3

Not sure if it was your case, but I found out -I should not be used together with appending data -a. I was importing many TIF files into a DB, and -I actually creates the index again and performs analyse on the table for each file, which takes 10x more time. -I should only be used when creating the table, with -p option.


3

I think the problem here is that you're passing directly the attribute ($yield_ls) to the ramp expression, and the results are sorted by number of occurrences of each category. In order to classify the content you're receiving, you've to use the buckets as follows: color: ramp(buckets($yield_ls, [ "Fractured 0.0 - 0.1 l/s", "Fractured 0.1 - 0.5 l/s"...


2

Let's reproduce your sample data: charters=data.frame( school.name=c("oceanside","discovery"), school.id=c("008229","228881"), district.id=c("3400011","340020"), lat=c(39.3635, 40.7343), lon=c(-74.4350,-74.1745)) then make it a spatial points data frame: coordinates(charters)=~lon+lat assume the lat-long in charters is the same sort of lat-...


1

If you have access to an Enterprise geodatabase, this is the use case for Geodatabase archiving. Quoting http://desktop.arcgis.com/en/arcmap/latest/manage-data/geodatabases/archiving-scenarios.htm: A similar example is helpful in parcel management. As edits are made to parcels over time, they can be kept in the archive class. If you want to view how ...


1

The Elastisearch geo-shape datatype seems to support any kind of unit (at least judging from the precision field): https://www.elastic.co/guide/en/elasticsearch/reference/current/geo-shape.html NGA provides an Elasticsearch datastore that connects to Elastisearch: https://github.com/ngageoint/elasticgeo However, I never tried either personally, and the ...


1

As I needed only the cost and not the entire path, I used pgr_dijkstraCost to calculate the costs and now it takes only 2 minutes for more than a million records. I have also added another column to user information to store the nearest osm node and that made my query much faster than expected. Below is the query. INSERT INTO "UserTravelTime" ("FromId", "...


1

Your question is almost all pandas so you could try posting it to stackoverflow and get a shorter answer. But this seems to work: import pandas as pd import arcpy #I tried with csv input. Convert shapefile table to csv... df = pd.read_csv(r'C:\somefolder\input.csv', dtype={'st_time':str,'end_time':str}) #...or use da.SearchCursor columns = ['ID', 'Year', '...


1

To use another user, add the parameters -U myusername -W -U specifies a username, -W will prompt for the password


1

Just for anyone else trying to do this within QGIS, I've found a way. Create a data only layer with a single column 'FIELD'. This should be populated with all the fields (their exact names) from your target layer that you want transposed as records. Load it into your project. Use field calculator to create a new column 'Results' and populate it with the ...


1

The OpenSpatial spec says numerous things about this, When integrating this SQL with that of SQL/MM, the type-name prefix "ST_" should be used as appropriate. And, Class names in SQL/MM carry a "ST_" prefix. This is optional and implementations may chose to drop this prefix as has been done in various places in this standard. From this Committee ...


1

The accepted response is fairly outdated for the current state of the art. MySQL 5.6 has decent support for the OGC data types on MyISAM tables. You probably don't want to use MyISAM tables though. MySQL 5.7 has decent support for the OGC data types on innodb tables: https://dev.mysql.com/doc/refman/5.7/en/spatial-types.html https://www.percona.com/blog/...


1

MySQL Spatial Everyone here is telling you how to do it with PostgreSQL using KNN, without telling you the advantages. Using MySQL you can not determine the nearest neighbor without calculating the distance for all of the neighbors. That's extremely slow. With PostgreSQL this can be done on an index. Neither, MySQL nor MariaDB currently support KNN


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