simonkassel

import os
import argparse

from gdal import Open
from gdalconst import GA_ReadOnly
from shutil import copyfile


def main(tif_directory, all_labels):
    tif_directory_contents = os.listdir(tif_directory)

simonkassel

[{"GEOID.msa":10180,"name":"Abilene, TX","year":2006,"category":"bus","total_expenses":1067426,"revenue_miles":433499,"revenue_hours":30754,"upt":506631,"fares":201795,"average_speed":14.0957,"avg_fare":0.3983,"farebox_recovery":0.189},{"GEOID.msa":10180,"name":"Abilene, TX","year":2006,"category":"other","total_expenses":1300467,"revenue_miles":618718,"revenue_hours":43670,"upt":110747,"fares":93789,"average_speed":14.168,"avg_fare":0.8469,"farebox_recovery":0.0721},{"GEOID.msa":10180,"name":"Abilene, TX","year":2007,"category":"bus","total_expenses":1042242,"revenue_miles":435929,"revenue_hours":30702,"upt":485752,"fares":217336,"average_speed":14.1987,"avg_fare":0.4474,"farebox_recovery":0.2085},{"GEOID.msa":10180,"name":"Abilene, TX","year":2007,"category":"other","total_expenses":1472836,"revenue_miles":631823,"revenue_hours":47605,"upt":110546,"fares":98383,"average_speed":13.2722,"avg_fare":0.89,"farebox_recovery":0.0668},{"GEOID.msa":10180,"name":"Abilene, TX","year":2008,"category":"bus","total_expen

simonkassel

simonkassel

var pts = [{"x":348,"y":673},{"x":431,"y":663},{"x":264,"y":604},{"x":373,"y":610},{"x":466,"y":607},{"x":148,"y":562},{"x":143,"y":525},{"x":227,"y":533},{"x":348,"y":570},{"x":552,"y":551},{"x":582,"y":553},{"x":65,"y":432},{"x":111,"y":425},{"x":231,"y":491},{"x":282,"y":477},{"x":346,"y":453},{"x":377,"y":411},{"x":440,"y":473},{"x":503,"y":492},{"x":522,"y":490},{"x":531,"y":446},{"x":563,"y":419},{"x":643,"y":399},{"x":698,"y":402},{"x":38,"y":384},{"x":99,"y":333},{"x":137,"y":342},{"x":214,"y":390},{"x":275,"y":325},{"x":425,"y":388},{"x":441,"y":358},{"x":495,"y":335},{"x":545,"y":316},{"x":634,"y":365},{"x":698,"y":370},{"x":2,"y":267},{"x":48,"y":235},{"x":78,"y":297},{"x":124,"y":293},{"x":197,"y":207},{"x":218,"y":297},{"x":236,"y":241},{"x":298,"y":271},{"x":345,"y":239},{"x":388,"y":287},{"x":420,"y":223},{"x":454,"y":213},{"x":585,"y":268},{"x":774,"y":284},{"x":289,"y":188},{"x":331,"y":145},{"x":442,"y":122},{"x":451,"y":183},{"x":517,"y":132},{"x":537,"y":155},{"x":570,"y":132},{"x":622,"y"

simonkassel

simonkassel

{
  "type": "FeatureCollection",
  "crs": {
    "type": "name",
    "properties": {
      "name": "urn:ogc:def:crs:OGC:1.3:CRS84"
    }
  },
  "features": [
    {

simonkassel

simonkassel

# First create a data frame of just  the year 2009, removing neighborhoods 
# that we gave an NA value for pctChange due to on insufficient sale volume
sf.2009 <- sf.summarized[which(sf.summarized$SaleYr == 2009), ] %>% na.omit()

# Then create the scatterplot using neighborhood name labels instead of points
change_scatterplot <- ggplot(sf.2009, aes(x = pctChange, y = medianPrice, label = Neighborhood)) + 
  geom_label(data = sf.2009[which(!sf.2009$pctChange %in% topPctChange),], 
             aes(label = Neighborhood), fill = "grey20", size = 2, color = "white") +
  geom_label(data = sf.2009[which(sf.2009$pctChange %in% topPctChange),], 
             aes(label = Neighborhood, fill = Neighborhood), size = 2, color = "white") +

simonkassel

# Create a locator map for these neighborhoods
sampleNeighborhoods <- ggmap(basemap) + 
  geom_polygon(data = neighb.tidy, aes(x = long, y = lat, group = group), 
               colour = NA, fill="black", alpha = 1) +
  geom_polygon(data = neighb.tidy[which(neighb.tidy$Neighborhood %in% sfForTimeSeries$Neighborhood), ], 
               aes(x = long, y = lat, group = group, fill = Neighborhood), 
               colour = "black") +
  coord_map() +
  scale_fill_manual(values=palette_9_colors)+
  mapTheme() + 

simonkassel

# First some data wrangling:

# We will create a list of the 8 highest percentages 
topPctChange <- unique(sf.summarized$pctChange) %>% sort(decreasing = TRUE) %>% head(8)

# Check out what those are:
topPctChange

# We'll use these percentages to subset our neighborhoods data frame
sfForTimeSeries <- sf.summarized[which(sf.summarized$pctChange %in% topPctChange), ]