Hi everyone,
I hope I will be able to express comprehensively what I am trying to do. So, I have a raster that shows different areas of habitat suitability for a species - highly suitable, suitable, marginally suitable and unsuitable. I would now like to use the Cost Distance tool to analyse habitat connectivity. This tool needs a source input - this will be my highly suitable habitat - and it needs a resistance layer input. This will be a reverse of my suitability map with the highest resistance values for the unsuitable habitat and the lowest for he highly suitable habitat. So far so good.
What I would now like to do is this: I would like to reclassify the unsuitable habitat around cities and villages with a higher resistance value than all the other unsuitable habitat. But I don't know how to do this. I don't think the Reclassify tool has an option for that? I have been thinking about creating an extra polygon feature layer for these areas - but how would I be able to merge this with my raster? Or is there another, smarter way? My knowledge of ArcGIS tools is not sufficient to come up with a good idea, I am afraid ...
Thanks very much and kind regards,
Carina
You can use the reclassify tool once your data are in raster format. Simply assign a numeric 'weight' to the classes
ie urban 1000 non-urban 100 which would mean that the cost of is 10 times worse than non-urban in terms of suitability.
think of the numeric values as either a attractiveness or deterrant, depending upon your perspective. You can make the ultimate 'not suitable at all' by classing a particular class to nodata. Have a read through the cost tools again in those terms.
Hi thanks, but my problem is this: In my raster I have the four classes of suitability. The urban areas fall under "unsuitable". However, areas of very high elevation or steep slope etc. are "unsuitable" as well. But now I would like to assign a different resistance value to the urban areas than to the other unsuitable areas. Do you know what I mean? 😕
there is unsuitabe, then unsuitable... there may be an intrinsic pecking order that you are going to have to decide upon. This is why doing 'cost' analysis can be somewhat subjective since the modeller has to present a justitication based upon criteria for how they rated their model. For example, if your intent was to run a pipeline through the landscape, both urban and high elevation may both indeed be 'unsuitable'... but realistically, there are the tangeables (ie cost) and realities (public perception).... soooo to go out on a limb, I would rate the 'cost' of going through an urban landscape some factor much higher than through areas of high terraine/slope .... the pipeline peeps would obviously perceive this as being more expensive in $$$ terms since they could tap into existing pipelines and/or use existing right-of-ways.
The person putting forth a model, simply has to state their criteria and their instrinsic order and/or 'value' in terms of the alternatives. The model spits out a result. Your task then is to examine the sensitivity of the model to the variations in the input parameters and their value. In short.... you can't be wrong... only less right than someone else's perception. So GIS isn't a panacea, it is controlled by people's perceptions and backgrounds. good luck
Carina, I would probably try what you were thinking re: creating a polygon feature class around the urban/populated areas, whether you do this with a buffer (probably not best) or is you have some other way to designate these areas. depending on the size of you study area, and the population, maybe you can take the roads and buffer those, or take the footprints of the buildings and buffer those. You could assign these polygons a value/weight and then use Polygon to Raster—Help | ArcGIS for Desktop to convert them to a raster for additional analysis.
Depending on the species, paraphrasing what Dan mentioned, suitability even in urban areas will vary, depending on the species. Some species, like moose, which my guess would not normally show as compatible in urban area, actually can thrive (urban like in Alaska, not NYC, but there would other factors there too, of course). I am not a biologist, and no expert on habitat analysis (although we have other staff that are working on that more these days) but have worked with them long enough to pick a few things.
i think the first step, again as Dan was getting at, is to get all your layers/variables/weights figured out, and in matching rasters, then start working with the raster/array math. Remember to keep a copy of the original sets since some steps might overwrite v's making a new raster.
also, although I have note searched, there may be samples of models already posted on the Internet for what you are analyzing. Might be worth doing a few browser searches, unless you intent is to come up with a new method. Don't reinvent the wheel is you don't need to.
What you could do (and what I have done for similar problems) is to use the urban areas (if you have those isolated) and calculate the distance from urban areas using the Euclidean Distance—Help | ArcGIS for Desktop tool. The result will be a raster indicating 0 for urban areas and the euclidean distance to the nearest urban area for all other pixels.
Using the Raster Calculator—Help | ArcGIS for Desktop you can translate those distance values to a factor that will increase suitability values: suitability will decrease when distances are low ("cost value" will increase). The reason I apply this method is that the influence of the distance to urban areas is not a "black and white" think, rather a scale of grey, where the closer you are to the urban area the influence is largest and at some point the influence stops.
Let's assume we have a suitability cost raster with values of 1, 4, 7 and 10 (1 being most suitable and 10 least suitable) and the distance to urban areas has an influence of 1.5 (cost multiplier) nearest to the urban areas and 1 (no influence) at a distance of 1000 meter (euclidean distance). You could perform the following calculation:
cost2_raster = Con(dist_raster <= 1000, ((1 - (dist_raster / 1000)) * 0.5 + 1) * cost_raster1, cost1_raster)
Where:
What this does is this:
Hi, thanks very much for all these answers! In the end I used the Cell Statistics tool with the MAXIMUM option.