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seen Dec 3 at 16:57

Apr
13
comment R packages for deriving spatial clusters from origin-destination matrix
Hi Andy, sorry, I didn't mean to imply that good looking visualizations weren't useful. As I understand the Intermax approach, it is a way of defining regions based on flows / connectivity between a large number of nodes. As such, it produces clusters of functionally connected nodes. In any case, I'll post back with my solution. Thanks for your feedback.
Apr
12
comment R packages for deriving spatial clusters from origin-destination matrix
Thanks for these links. I was interested in the second approach, which I believe is an implementation of the Intermax approach. I believe that Bostock (D3) was involved in the creation of Protovis, which had flow map creation. However, I'm less interested in a pretty visualization and more interested in being able to derive lists of node regions. The context is deploying resources to manage and balance assets within each region, if that helps.
Feb
18
comment How to implement multivariate hex bins?
Fantastic. Worked like a charm.
Feb
18
comment How to implement multivariate hex bins?
@underdark I used hex polygons that I generated using Michael Minn's plugin and then generated points at the centroid of the polygons. Those are the points that I'm trying to style. But I have access to both polygons and points which have the necessary attributes.
Feb
18
comment How to implement multivariate hex bins?
Aaron, I haven't tried this in R yet. I took a look at hexbin and didn't see any easy way to implement a bivariate mapping. But the other reason why I didn't go the R route is that ultimately I'm designing maps with this data and it seemed easier to stick to QGIS/ArcGIS.
Feb
18
comment How to implement multivariate hex bins?
Jake, thanks for this solution. I'll give it a shot a bit later and report back. As for applying a color ramp to SVGs, it's tricky and had to be done manually. What I did was modify the svg file. Within the polygon tag, I inserted the following: fill="param(fill) #ff0000" stroke="param(outline) #00ff00" stroke-width="param(stroke-width) 10" Then, I used ColorBrewer to generate a color scheme which I applied to the classified data manually. Not the easiest approach and I think your proposal will work better. Thanks.
Feb
18
comment Points in Polygon Count: Error with arcpy.selectLayerByLocation_management
This approach yielded the same error. But I'll implement the function-based approach that you linked to. Thanks.
Feb
17
comment Points in Polygon Count: Error with arcpy.selectLayerByLocation_management
Sorry, I changed some of the names from the original code. The errors aren't related to this. Thanks for pointing this out.
Dec
24
comment ArcGIS Kernel Density with polyline, search radius / bandwidth calculation
In the study area, rivers are important means of transport. So areas with higher river density are expected to see more of certain types of activity and visa versa. Given this, any thoughts on determining an appropriate search radius? Thanks.
Dec
24
comment ArcGIS Kernel Density with polyline, search radius / bandwidth calculation
Ah! I see what you mean. The points in this case represent towns. It would be ideal to have some value derived from other research that would help determine a meaningful distance. But that data is not available. Thanks for clarifying. I have gone back to client to get clarification and I'll post back when I know more.
Dec
24
comment ArcGIS Kernel Density with polyline, search radius / bandwidth calculation
Fair enough. However, the question I asked had to do with issues determining a search radius while using polyline features. If the number of features is a factor in determining the radius, what is the best way to handle lines where segment lengths differ (sometimes significantly so)?
Dec
22
comment ArcGIS Kernel Density with polyline, search radius / bandwidth calculation
To be honest, I'm not entirely sure how they will be used. I'm doing this work under contract and only have a few details about the project. My understanding is that the intent to create a measure of how "in the creeks" each point of interest is in. My assumption is that the extracted value will ultimately be used in a regression model, but I don't know specifics. Sorry, not a very satisfying or useful answer but I've only been given limited information at this point.
Dec
22
comment ArcGIS Kernel Density with polyline, search radius / bandwidth calculation
Sure. The polyines represent river features. There is a separate layer of points of interest. The intent of the density analysis is to create a raster file on which the points of interest file will be overlaid. The raster value at each point of interest will then be extracted using the Extract Values to Point tool. Thanks.