being and climate adaptation. Comprehensive Housing Affordability Strategy (CHAS) program, supported by the US Department of Housing and Urban Development (HUD), identified areas with low-income households in the United States. CHAS also developed the HUD Area Median Family Income (HAMFI) indicator, which is the median family income calculated by HUD for each jurisdiction, in order to determine Fair Market Rents and income limits for HUD programs. Given the data granularity, we mapped the reporting organization to the county level and then calculated the percentage of the population with income that is less than 30% of HAMFI. The indicators considered the regional costs of living for both homeowners and renters, and utility costs. The cities are ranked and scored by the calculated percentage.\n\nLinguistic isolation is regarded as a significant barrier to community development. A high degree of linguistic isolation affects communication with service providers and daily activities. Additionally, ability to speak English is essential for obtaining health care and responding to emergent climate hazards. Literatures also indicate  that linguistic isolation is an indicator of a community's ability to participate in decision-making processes. In this analysis, the percentage of population speaking English less than “well”, from SVI, was used to represent the cities’ minority groups.\n\nPeople’s wealth conditions influence their living conditions, access to health care and ability of climate adaptation. Economically disadvantaged populations are more likely to experience disproportionate environmental burdens, and less likely to have the ability to prepare and respond to climate hazards, such as extreme weather events. In this analysis, we selected the SVI indicator, percentage of population below Federal poverty level, to evaluate cities’ poverty status.\n\nEducational attainment is another important element of socioeconomic status and a social determinant of climate resiliency. Numerous studies suggest education can have a protective effect from exposure to climate hazards that may damage health. In this analysis, we used the percentage of the adult population with no high school degree (from SVI) as an indicator. \n\n<a id=\"Collaboration\"></a>\n### Collaboration Opportunity between Cities and Corporates\nDirect data visualization and text mining are used to explore the potential collaboration opportunity between cities and corporates. \n\n<a id=\"Collaboration\"></a>\n#### Direct Data Visualization\nDue to the heterogeneousness of cities and industries, we think the best way to visualize risks and opportunities in categorized groups. We categorized US cities into four groups according to their region (northeast, midwest, south, and west) and categorized the corporates according to the twelve primary industries provided in the corporate disclosing dataset. “International bodies” is omitted due to low observation count.\n"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"region_map","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 2.** Regions of United States of America"},{"metadata":{},"cell_type":"markdown","source":"\n\nFor cities, we visualized (1) top 9 supporting factors identified by the cities for their climate adaptation plan and the corresponding percentages (i.e. what percentage of the cities report a factor as a supporting factor; Q2.2); (2) top 9 challenging factors identified by the cities for their climate adaptation plan and the corresponding percentages (i.e. what percentage of the cities report a factor as a challenging factor; Q2.2); (3) reported climate-related risks and their corresponding percentages in each region (i.e. in each region, what percentage of the cities report the risks; Q3.0).\n\nFor corporates, we visualized (1) general risk categories reported by the corporates and their corresponding percentage in each industry (i.e. what percentage of the corporates report a category as a risk; C2.3a); (2) general opportunity categories reported by the corporates and their corresponding percentage in each industry (i.e. what percentage of the corporates report a category as an opportunity; C2.4a); (3) specific physical risk types (the risks directly cause by Climate Change related events) and their corresponding percentages in each industry (i.e. what percentage of the corporates report the risk type as an opportunity; C2.4a). \n\n#### Text Mining\nText mining is mainly used for exploratory purposes in this project. Since the question prompts in the questionnaire would lead the cities and corporations to answer the questions in similar fashions, text mining cannot always successfully extract meaningful information. However, we were still provided with some insights.\n\nWe collected all the questions with a text field answer for all corporates and filtered out the text answer. Then we unnested the texts into bigrams and performed tf-idf analysis and words correlation analysis. Both methods are frequently used to identify the unique phrases only appearing in specific sectors. In this project, both methods will show what the most common but unique phrases used in different industries are.