I began this final project by talking with Maria Salinas, the executive director at my field placement, Congress of Communities in Southwest Detroit. I asked her for ideas of issues that CoC is planning to act on in the next year and what types of data visualization would be useful for her to use in community meetings. Gentrification and land ownership in Detroit is a main issue in Detroit, so I looked at where tax foreclosures are taking place, who is buying them, and where the Detroit Land Bank is holding onto properties.

I am using a few different data sets for my final project. The data are Wayne County tax foreclosure auction results from 2017. The data was collected by Wayne County and was found at the City of Detroit’s data portal. 3,911 properties were purchased at auction.

From 2002-2013, there were 83,381 parcels that were foreclosed upon. The data were collected by the City of Detroit and disseminated by Data Driven Detroit. There are 70 foreclosed parcels that are listed in the dataset are from the Wayne County treasurer.

The land bank inventory data were provided by the Detroit Land Bank Authority. It represents 94,200 properties and was last updated December 5, 2018.

library(png)
library(grid)
img <- readPNG('C:/Users/Emily Cole/Documents/Congress of Communities/Foreclosures.png')
grid.raster(img)

The tax foreclosure map in QGIS shows where there has historically been less housing stability, which may predict stability for the future. While the data is only comprehensive for 2002-2013, it gives a good idea of how the city changed before and during the recession in Detroit. Southwest Detroit wasn’t affected much by tax foreclosures, but I would be interested to see more recent data to see tax foreclosures in the area.

img <- readPNG('C:/Users/Emily Cole/Documents/R/Final Dashboard.png') 
grid.raster(img)

In the map of Detroit Land Bank Inventory, it’s clear that the majority of the properties around downtown are vacant lots. This means that they are more likely to be developed in the short term because they don’t have to go through demolition, and the proximity to downtown is another predictor for development. There are a lot of vacant lots in Southwest Detroit mixed in with structures. The graph of tax foreclosure auction purchases is striking because it shows that a few individuals are purchasing large amounts of land in Detroit for low prices. Almost all of them live outside the city, and some are from out of state. Notably, S Hagerman and Stevey Hagerman are a father-son team that are notorious slum lords in Detroit. Stephen, the father, was sentenced to 60 days in jail for code violations in June, 2018. The other graph shows the concentration of properties bought by these individuals. I would be interested in zooming in even closer to see where purchases by these investors are located block-by-block. This information is vital for community organizations with residents who will be directly affected by new ownership in their neighborhoods.

The Congress of Communities youth council meets weekly and learns about different social justice issues. One that they want to learn more about is climate justice. I created these graphs for my own interest, but I will share them with the youth so that they can see perspectives about climate change, its effects and if there is potential to act on a state level around climate justice.

library(ggplot2)

library(dplyr)

library(viridis)

library(ggthemes)

load("C:\\Users\\Emily Cole\\Box Sync\\DS0001\\36368-0001-Data.rda")

da36368.0001 %>% select(STATE_IMPACTSFELT, DEMOG_STATE) %>%
  filter(!is.na(STATE_IMPACTSFELT)) %>% 
  filter(DEMOG_STATE %in% c("CA", "FL", "IL", "GA", "MI", "NC", "NY", "OH", "PA", "TX")) %>% 
  ggplot(aes(x = STATE_IMPACTSFELT, 
             fill = STATE_IMPACTSFELT)) + 
  geom_bar(stat = "count", position = "dodge") + facet_wrap(~DEMOG_STATE) +
  scale_fill_wsj(palette = "rgby", name = "My state has already felt \nthe negative impacts of global warming") + 
    theme_wsj(base_size = 8, color = 'gray', base_family = "sans", title_family = "mono") + 
  theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), legend.position = "bottom", 
legend.text = element_text(size = 7), legend.title = element_text(size = 6)) + 
  labs(title="The 10 Most Populous US States Have Begun \nto Feel the Effects of Climate Change")

The purpose of the study was to examine opinions on climate change, energy policy, and other environmental issues in the United States. It was conducted by Muhlenberg Institute of Public Opinion at Muhlenberg College and the Center for Local, State, and Urban Policy at the University of Michigan’s Gerald R. Ford School of Public Policy. The climate data were gathered by total of 603 adult respondents completed the Fall 2008 telephone survey, and a total of 911 adult respondents completed the Fall 2015 telephone survey.

barplot(table(da36368.0001$GOVT_NOSTATEACTION), main = 'Should states pick up the slack on climate action?', 
        sub = 'If the federal government fails to address global warming,\nits my states responsibility to act',
        col = viridis(n=4))

I had wanted to graph people’s responses to which level of government had most responsibility for climate action, but wasn’t able to figure it out, so I decided to graph people’s opinions of the state’s responsibilty for climate action if the federal government does not act. This is interesting because the current administration has dangerously backwards stances on climate and has attacked states that have acted independently to solve climate issues.