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20

I would go the complicated way: Two Tables in a 1:n relation one table with the point location of the graves another table with the Grave-ID and person data You can build a relation between the two tables so that selecting a grave will select all person records in the person-table. The idea of having tables with fields like Person1, Person2... is ...


11

Populated Places 1:10m (Natural Earth) Shapefile 7343 Cities Download from http://www.naturalearthdata.com/downloads/10m-cultural-vectors/ (Populated Places) Terms of Use http://www.naturalearthdata.com/about/terms-of-use/ No permission is needed to use Natural Earth. Crediting the authors is unnecessary. However, if you wish to cite the map ...


7

One approach is to convert this to raster and then extract contours Another is to find the buffer for each point;Dissolve those buffers to get a narrow polygon;Find the center line of each dissolved polygon.


6

The Geometry.within() method only accepts other Geometry objects -- in your case, it has to be a Polygon. It doesn't know what to do with a feature class, so I would suggest dissolving that feature class into one containing a single polygon, and then access the Geometry object with this: urban_area_geom = [r[0] for r in ...


5

You can manipulate individual point geometry using an Update Cursor. Accessing the SHAPE@XY token is by far the most efficient cursor-based approach. However, if you have a polygon of your area of interest, using a random point generator will likely be more efficient. In this example, I use a minimum and maximum threshold to set the limits for a random ...


4

I would create a polygon for the grave since the grave itself is a plot of land and have a one to many relationship for the people; one grave can have zero (unoccupied, available, or for sale ?) or many people. You could also use a point instead of the polygon. Polygons would make better presentations for sales and maintenance.


4

I think this is a great question and one with more relevance as we start using different technologies to store spatial data such as PostGIS. There are two issues involved here, from what I can think about: 1) Scale: I used to teach cartography students that a City from the view of "The Country" is a point, but as you zoom in to the scale of a "region", ...


3

Buffer (without dissolving) all lines a (very) small distance. Spatially Join your points to your buffers with a one-to-one join_operation with an intersect match_option. In the resulting feature class, there will be an attribute Join_Count of how many buffers matched each point. Any point at an intersection would intersect two buffers, while points ...


3

I would take DenaliHardtail's suggestion of using polygons to represent accurate sizes of the plots. This layer could have a table with Grave_ID, Grave_Type, Grave_Capacity, and Grave_Occupancy_Number. Then you could have a point layer with points overlying the corresponding grave polygons. Columns for the point layer table could be Person-ID, First_Name, ...


2

Normalizing the data leads me to some missing ideas/points. Also, I think Excel can do everything you want for the "database" you contemplate. Hint: Use sheets, or multiple files and use variations of Lookup functions. Save into the useful file(s) for imports/lookups from QGIS I envision these discrete tables [or excel sheets], to start off your data ...


2

Are there any parameters on the cities you want? GeoNames might be a good resource. Under their tools page there is a GeoNames csv file to shapefile converter. It might take a little bit of work and sifting through the data to get exactly what you want.


2

Must it be a shapefile? Google Maps offer something like you asked as a kml file (kmz in fact, which is just a zip containing a kml), at https://www.google.com/maps/d/viewer?mid=zMlf_4RO8x7E.kllT447wz1Ws&msa=0&ie=UTF8&t=m&ll=8.407168,165.9375&spn=130.849955,280.898438&z=2&source=embed


2

This is a bit more complex, you will need to create two temporary 'unselect' feature classes. Use the tool Feature Vertices to Points with the 'BOTH_ENDS' specified to create points where you don't want selection. Do this for the blue and the red lines then merge. Use Select by Location to select the points within a small tolerance from the blue lines (how ...


1

If your data density is so low that it is non-contiguous, the obvious alternative, which is frequency hotspot analysis using raster surfaces, won't be so good either. Also raster surfaces are not good near coastlines due to weighting issues (some cells have large chunks of sea, then you have to fiddle with weighted corrections - which requires dense ...


1

First you need a line identifier. A buffer will probably do what you need, with dissolve. Then you can spatially join the buffer polys back to the points and transfer the buffer fid across. Then you need to sequence them in some way. The points all seem to go NE-SW, so perhaps ordering on the Y coord will get you a sequence. Apply this as an index to the ...


1

I will calculate the intersection of two paths, one starting at Point A using the bearing from Point A to Point B, and the other path starting at 89.99999N, 179.99999E bearing 180 degrees. That will give me the approximate point on the 180.



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