\n\n"},{"metadata":{},"cell_type":"markdown","source":"<a id=\"section-four\"></a>\n## Result and Discussion \n<a id=\"kpi\"></a>\n### KPI \n\nWe ranked cities based on Climate Action Index and below is the top 10 cities:"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"city_score <- city_score %>%\n    select(state, name, index)\n\nslice(city_score[order(-city_score$index),],1:10)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a id=\"Performance-assessment\"></a>\n#### Performance assessment\n\nWe haven’t found a well-developed climate change KPI for US cities. Two scoreboards we tried to calibrate with are the city clean energy scoreboard developed by American Council for an Energy-Efficient Economy (source: https://www.aceee.org/local-policy/city-scorecard) and a peer-reviewed literature targeting on evaluating cities climate action plans (only mitigation policies are addressed in this study; source: https://doi.org/10.1016/j.scs.2018.06.023). However, since both scoreboards only cover a specific aspect of the issue. It is not very meaningful to compare these studies.\n"},{"metadata":{},"cell_type":"markdown","source":"<a id=\"Opportunity\"></a>\n### Collaboration Opportunity\n#### Common Risks"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"city_risk_graph","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 3.** Climate-related risks reported by the cities in 2020"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"corp_risk_graph","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 4.** Climate-related risk categories reported by the corporates in 2020"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"risk_percentage_graph","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 5.** Climate-related physical risks reported by the corporates in 2020"},{"metadata":{},"cell_type":"markdown","source":"Regarding general risks, physical impacts from climate change related events and climate related regulations are two major concerns of industries. Additionally, by cross checking the physical risks reported by cities and corporates, cities will be able to identify the industries mostly likely facing similar risks.\nFor example, many cities in the west report risks in wildfire, extreme high temperature, sea level rise, and water scarcity. Meanwhile, such risks are also commonly reported by hospitality industries.\n\n#### Common Opportunities\n\n"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"corp_oppo_graph","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Figure 6. Climate-related opportunity categories reported by the corporates in 2020"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"risk_challenge_graph","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 7.** Top 9 challenging factors for climate adpation in cities"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"risk_support_graph","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 8.** Top 9 supporting factors for climate adpation in cities"},{"metadata":{},"cell_type":"markdown","source":"\nSimilarly, we can evaluate if the opportunities reported by the industries will benefit the cities’ climate adaptation initiatives. For example, resources efficiency is commonly reported by the corporates as a potential opportunity category. Meanwhile, resource availability is a challenging factor for cities’ adaptation plan, indicating the collaboration potential in this area.\n\n#### Text Mining Information\ntf-idf analysis can show the most unique words or phrases used by a particular industry. Here is an example for the services industry and the manufacturing industry. Naturally, corporate names appear frequently in this chart since they are mostly used by their corresponding industries. However, both industries also have phrases other than corporate names, such as graphic packaging, sealed air, and product energy for the manufacturing industry, and data center, sustainable finance, and disaster recovery for the services industry. \n"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"corp_bigram_tf_idf_manufacturing_service","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 9.** tf-idf anlaysis for manufacturing and services industry"},{"metadata":{},"cell_type":"markdown","source":"Similarly, word networks can show what word combinations appear the most in the text. The example shows the word networks for the services industry and the manufacturing industry. The unique links appearing in each graph show the particular interests of this industry."},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"bigram_graph_services","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 10.** word network for service industry**"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"bigram_graph_manufacturing","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**Figure 11.** word network for manufacturing industry"},{"metadata":{},"cell_type":"markdown","source":"#### Uncertainty\n<a id=\"unc\"></a>\nThere are different types of uncertainty that are likely to be introduced in this KPI model. The most important factor for uncertainty is the quality of self-reporting data. The data used in this KPI model heavily relied on reporting information, not actual implementation. All the information derived from the CDP questionnaire only reflects how cities responded and disclose climate-related risks and actions. But the performance of the action implementation is hard to measure from the questionnaire. It also means that the city may have a low score because they have historically implemented effective climate actions. Also due to lack of standardization, cities adopt different reporting boundaries and timeframe therefore the quantitative data are not comparable.\n"},{"metadata":{},"cell_type":"markdown","source":"## Appendix - KPI Results"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"city_score","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"3.6.3"}},"nbformat":4,"nbformat_minor":4}