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All the layers spatial references are the same. Thank you for the suggestion though! Sometimes it is the small things lol.
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03-12-2017
01:05 PM
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I tried saving it to the default geodatabase area and it would not even start the spatial join. It was an immediate error.
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03-09-2017
10:44 PM
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jayanta.poddar the only option when filling out the output feature class is a .shp or how would I change that?
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03-09-2017
10:38 PM
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jayanta.poddar I will definitely give this a try and get back to you on the result. Thanks!
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03-09-2017
10:23 PM
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I am trying to join my data points to the map base shapefile through a spatial join. It is a very large amount of data and all appears to be going fine until the very end when it is creating the new output file. It just gives me the every generic error. Any suggestions as to what may be the problem and how to resolve? The peach line should look like the purple map. Thanks!
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03-09-2017
09:29 PM
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Yeah that is what I was leaning towards as well. I just have to find a way to defend it as Inverse can also be defended depending on perspective. Haven't been able to find published articles on using this type of analysis for this type of data either. Ahh got to love thesis work!
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01-24-2017
09:37 PM
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Yeah that is what I was thinking too. So do you think that the Zone of indifference would be most acceptable choice if set at the parameters of the city? Or would you go inverse distance?
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01-24-2017
09:02 PM
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Unfortunately due to the way the data was collected we can't tell which stops the riders got on at so we have had to sum the riders to each neighborhood polygon. I am not sure how else to put the data together to be honest and still be able to see how the neighborhoods ridership cluster. I can input each rider at a lat/long but then each point represents a rider which then only has a value of 1 which does not work for the analysis. I believe I am getting confused because I keep thinking that distance does not really affect where the rider gets on it hence the setting of the Zone to cover the city. But then at the same time it could affect hence the inverse distance. So trying to see which works. Or I was trying to figure out how to create a spatial matrix file. But same problem with choosing that as well. What I am trying to do is figure out where our highest significantly different rider usage is at a neighborhood level. Which is proving difficult with that data that was made available to us.
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01-24-2017
08:38 PM
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I have ridership data for transit that I am trying to hotspot to see if there are significant spatial clusters of riders in certain neighbourhoods. The data unfortunately is not tied to bus stops so I have had to sum rides for each neighbourhood and add it to the shapefile that way. I am now trying to determine which conceptualization to use. Based off of the descriptions I believe I have it narrowed down to Zone of indifference or Inverse distance. Any thoughts as to how you would go about hot spotting this data or choosing the proper Conceptualization? I have run them all as well to get a feel for the data but it has just made it more confusing. Thank you for your thoughts!
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01-24-2017
06:37 PM
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Thanks! Some of the info was missing on the page page but I think I figured it out.
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01-15-2017
08:25 PM
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I am new to using the Generate Spatial Weights Matrix tool for a hotspot analysis I am working on for my thesis. It says it successfully generated. however when I click on the output it says can not open file. Is this normal and if so how do you see the weights that were made to make sure it is correct? Thanks!
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01-15-2017
07:36 PM
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Thank you! I had not found that page. Still not sure if I am choosing the right option but at least I have references for it now. Cheers!
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01-15-2017
07:16 PM
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Hey Community! I am trying to figure out the proper methodology to figure out how to choose the best Threshold Distance for my analysis of people across a small city for the Spatial Autocorrelation Moran's I tool. I have found several different suggested ways but they are not consistent and not Arc specific. Any suggestions? Thank you!
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01-15-2017
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