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3

gdalwarp -tr should deliver what you want. gdal_translate -outsize does basically the same thing, if both datasets share the same extent. In QGIS, Raster -> Projections -> Warpand Raster -> Conversions -> Translate call the same functions. For the first one, you have to edit the command line to get the -tr option. You might have to do ...


3

This error is caused because your raster is actually GeoTIFF file with .asc extension, which is unsupported by MaxEnt. You can check this in raster metadata (or by running gdalinfo from comamnd line, or in QGIS Information... from processing toolbox). Check the Driver: value. Cause: You converted your .shp with Raster -> Conversion -> Rasterize (...


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The worldclim data is in Geotiff format and Maxent uses ASCII files in ESRI .asc format. The conversion should be relatively simple. If you have access to ArcGIS, you can load the Geotiff and export it using the "Raster to ASCII" tool If you use QGIS then load the raster, go to Raster -> Conversion -> Translate -> as file type choose Arc/Info ASCII Grid (....


2

I suppose you did these 2 steps correctly: 1- calculate the statistics of the .asc file. 2- set the spatial reference of the .asc file. I don't think you have to import your .asc files into a geodatabase except you need to do special analysis that is not possible in ArcGIS with asc file. "when I do this to the original file and then import it will not ...


2

As per my comment above, ASC files have certain control characters that are corrupted if you open and save the file in an application like word. Try using notepad or notepad++ instead.


2

Maxent is supposed to predict probability of presence due to the co-variates you are testing (usually environmental variables). It is a presence only model and is not an abundance model so I really fail to see how the paper you mentioned use number of mosquitoes (the link does not work). One option is of course duplicating that presence point represented ...


2

on your screenshot, there is a problem with the cell size. The unit in mercator cylindrical is meter, therefore your should "convert the cell size from degree to meters. If your input cell size is 0.04166 degree, I suggest that you use 4000 m (at the equator, 1 degree ~= 110 km and it decreases towards the poles, here I take a rounded value with something ...


2

If you are wanting to extract the pixel values for a polygon boundary rather than the interior area, then you need to convert your polygons into polylines. You don't actually state what GIS system you are using so I shall assume you are using an Advanced licensed ArcGIS. Use the feature to line to convert your polygons to polylines, then convert this to a ...


2

I do not know where you picked up the idea that it is "advised to condenser vegetation categorical" but, this is not empirically justified. For nonparametric models, I always tell my students to derive a multiscale proportional representation of specific classes in categorical data that they have developed hypothesis around. This, on the whole, produces much ...


2

I used gdalwarp for exactly same purpose. From QGIS version 2.12 there is also built-in Raster alignment tool (Raster -> Raster alignment tool) - it can clip, resample and reproject rasters to match other raster. For more info see changelog here: http://qgis.org/en/site/forusers/visualchangelog212/#feature-raster-alignment-tool edit: To run MaxEnt all ...


1

The data in your original TIFF is stored as "Float64 - Sixty four bit floating point". This is a standard binary format for decimal numbers. The standard includes special binary numbers for encoding infinity, minus-infinity, and a special set of codes for "Not a Number". For example, if you divide 0 by 0 you get "Not a number" - here's R doing that: > 0/...


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I worked on the problem through the day and came up with the following solution. I hope it will come handy for everyone. # Creating the objects x <- stack() threshold.m <- vector("numeric", 2) auc.m1 <- vector("numeric", 2) l.T1 <- list() l.model <- list() run = 2 for(i in 1:run){ # Creating the k-fold data group.k1 ...


1

Here's a couple of function that get you 90% of the way. Its hacked out of response.plot: #' get the responses for variable v in a list of models #' use min and max for the range of the variable, this is #' fixed for each model #' #' for a categorical variable, it uses the levels instead #' Based on response.plot #' responses = function (mods, v, type, min,...


1

Not sure whether this is what you are looking for, but: if you use response() specifying a single variable, the function will plot and invisibly return the x, y, data. Is that what you are looking for? r <- response(mod, var="layer.1") r # recreate the plot: plot(r, type="l", ylim=c(0,1), col=2) If you want to look at the source code, check: getMethod(...


