I need to identify clusters centroids to visit in the field. the data the I have is Latitude Longitude and Time. I need to identify clusters with the algorithm >= 8 locations within 50 meters within 24 hours. Is there any particular package or function that can help me with this? Thanks
Related
I am trying to find all locations within a one mile radius of a specified point. I have longitude and latitude data for each point,
multi polygon geometry (Example :
MULTIPOLYGON(((2147312.63139525 161631.130590368,2147298.94808962 164286.665686698,2147623.79332922 164287.328517173,2149920.27996486 164292.162599235,2149944.29540875 161654.921437815,2147312.63139525 161631.130590368)))
and the_geom data
Example:
0106000020DA08000001000000010300000001000000060000003F8FD150F86140417EF6720BF9BA03412C005B79F16140412A8C5353F50D0441D7CF8BE593624041929CCDA0FA0D044177E3D52310674041E5D3004D210E044134F4CF251C67404106CA1A5FB7BB03413F8FD150F86140417EF6720BF9BA0341
I'm just not sure of the best way to approach finding all distances within one mile.
To query geometries within a given radius you have to use ST_DWithin:
SELECT * FROM t
WHERE
ST_DWithin(
the_geom::geography,
ST_MakePoint(longitude,latitude),1609.344); -- 1609.34 metres = 1 mile;
The geography cast enables you to search using metres as unit, which can be easily converted to miles.
Related post: Getting all Buildings in range of 5 miles from specified coordinates
I wanted to stop by and thank Jim Jones for your help! I wasn't able to end up to get the_geom to work, although this was probably user error. The link you shared did help me to come up with the fix below.
SELECT * FROM t where ST_DWithin('POINT($long $lat)'::geography, ST_MakePoint(longitude,latitude)::GEOGRAPHY, 1609.344);
I have a problem to solve. I need to visit 7962 places with a vehicle. The vehicle travels with 10km/h and each time I visit one place I stay there for 1 minute. I want to divide those 7962 places into subsets that take will take up to 8 hours. So lets say 200 places take 8 hours I visit them and come back the next day to visit another maybe 250 places(the 200 places subsets will require more distance travelled). For the distance I only care for Euclidean Distances no need to take into account the distance through the road network.
A map of the 7962 places
What I have done so far is use the k means clustering algorithm to get good enough subsets and then the Lin Kernighan heuristic (Program Concorde) to find the distance. And then compute times. But my results go from 4 hours to 12 hours. Any idea to make it better? Or a code that does this whole task all together. Propose anything but I am not a programmer I just use Python some times.
Set of coordinates :
http://www.filedropper.com/wholesetofcoordinates
Coordinates subsets(40 clusters produces with the k means algorithm):
http://www.filedropper.com/kmeans40clusters
I'm trying to find a way to let my database support fast location-based searches (for example, all items that lie within a certain distance from some geopoint (LAT, LON)). I guess the brute-force solution which calculates the distances between every point in the database and the query point probably won't work for large dataset, so some kind of indexing should be necessary. I'm not sure if there are any existing standard ways to do this (well I know they are out there but Google failed me), but here is a method (or more like a hack?) that I think might work:
Calculate a value from (LAT, LON) and store it in an indexed column. For example, something like floor(LAT / 10) * 10 * 100 + floor(LON / 10) * 10. Each time a query arrives, we first calculate this value for the query and find all the corresponding rows, and then calculate the Euclid distances between all points and the query point.
I have a table with around 5 millions rows and each row has 10 columns representing 10 dimensions.
I would like to be able when a new input is coming to perform a search in the table to return the closest rows using Manhattan distances.
The distance is the sum of abs(Ai-Aj)+abs(Bi-Bj)...
The problem is that for the moment if I do a query, it does a full scan of the entire table, to calculate the distances from every rows, and then sort them to find the top X.
Is there a way to speed the process and make the query more efficient?
I looked at the distance function online for the SDO_GEOMETRY, but I couldn't find it for more than 4 dimensions.
Thank you
If you are inserting a point A and you want to look for points that are within a neighbourhood of radius r (i.e., are less than r away, on any metric), you can do a really simply query:
select x1, x2, ..., xn
from points
where x1 between a1 - r and a1 + r
and x2 between a2 - r and a2 + r
...
and xn between an - r and an + r
...where A = (a1, a2, ..., an), to find a bound. If you have an index over all x1, ..., xn fields of points, then this query shouldn't require a full scan. Now, this result may include points that are outside the neighbourhood (i.e., the bits in the corners), but is an easy win to find an appropriate subset: you can now check against the records in this subquery, rather than checking against every point in your table.
You may be able to refine this query further because, with the Manhattan metric, a neighbourhood will be square shaped (although at 45 degrees to the above) and squares are relatively easy to work with! (Even in 10 dimensions.) However, the more complicated logic required may be more of an overhead than an optimisation, ultimately.
I suggest using function based index. You need this distance calculated, therefore pre calculate it using function based index.
