Spatial Data: DNR Open Data

Data Category: Open data pertaining to Michigan DNR land including ownership, recreation areas, compartment boundaries. I will focus on the Forestry Data sets, and particuarly the Forest Health Points dataset

Data Description: This data set highlights forest areas that are impacted by forest pests and pathogens across Michigan state forests.

Data Link to All Data Sets: http://gis.midnr.opendata.arcgis.com/ Data Link to Forest Health Data Set:http://gis.midnr.opendata.arcgis.com/datasets/baa14b73ba2f4d39b575713db8b42bb9_8 Here is the logo! []http://www.michigan.gov/images/dnr/dnr_396072_7.png

Data Use Policy: http://www.michigan.gov/som/0,4669,7-192-29938-281460--,00.html

Use with R: Data sets can be pretty large. I recommend downloading the shapefiles to your hard drive and then load them into R.

library(rgdal)
library(raster)
map<-readOGR(dsn="C:/0_Minnix/Graduate Stuff/FOR870/Lab7", layer="Forest_Health_Points")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:/0_Minnix/Graduate Stuff/FOR870/Lab7", layer: "Forest_Health_Points"
## with 2134 features
## It has 32 fields

plot(map)

#There is an extreme outlier in this dataset. When opening the dbh file in Excel, in the Long column, OBJECTID 
good_data<-subset(map, FH_ID!="FH63201400002")
plot(good_data)

#Yay! It's better. Now let's add a state boundary
statesShpFile <- "https://prd-tnm.s3.amazonaws.com/StagedProducts/Small-scale/data/Boundaries/statesp020_nt00032.tar.gz"
download.file(statesShpFile, destfile = "temp1.tar.gz")
untar("temp1.tar.gz")
states <- readOGR(".","statesp020")

## OGR data source with driver: ESRI Shapefile 
## Source: ".", layer: "statesp020"
## with 2895 features
## It has 9 fields

MI <- subset(states, STATE == "Michigan")
plot(MI)
points(good_data)