Mike Treglia mtreg

## postgis_raster_polygon_issue.txt
Related to GIS-StackExchange thread here: https://gis.stackexchange.com/questions/294482/error-quantifying-land-cover-classes-by-polygon-in-postgis?noredirect=1#comment473338_294482

## nyc_ortho_imagery_download.R
# Based on helpful example here: http://felixfan.github.io/download-files/
# Regular NY Download page here: http://gis.ny.gov/gateway/mg/2018/new_york_city/18ic_c_new_york_city_l06_4bd.htm

url = "ftp://ftp.gis.ny.gov/ortho/nysdop9/new_york_city/spcs/tiles/"
filenames = getURL(url, ftp.use.epsv = FALSE, dirlistonly = TRUE)
filenames <- strsplit(filenames, "\r\n")
filenames = unlist(filenames)
filenames

for (filename in filenames) {

## .block
license: mit

## plotly_sunburst_challenge.R
library(plotly)

# Create some fake data - say ownership and land use data with acreage
df <- data.frame(ownership=c(rep("private", 3), rep("public",3),rep("mixed", 3)),
                 landuse=c(rep(c("residential", "recreation", "commercial"),3)),
                 acres=c(108,143,102, 300,320,500, 37,58,90))

# Just try some quick pie charts of acreage by landuse and ownership
plot_ly(data=df, labels= ~landuse, values= ~acres, type='pie')
plot_ly(data=df, labels= ~ownership, values= ~acres, type='pie')

## gdbs_compile_layers.R
############################################################
#' Compile layers from different gdb files with same, specified prefix, and desired coordinate system
#'
#' Compiles layers with standard prefix from multiple gdb files (specified by user) into single sf object.
#' This was originally developed to get respective layers from separate geodatabases that resulted from running the ACPF toolbox
#' into single layers (e.g., all stream reach datasets into one layer, rather than spread across many gdb files) but may have broader applicability.
#' (ACPF stands for Agricultural Conservaton Planning Framework; see https://acpf4watersheds.org/toolbox/)
#'
#' The handling of different projections is currently fairly crude - it just automatically reprojects files to the desired projection
#' (even if already in that projection), though that step is generally fairly quick.

## gpkg_lyr_from_ziparchive_zenodo.R
# Example of loading data from gpkg in zipped folder on Zenodo into R
# Data from the repository 'The State of the Urban Forest in New York City - Supplemental Datasets'
# at https://zenodo.org/record/5210261

# Load sf package
library(sf)

# URL to Files on Zenodo
vurl <- "/vsizip//vsicurl/https://zenodo.org/record/5210261/files/canopy_streettree_summaries.zip/canopystreettree_supp.gpkg"

## test.md

      
        
          
            
              
              1 file
            
          
          
            
              
              0 forks
            
          
          
            
              
              0 comments
            
          
          
            
              
              0 stars
            
          
        
        
          
              
          
          
            
                mtreg
                / test.md
            
            
              Last active
              September 26, 2023 19:14
            
              
                test for embedding webmap
	# Based on helpful example here: http://felixfan.github.io/download-files/
	# Regular NY Download page here: http://gis.ny.gov/gateway/mg/2018/new_york_city/18ic_c_new_york_city_l06_4bd.htm

	url = "ftp://ftp.gis.ny.gov/ortho/nysdop9/new_york_city/spcs/tiles/"
	filenames = getURL(url, ftp.use.epsv = FALSE, dirlistonly = TRUE)
	filenames <- strsplit(filenames, "\r\n")
	filenames = unlist(filenames)
	filenames

	for (filename in filenames) {
	library(plotly)

	# Create some fake data - say ownership and land use data with acreage
	df <- data.frame(ownership=c(rep("private", 3), rep("public",3),rep("mixed", 3)),
	landuse=c(rep(c("residential", "recreation", "commercial"),3)),
	acres=c(108,143,102, 300,320,500, 37,58,90))

	# Just try some quick pie charts of acreage by landuse and ownership
	plot_ly(data=df, labels= ~landuse, values= ~acres, type='pie')
	plot_ly(data=df, labels= ~ownership, values= ~acres, type='pie')
	############################################################
	#' Compile layers from different gdb files with same, specified prefix, and desired coordinate system
	#'
	#' Compiles layers with standard prefix from multiple gdb files (specified by user) into single sf object.
	#' This was originally developed to get respective layers from separate geodatabases that resulted from running the ACPF toolbox
	#' into single layers (e.g., all stream reach datasets into one layer, rather than spread across many gdb files) but may have broader applicability.
	#' (ACPF stands for Agricultural Conservaton Planning Framework; see https://acpf4watersheds.org/toolbox/)
	#'
	#' The handling of different projections is currently fairly crude - it just automatically reprojects files to the desired projection
	#' (even if already in that projection), though that step is generally fairly quick.
	# Example of loading data from gpkg in zipped folder on Zenodo into R
	# Data from the repository 'The State of the Urban Forest in New York City - Supplemental Datasets'
	# at https://zenodo.org/record/5210261

	# Load sf package
	library(sf)

	# URL to Files on Zenodo
	vurl <- "/vsizip//vsicurl/https://zenodo.org/record/5210261/files/canopy_streettree_summaries.zip/canopystreettree_supp.gpkg"