I'm using Core Location with ios 6 for this.
Scenario:
I have the spacial coordinates of a sample of points. I save all those coords using core data.
As and when I am moving with my iphone, I need to detect if I am like 500m from any points in that sample.
Right now, I am looping through those points and calculating the distances of them from my current location. It does this frequently as the user's current location is changing.
But the thing is this will not be a good idea if I have like 100 points, 1000 points.etc
Question:
How can I optimize this, any hint?
Idea:
rasterize (grid) your objects and asign each object to a grid-object (clustering).
while moving, detect the grids intersecting your current position/radius.
get the most nearby object(s) inside those grids.
So you only need to calculate the distances of your grid-objects and the distances to the objects inside the grid-objects nearby your position.
Related
I have a game map that has been tiled over the world map of MapKit. I generate a path to take for the player. With this I find the 3 nearest nodes (in game cities) and select one at random then recurs this to find a 3rd node. I have some logic that means the chosen nodes at each stage aren't in any of the previous arrays to allow for a nice path and no "coming back on your self".
However, the issue I'm facing is I'm using CLLocation.distance(), this unfortunately uses an euclidean distance calculation due to the curvature of the earth. Is there any way to off set the curve as my current logic ends up in all paths slowly leaning towards the poles as the world map is just a flat image.
I've thought about translating CLLocation to a UIView between the first node and all possible second nodes, however this becomes massively intensive.
Any ideas on how to either offset the curve calulation or remove it all together?
I have an iOS app that is using the Google Maps SDK. I track a user as they walk or run then use those coordinates to plot their course along the road using snap to roads.
I have found however the snap to roads uses the direction of the road to plot the course so when it comes to roundabouts the course follows this around even if the user was on the inside of the block.
Is there a way to use snap to roads (or directions) that allows for walking tracks and follows the shortest distance?
Thanks
Looking at the documentation, snap to road is not for walking...
The snapToRoads method takes up to 100 GPS points collected along a route, and returns a similar set of data, with the points snapped to the most likely roads the vehicle was traveling along.
but well.. what you can do it to compare the points, and if the returned points are "some" distance faraway then the original point, you know that this points should be inside the block.
I have a table that contains a bunch of Earth coordinates (latitude/longitude) and associated radii. I also have a table containing a bunch of points that I want to match with those circles, and vice versa. Both are dynamic; that is, a new circle or a new point can be added or deleted at any time. When either is added, I want to be able to match the new circle or point with all applicable points or circles, respectively.
I currently have a PostgreSQL module containing a C function to find the distance between two points on earth given their coordinates, and it seems to work. The problem is scalability. In order for it to do its thing, the function currently has to scan the whole table and do some trigonometric calculations against each row. Both tables are indexed by latitude and longitude, but the function can't use them. It has to do its thing before we know whether the two things match. New information may be posted as often as several times a second, and checking every point every time is starting to become quite unwieldy.
I've looked at PostgreSQL's geometric types, but they seem more suited to rectangular coordinates than to points on a sphere.
How can I arrange/optimize/filter/precalculate this data to make the matching faster and lighten the load?
You haven't mentioned PostGIS - why have you ruled that out as a possibility?
http://postgis.refractions.net/documentation/manual-2.0/PostGIS_Special_Functions_Index.html#PostGIS_GeographyFunctions
Thinking out loud a bit here... you have a point (lat/long) and a radius, and you want to find all extisting point-radii combinations that may overlap? (or some thing like that...)
Seems you might be able to store a few more bits of information Along with those numbers that could help you rule out others that are nowhere close during your query... This might avoid a lot of trig operations.
Example, with point x,y and radius r, you could easily calculate a range a feasible lat/long (squarish area) that could be used to help rule it out if needless calculations against another point.
You could then store the max and min lat and long along with that point in the database. Then, before running your trig on every row, you could Filter your results to eliminate points obviously out of bounds.
