I am working in QGIS to perform climate change risk mapping. However, I am having difficulties to understand the grid size of my DEM. I am trying to understand from the properties section of DEM layer in QGIS, but its confusing for me. I am also adding the screenshot from the properties section of my raster layer in below link:
Properties of my DEM layer
I obtained this layer big layer by combining 5 different DEM raster layers (since my study area is big). From the resource that I have downloaded DEMs (these are .hgt files), it was written that resolution of DEMs are 3 arc seconds 1x1. But if I'm not mistaken, properties say that my layer's grid size is 77.4254 x 77.4254 meters.
Can someone help me to understand DEM raster layer's grid sizes?
It is important because I will add several data layers on this raster to perform analyses. So I have to first understand the resolution of this raster layer to convert other data layers to the same grid size.
Related
In R, I am trying to create a choropleth map. I have built a database of businesses, some are part of chains (e.g. McDonalds) and others are independent. I want to calculate how many businesses are within 30km of each point on the map, but treat the different locations of chains as a single business.
For example, if you a point is:
5km from a McDonalds,
10km from Taco Bell
15km from Chick-Fil-A
20km from KFC
25km from McDonalds
35km from Five Guys
The colour will show that there are 4 fast food outlets within 30km.
I am happy to use any R package but I am mostly familiar with tmaps and ggplot2 maps.
At this stage the best approach I can think of is to create polygons for each chain and stack them as transparent layers of the same colour. I don't think this would be very efficient and wouldn't create a very nice looking choropleth.
The other answers I could find were either counting points (e.g https://gis.stackexchange.com/questions/229066/counting-how-many-times-a-point-is-inside-a-set-of-intersecting-polygons-in-r) or for GIS software.
EDIT:
I have managed to create a 30km radius from every location of every chain (using rgeos gIntersection). I now have a series of polygons.
To solve my question the additional thing I need to do is create polygons for where:
Only one polygon covers the area,
Two polygons covers the area,
etc.
To try to visual is this I used the answer from https://gis.stackexchange.com/questions/229066/counting-how-many-times-a-point-is-inside-a-set-of-intersecting-polygons-in-r
In the linked question they are trying to count how many polygons cover the numbered points (the image on the right). What I am trying to do is to create the image on the left, where there are polygons of no overlap (1), two overlapping polygons (2) and so on.
I think what you are trying to accomplish would be best approached using a raster approach rather than a chloropleth. To make a chorlopleth, you define a set of (generally irregular) polygons, summarize something within each polygon, then color the polygons based on the attributes. This would be a good approach if you wanted to say how many fast food resteraunts are within each state or county, or how many fast food joints per capita by state.
From your description, however, you are looking for how many fast food joints within a set radius for all points. This is more of a raster question, since you can represent your data on a regular grid.
The raster package is a good start for working with raster data and works well with the sf package.
You need to determine what density you need to accomplish your goal, then use this to determine the resolution of your raster. Once you've got that you can use raster::rasterize() to summarize your (I'm assuming) point data.
I'm assuming you have an object that has the locations of each restaurant, I'll call this object "points".
library(raster)
library(sf)
# create raster template with 30km resolution (I'm assuming your projection is in meters)
raster_template = raster((extent(points),
resolution = 30000,
crs = st_crs(points)
)
# rasterize your point data
r = rasterize(points, raster_template, fun = "count")
This should create a grid where each cell has the number of points within each 30km cell. You should then be able to plot the raster, but may want to either clip or mask it to just show parts that are within New Zealand
I'm having trouble with using QGIS.
I have a GRIB file containing meteorlogical data, this is loaded in QGIS as rasterdata if I'm not mistaken.
I also have a shape-file containing a polygon, describing regions within the area covered in the GRIB file.
What I need to know, is how many % of a raster-cell is covered by a region within the polygon. I'm using QGIS.
What I have done so far, is I rasterized the polygon, and used zonal statistics to calculate the SUM and COUNT on the shape-file, and then used the field calculator to calculate the percentage covered. This does not seem to provide the result I hoped for.
Can anyone push me in the right direction?
I have a point shapefile of Station IDs and stageheights. I would like to create a raster where each cell has the stage height value (in meters) of the closest in situ station to that cell.
I want this raster to match up with another raster. So I would like it if I could input both a raster I have created (dataset 3 described below) and my point shapefile (1).
Datasets:
1) Point Shapefile with stage heights of a river delta
2) Shapefile of the river delta extent
3) Raster of the delta where NA's represent land (could also have them be zero's if need be) and 1's are water. Two datasets 10 meter resolution and 30 meter resolution.
One conceptual issue I am having is with the amount of small streams I have.
For example (pictured in image below), station 1 (circled in blue) is technically closer to the black x region than station 2 (circled in red), but the stage height value in red is more representative of point x. There are NA's in between the two streams, does that mean that the value will not jump across streams?
How can I reassign the values in my Raster (all the 1's) to the stage height of the nearest station and make sure that these values are not jumping from stream to stream? Do I need to use least cost path? What is the best way to do this?
I would like to use R, but can use ArcMap if I must.
So I'm not sure what tools you have available to you but I think this answer may be useful:
Calculating attribute for network distance between multiple points in ArcGIS Desktop?
Here the questioner was looking to calculate distances on roads to some points, but your problem seems similar. I think the main point I would make here is that you should do your network distance classification prior to worrying about the raster layer. You may have to convert from polygon to lines or some workaround to get your data into a format that works, but this is the kind of job the tool is designed to do.
After you have reclassified your river shapefile based on their network distance to a given point, then convert the polygons to raster and use this to classify your original raster. You could do this in R or Arcmap. Arcmap will probably be faster.
I am pretty new to R, and have been attempting to use the mask function on a raster image of 250mx250m resolution. My problem is that for some reason I am getting overhang, as there are pixels which lie both inside and outside of the polygon. Is there a way to tighten the tolerance level of mask so that only the pixels within a certain percentage inside the polygon are accepted?
green is my polygon, blue is the resulting mask
I am guessing that you are using the rasterize function from the raster package.
The grid cells are rather large relative to the polygons you are using. rasterize uses the center of the cell to determine if it is covered. However, if you use argument getCover=TRUE you will get a value between 1 to 100 indicating the percentage of each cell that is covered. You could then use a threshold of your choice.
Source: Masking low quality raster with polygons in R gives weird overhang?
I have a high resolution layout of a floor (in a building) which I would like convert to a shapefile or polygons. The image is a JPEG, it outlines the walls of the rooms and the locations of beds in the on the floor. I would like to convert that JPEG image into a shapefile, or polygon, so that I can overlay a predictive model onto it.
I have done this with simpler shapes. For example, using the geom_polygon tutorial (http://docs.ggplot2.org/0.9.3.1/geom_polygon.html) so I know it can be done. But the layout is more complex and if I can I would like to overlay posterior probabilities of my predictive model overtop of the true ward layout. Right now, this involves me mapping out the co-ordinates of the wall intersections and create polygons that way (doable, but it takes a long time).
It would be ideal to convert the room layout into a shape file, and load that into R instead (once I have the shape file, I've converted it to a polygon to use in ggplot, so that isn't the issue).
Here is an example of what my layout data looks like
The thicker lines represent walls and the small rectangles are beds. What I want is to input this into R and that the points line up to a cartesian co-ordinate system, it would also be good to get the bed co-ordinates as well. I don't have access to ARCGis..or any geographers...