1

As a test to see whether it is faster you can run gdal_translate from the OSGeo4W shell using the instructions here: http://www.gdal.org/gdal_translate.html Essentially navigate to your directory within the shell and type: gdal_translate -of AAIGrid SourceDatasetName.tif OutputDatasetName.asc This should be much faster than using the translate tool ...


1

The issue here is that you are not using pseudo absences (but only true absences). This setting is not obtimal at all for MAXENT that is optimize design to spot te difference between presences and the rest (backgroud wich random pseudo-absences is from a computing point of view the same even if it is slightly diferent philosophically.. but I'll not go on ...


1

Have you tried posting this to the MaxEnt Google group? You might get a better answer there. Anyway, MaxEnt is for presence only data - I don't recall ever reading a paper where count data was used. What they probably did was something like put several points very close together in order to simulate count data at a single location. For example, if you were ...


1

General summary: You have to create raster from your vector layer (rasterizing) which must have the exactly same extent, cell size and coordinate system as your other environmental data. Than set this data as categorical variable in MaxEnt. QGIS solution: You have to create new attribute with intiger codes of categories. Use field calculator, if your ...


1

Interesting method to use; have you considered running the model with equal numbers of pseudo absences and then increasing or decreasing the proportion of known absences to pseudo absences to see if a trend appears in how it effects the model output? This approach may also give you insight into the uncertainty of the model as well.


1

The installation fails on Windows because r.maxent.lambdas is a shell script and not a Python script. The options are rewrite it using Python (perhaps contact the author?) use a non-Windows operating system like Linux


1

I created a polyline feature along southern extent of my raster layer, and then used the Euclidean Distance tool to create a raster of distances from that polyline. I'm going to use "distance from southern extent" as a Maxent predictor instead of the Northing.


1

To run MaxEnt all environmental data must have the exactly same extent, cell size and coordinate system. You can achieve this several ways (QGIS, GDAL, R, ...) look at Changing pixel size in 'asc' file using Qgis?. If you are familiar with gdal you can check the extent of .asc data with gdalinfo.


1

First, I would recommend to download the data (the 19 bioclim variables I suppose) in a more accessible format (in terms of ease using common software / packages). GeoTiff should be fine. You can get the files from either WorldClim or CHELSA. Note that the two sets differ as they are computed in different ways. Then you can open and manipulate the files in ...


1

If you have access to the ArcGIS Spatial Analyst extension, I would use the Region Group tool, then filter on value and count using the Con or SetNull and Lookup tools. You could chain them together in the Raster Calculator, using something like (completely untested...!): (assuming a value of 2 = "yellow" pixels and grey pixels are NoData/null) SetNull(("...


1

If you are using Feature to Raster from the toolbox to convert the MCP to raster, first add one of your ascii climate layers to the table of contents, then: click the "Environments" button at the bottom of the tool Set "Processing Extent" to be "same as layer " set "Processing Extent" snap raster to the ascii climate layer Set "Raster Analysis" cell size ...


1

If you were able to import your ascii file as an image, you already have a raster file. You should then check if the spatial reference is OK by adding another layer (e.g. open street map). If it is not well georeference, please provide the first lines of your ascii file otherwise there are too many possibilities for automatic or semi-automated georeferencing....


1

I think it is your Select tool which only selects grid_code= 1. The Select Tool selects only what you tell it to select and leaves everything else behind. Therefore with your Copy Feature you are only saving grid_code=1 but nothing else. # Process: Select arcpy.Select_analysis(polygon, polyselect, "\"grid_code\" = 1")


1

I'm not familiar with Diva but MaxEnt prefers ascii files to anything else. If you can export / save your rasters as .txt files then you can just change the filename to .asc afterwards and your environmental layers should be good to go with MaxEnt. In addition, double check all your projections. WGS84 is the way to go. If you have access to ArcGIS you ...


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