Latest Contributions by PeterMerx

Hi Dan, Thanks for your fast response! Hereby a snap of the att table.... ... View more

Ok I will describe it most precise: I have a feature class from a downloaded .gdb consisting of polygons in the whole Netherlands. I clip it to two municipalities so I have two feature classes inthe Project.gdb . These two feature classes behave normal and I can switch symbology on all categories normally for example. Then i join a standalone table to one of these feature classes (standalone table: 50.000 records, feature class 100.000 records). The joining itself is done in a few seconds but if I then want to work with the feature class for example cahnge the symbology to another category it takes hours before this action is performed. A solution is to run the feature to feature class tool and then I get a new feature class and this one acts normal. The running of this feature class to feature class tool takes 2 hours. I think this has to do with the indexing, am I right? Would you know why it takes so long? ... View more

I'm sorry you're right...it's a feature class in a gdb (I add it from the .gdb to from Catalog). I'm joining it to a standalonetable.   ... View more

The thing is, i get the WARNING 000970 before joining! And if i run the Add Attribute Index tool, i get the warning again. After I run the feature class to feature class tool the performance of the shapefile is greatly enhanced, for example when selecting specific symbology.  The file is 100.000 records.... ... View more

Hi Xander, What I find the most hard in this case is creating the relationship between the impact of the village population size and the impact of the distance and (in my case) the price of agricultural parcels. I've been looking into some other studies and found that Reilly's gravitational function is often used. If I look at your name I suppose your Dutch so you can read this . If you CTRL+F for Reilly you will find the function. I used it to reprocess my results....And the results don't differ much from my own created function 🙂 ... View more

Hi Xander, Thank you for your response! My one is a little later due to a little holiday... But it's very interesting. I mainly did it in SPSS because I first exported to Excel to make a pivot table to get the averages from each field. The number of found nears per polygon vary so the only way I think I could do it is this way. In my research I investige the (potential) relation between the price of Agricultural Lots (the Green Polygons) and the nearbyness and number of inhabitants of Villages (the Orange polygons). I'm also putting in some other variables which theoretecally might cause variance.  I'm also using the "Agricultural Areas" division by the CBS ( CBS StatLine - Gebieden in Nederland 2013 ) to proxy for type of use and quality of the agricultural lots. So for each lot I have a (averaged) weight for the proximity to the nearest maximum of 5 villages within 3 km, with the number of inhabitants in account. By adding a dummy for "Agricultural Area" I can calculate interaction for them ( Weight x AgriculturalDummy ).  You're suggestion on using a sum of instead of an average is very interesting. I think it's reasonable to say that making a sum gives a better weight to nearness to a big city and that's what i'm trying to figure out.  Nice to see that we can help each other further in our calculations! I will head back in a few hours to post some results of both 🙂 ... View more

Hi Xander, Thank you for your post. Very good understandable and also very interesting to see how you approach my question. What I did was Generate a Near Table with a search radius of 3 km and a maximum of 5 features. Input is the Green Polygons and Near Features the Orange Polygons. As in your example my orange polygons also have a value which might have an impact so I exported it to SPSS and made a function (value/near_distance)/near_distance. I made it this way because I expect a positive relation between the values of green and the values of orange, and I expect that the closer they are to each other the higher the values of green will get. So I have now for each green polygon 1-5 values of the weight of the orange polygons. I then sum those values and divide them by the number of it. Then I have an averaged weight of the value x distance of the nearby orange polygons. Then I can use these values to perform a regression...You have comments or suggestions on this method?  @Xander Bakker: Is the approach you're using almost the same as creating a Heat Map with values as weight in ArcGIS Pro? ... View more

Hi Dan, Thanks for your response! I included a picture. I want to know if there's a relationship between the value of the green polygons and nearbyness of the yellow polygons. Is this better understandable? P ... View more