If I undestand you correctly then my first idea would be to cache some data and eliminate most of the checking.
Like imagine your circle is actually a box and it has 4 sides
you could store the base coordinates of those lines much like you have lines (a mesh) on a real map. So you store east, west, north, south edge of each circle
If you get your coordinate and its outside of that box you can be sure it won't be inside the circle either since the box is bigger than the circle.
If it isn't then you have to check like you do now. But I guess you can eliminate most of the steps already.
I have a series of nature reserves that need to be plotted, as polygon overlays, on a map using the coordinates contained within KML data. I’ve found a tutorial on the Apple website for displaying KML overlays on map instances.
The problem is that the reserves vary in size greatly - from a small pond right up to several hundred kilometers in size. As a result I can’t use the coordinates of the center point to find the nearest reserves. Instead I need to calculate the nearest point of the reserves polygon to find the nearest one. With the data in KML - how would I go about trying to achieve this?
I've only managed to find one other person ask this and no one had replied :(
Well, there are a couple different solutions depending on your needs. The higher the accuracy required, the more work required. I like Phil's meanRadius parameter idea. That would give you a rough idea of which polygon is closest and would be pretty easy to calculate. This idea works best if the polygons are "circlish". If the polygon are very irregular in shape, this idea loses it's accuracy.
From a math standpoint, here is what you want to do. Loop through all points of all polygons. Calculate the distance from those points to your current coordinate. Then just keep track of which one is closest. There is one final wrinkle. Imagine a two points making a line segment that is very long. You are located one meter away from the midpoint of the line. Well, the distance to these two points is very large, while, in fact you are very close to the polygon. You will need to calculate the distance from your coordinate to every possible line segment which you can do in a variety of manners which are outlined here:
http://www.worsleyschool.net/science/files/linepoint/distance.html
Finally, you need to ask yourself, am I in any polygons? If you're 10 meters away from a point on a polygon, but are, in fact, inside the polygon, obviously, you need to consider that. The best way to do that is to use a ray casting algorithm:
http://en.wikipedia.org/wiki/Point_in_polygon#Ray_casting_algorithm
I'd like to know (from a high level view) what would be required to take a pdf floor plan of a building and determine where exactly you are on that floor plan using GPS coordinates? In addition to location, the user would be presented with a "turn by turn" directions to another point on the map, navigating down hallways, between cubicles, etc.
Use case: an iPhone app that determined a user's location and guided them to a conference room or person's office in the building.
I realize that this is by no means trivial, but any help is appreciated. Thanks!
It's an interesting problem. When you're using Core Location, you're not necessarily using GPS. Using WiFi and cell tower triangulation, you can get pretty good location results. So from Core Location you get a latitude and longitude fix. (You might also get altitude info, since GPS data is 3-dimensional. You also will get an accuracy value.)
So you have lat and lon. You need to map these coordinates to the PDF plan's coordinates. Assuming that the plan is aligned with the latitude and longitude lines, and that you have a lat-long fix for one of the points on the plan, you need to calculate the x-axis scale and y-axis scale. Then it's some calculations to map the lat-long to x-y coordinates on the PDF plan.
GPS may not be accurate enough for this purpose, especially indoors. Assuming errors on
the order of 10 meters, you'll have difficulty determining which floor the user is on.
Here's a neat (?) idea that might work: can you post some "You are here" placards
at various locations around the building? You could label each one with a unique,
machine-readable location code (maybe a QR code or something similar), then take an
image using the camera, have your app read that image and interpret the location code,
and use that instead of GPS to determine the start location.
GPS inside? That's your first -- and biggest -- hurdle.
Next hurdle is knowing the GPS coordinates of at least three points on that PDF to define the plane of of your map in the real world. (The PDF will need to be to scale, of course.)
So that gives you where you are on the PDF. Now you'll need to figure out some way to determine where you can walk (or where you can't) to get directions.