You may want to read following question and it links. Function based index creates hidden column for you. This hidden column will hold manhanttan distance , therefore sorting will be easier.
Thanks for #Xophmeister's comment. Function based index will not help you for arbitrary point. I do not know any sql function to help you here. But if you are willing to use machine learning data mining algorithm.
I suggest cluster your 5 million rows using k-means clustering. Lets say 1000 cluster center you found. Put this cluster centers to another table.
By definition clustering , your points will be assigned to cluster centers. Because of this you know which points are nearest to this cluster center, say
cluster (1) contains 20.000 points, ... cluster ( 987) contains 10.000 points ...
Your arbitrary point will be near to one cluster. You find that your point is nearest to cluster 987. Run your sql , using only points which belongs to this cluster center, that 10.000 points.
You need to add several tables/columns to your schema to make this effective. If your 5.000.000 rows changes continuously, you need to run k-means clustering again as they change. But if they are fairly constant values, one clustering per week or per month will be enough.
I have a coordinate, assume any latitude and longitude values.
I have a domain setup on SimpleDB that has many items (simple strings) with attributes of 'Latitude' and 'Longitude'. Now what I want to do is query SimpleDB and see if the current location coordinates are 'x' meters apart from SimpleDB's items' coordinates. 'x' should be 10.
My app uploads an item to SimpleDB with an attribute that contains the latitude and longitude. I detect the users location, get the coordinates and I want to use a select expression to see if the coordinates are 'x' meters apart. So is their a better approach to doing this? Or is this is the best way, if so, how can I do it?
Here is an example of what the select expression may look like, I just have no idea how to use it in this case and what the '%#' values would be filled in by. This whole format could be off, its just my idea.
select * from test-app-simpledb where Latitude >= '%#' AND Latitude <= '%#' AND Longitude >= '%#' AND Longitude <= '%#'
So "test-app-simpledb" is my SimpleDB domain name, Latitude and Longitude are the attributes I compare to the coordinates. They are all converted to string.
So, how can I do location comparative select expressions. Querying if the item's coordinate (latitude and longitude attribute) are 'x' (in this case 10) meters apart.
Any way to do this? Thanks!
In the revised question, it has become clear that the actual question is how to conduct a query against a Amazon SimpleDB database to see if locations in the database are within a certain radius of a location provided by an iOS app.
As you correctly identify, you really want to do this server-side, rather than client-side, if possible. And the particular solutions will be highly dependent upon the particular database technology (SimpleDB in this case).
This question is touched upon in Spatial queries on AWS SimpleDB. I would suggest checking that out for more information.
As a proxy for a proper distance algorithm, you could translate the distance in meters into a ranges of latitudes and longitudes. Thus, you could, in iOS, calculate a minimum and maximum for both latitude and longitude and then pass those along in the WHERE clause to your remote database. Then the server could filter results based upon those criteria. That admittedly gives you a square-shaped region (rather than a circular region that you get by calculating distances properly), but it makes it really easy to quickly limit the result set with no special geolocation logic required on the server. To do this, you could define a region with MKCoordinateRegionMakeWithDistance, and then grab its span.
MKCoordinateRegion region = MKCoordinateRegionMakeWithDistance(originalCoordinate, 200, 200);
MKCoordinateSpan span = region.span;
That gives you span.latitudeDelta and span.longitudeDelta which you can center around a given location's latitude and longitude to define a square shaped region around a location. To demonstrate that in action, here the center pin is my location at Times Square, and the shaded area is a region (constructed using the above span which is 200m wide and tall), which covers longitudes between 100m east and 100m west of my current location, as well as latitudes 100m north and 100m south of my current location.
This is a way to employ MapKit.framework functions to greatly streamline your SQL queries for remote databases to filter locations based upon geographic distance. If the square region is problematic, you could also further filter the results once they're downloaded to the iOS app using the CLLocation instance method, distanceFromLocation to determine the actual distance. But by limiting the longitudes and latitudes of locations retrieved by the server, you dramatically streamline the server retrieval process while not encumbering it with too much geographic location calculations.
But you really want to have SimpleDB do the full, proper distance calculation, I'll have to leave that to others.
Below, is my original answer. In the original question, I misinterpreted it as being "how do I construct a list of coordinates going in a circle around a particular location?" As made clear by the revised question, that was not the issue at all, but I'll keep my old answer here for historical reference.
Original answer:
If you used Calculate new coordinate x meters and y degree away from one coordinate, the implementation might look like:
NSInteger numberOfPoints = 10;
for (double bearing = 0.0; bearing < 360.0; bearing += (360.0 / numberOfPoints))
{
CLLocationCoordinate2D coordinate = [self coordinateFromCoord:originalCoordinate
atDistanceKm:distanceKm
atBearingDegrees:bearing];
// do whatever you want with this coordinate
}
It seems to work fine. For example, I had an app use this routine to drop 10 pins 100m from me in Times Square: