I have a list with nearly 100,000 GPS coordinates in lat/long format in a CSV file. A lot of these are only a few inches away from each other, so I would like to merge them somehow, or filter those out that are too close together within a certain radius.
Do you guys know of a script or a service that can do this automatically?
There is a reference to a nice paper that explains how to find nearest lat/lng points inside a specified bounding box in another thread, which you can find here: latitude/longitude find nearest latitude/longitude - complex sql or complex calculation
Here is the direct link to the paper: Geo Distance Search with MySQL
I think you can adapt the idea from the paper to your domain in order to set up a filter procedure.
I am currently developing technique to help users find a spot to park.
But i face a little problem:
if a user indicates that he is parking right now in a free spot but he is lying and he is at home right now.
How can i detect from GPS if he is inside a building or along side the road?
Thanks
You'll need map data (OpenStreetMap is free), and figure out whether the user is somewhere on that map or not. You do that by comparing GPS data to the map data.
What I do in such situations is measure the distance between the lat/lon and each road, and compare the GPS angle to that of each line. The more context information you use the more accurate you can get your results:
If the speed is 60km/h, you're probably not in a building. You're probably not on a 30km/h road either.
If you're standing still for more than 2 minutes, you're probably not in a car.
If you know the buildings, and there are only a few of them, you could check if you see a certain wifi router or not.
Basically you'll calculate a score for each road, and then pick the road with the highest score to know where you are.
Score = DistScore*DistWeight + AngleScore+AngleWeight etc.
Also, from iOS and Android you get an accuracy in meters. You can also calculate that yourself if you can access raw GPS data. Using that, you set the area that you need to scan. For example, for a high accuracy (3m), you probably don't have many roads to scan. If the accuracy is 50m, you should probably match roads that are farther away.
If accuracy is important, you should look at series of GPS data, and test if the followed route is a logical path or not.
This question somewhat overlaps knowledge on geospatial information systems, but I think it belongs here rather than GIS.StackExchange
There are a lot of applications around that deal with GPS data with very similar objects, most of them defined by the GPX standard. These objects would be collections of routes, tracks, waypoints, and so on. Some important programs, like GoogleMaps, serialize more or less the same entities in KML format. There are a lot of other mapping applications online (ridewithgps, strava, runkeeper, to name a few) which treat this kind of data in a different way, yet allow for more or less equivalent "operations" with the data. Examples of these operations are:
Direct manipulation of tracks/trackpoints with the mouse (including drawing over a map);
Merging and splitting based on time and/or distance;
Replacing GPS-collected elevation with DEM/SRTM elevation;
Calculating properties of part of a track (total ascent, average speed, distance, time elapsed);
There are some small libraries (like GpxPy) that try to model these objects AND THEIR METHODS, in a way that would ideally allow for an encapsulated, possibly language-independent Library/API.
The fact is: this problem is around long enough to allow for a "common accepted standard" to emerge, isn't it? In the other hand, most GIS software is very professionally oriented towards geospatial analyses, topographic and cartographic applications, while the typical trip-logging and trip-planning applications seem to be more consumer-hobbyist oriented, which might explain the quite disperse way the different projects/apps treat and model the problem.
Thus considering everything said, the question is: Is there, at present or being planned, a standard way to model canonicaly, in an Object-Oriented way, the most used GPS/Tracklog entities and their canonical attributes and methods?
There is the GPX schema and it is very close to what I imagine, but it only contains objects and attributes, not methods.
Any information will be very much appreciated, thanks!!
As far as I know, there is no standard library, interface, or even set of established best practices when it comes to storing/manipulating/processing "route" data. We have put a lot of effort into these problems at Ride with GPS and I know the same could be said by the other sites that solve related problems. I wish there was a standard, and would love to work with someone on one.
GPX is OK and appears to be a sort-of standard... at least until you start processing GPX files and discover everyone has simultaneously added their own custom extensions to the format to deal with data like heart rate, cadence, power, etc. Also, there isn't a standard way of associating a route point with a track point. Your "bread crumb trail" of the route is represented as a series of trkpt elements, and course points (e.g. "turn left onto 4th street") are represented in a separate series of rtept elements. Ideally you want to associate a given course point with a specific track point, rather than just giving the course point a latitude and longitude. If your path does several loops over the same streets, it can introduce some ambiguity in where the course points should be attached along the route.
KML and Garmin's TCX format are similar to GPX, with their own pros and cons. In the end these formats really only serve the purpose of transferring the data between programs. They do not address the issue of how to represent the data in your program, or what type of operations can be performed on the data.
We store our track data as an array of objects, with keys corresponding to different attributes such as latitude, longitude, elevation, time from start, distance from start, speed, heart rate, etc. Additionally we store some metadata along the route to specify details about each section. When parsing our array of track points, we use this metadata to split a Route into a series of Segments. Segments can be split, joined, removed, attached, reversed, etc. They also encapsulate the method of trackpoint generation, whether that is by interpolating points along a straight line, or requesting a path representing directions between the endpoints. These methods allow a reasonably straightforward implementation of drag/drop editing and other common manipulations. The Route object can be used to handle operations involving multiple segments. One example is if you have a route composed of segments - some driving directions, straight lines, walking directions, whatever - and want to reverse the route. You can ask each segment to reverse itself, maintaining its settings in the process. At a higher level we use a Map class to wire up the interface, dispatch commands to the Route(s), and keep a series of snapshots or transition functions updated properly for sensible undo/redo support.
Route manipulation and generation is one of the goals. The others are aggregating summary statistics are structuring the data for efficient visualization/interaction. These problems have been solved to some degree by any system that will take in data and produce a line graph. Not exactly new territory here. One interesting characteristic of route data is that you will often have two variables to choose from for your x-axis: time from start, and distance from start. Both are monotonically increasing, and both offer useful but different interpretations of the data. Looking at the a graph of elevation with an x-axis of distance will show a bike ride going up and down a hill as symmetrical. Using an x-axis of time, the uphill portion is considerably wider. This isn't just about visualizing the data on a graph, it also translates to decisions you make when processing the data into summary statistics. Some weighted averages make sense to base off of time, some off of distance. The operations you end up wanting are min, max, weighted (based on your choice of independent var) average, the ability to filter points and perform a filtered min/max/avg (only use points where you were moving, ignore outliers, etc), different smoothing functions (to aid in calculating total elevation gain for example), a basic concept of map/reduce functionality (how much time did I spend between 20-30mph, etc), and fixed window moving averages that involve some interpolation. The latter is necessary if you want to identify your fastest 10 minutes, or 10 minutes of highest average heartrate, etc. Lastly, you're going to want an easy and efficient way to perform whatever calculations you're running on subsets of your trackpoints.
You can see an example of all of this in action here if you're interested: http://ridewithgps.com/trips/964148
The graph at the bottom can be moused over, drag-select to zoom in. The x-axis has a link to switch between distance/time. On the left sidebar at the bottom you'll see best 30 and 60 second efforts - those are done with fixed window moving averages with interpolation. On the right sidebar, click the "Metrics" tab. Drag-select to zoom in on a section on the graph, and you will see all of the metrics update to reflect your selection.
Happy to answer any questions, or work with anyone on some sort of standard or open implementation of some of these ideas.
This probably isn't quite the answer you were looking for but figured I would offer up some details about how we do things at Ride with GPS since we are not aware of any real standards like you seem to be looking for.
Thanks!
After some deeper research, I feel obligated, for the record and for the help of future people looking for this, to mention the pretty much exhaustive work on the subject done by two entities, sometimes working in conjunction: ISO and OGC.
From ISO (International Standards Organization), the "TC 211 - Geographic information/Geomatics" section pretty much contains it all.
From OGS (Open Geospatial Consortium), their Abstract Specifications are very extensive, being at the same time redundant and complimentary to ISO's.
I'm not sure it contains object methods related to the proposed application (gps track and waypoint analysis and manipulation), but for sure the core concepts contained in these documents is rather solid. UML is their schema representation of choice.
ISO 6709 "[...] specifies the representation of coordinates, including latitude and longitude, to be used in data interchange. It additionally specifies representation of horizontal point location using coordinate types other than latitude and longitude. It also specifies the representation of height and depth that can be associated with horizontal coordinates. Representation includes units of measure and coordinate order."
ISO 19107 "specifies conceptual schemas for describing the spatial characteristics of geographic features, and a set of spatial operations consistent with these schemas. It treats vector geometry and topology up to three dimensions. It defines standard spatial operations for use in access, query, management, processing, and data exchange of geographic information for spatial (geometric and topological) objects of up to three topological dimensions embedded in coordinate spaces of up to three axes."
If I find something new, I'll come back to edit this, including links when available.
I have come across a problem that I 've never solved before but I find it frequently implemented in various apps so I would like to ask if there is a common way to solve it. I have a set of analytics data each representing some logging action (i.e. info, warn etc). Each of this items has a location and a type (i.e. action). There can be millions of these items per area (depending on the area size or map zoom).
I am looking for the best way to store this set of data in my database. I am very comfortable with SQL Server but dont mind what db I have to use as long as it can handle the scalability requirements. If Amazon WS offers such a product or some other cloud solution then even better cause thats how we are planning to host this app. Google maps will be used to visualize the data.
Some requirements:
Be able to plot all data for a given map rectangle (a common google
map interface with markers representing the logging actions)
Be able to zoom in/zoom out and get relevant data for the new map rectangle
Be able to "group" markers in one bigger marker if data are very close. For instance, if point A is 1 km away from point B and I am seeing a map of 10 km radius then I should see two independent points, A and B. But if I zoom out to 500 km radius then point A and B are too close to each other so I would like to group them in one marker. Hopefully that's possible.
If SQL Server is not a good solution then a free, very cheap or cloud-based storage solution should be recommended (no I cant afford an Oracle).
All the queries above should be able to come back within milliseconds or somehow to be cached. Queries will be of the kind: Get me all analytics data for the given map window with zoom of the given rectangle latitude/longitude.
Thanks,
Yannis
If I undertsood correctly, there are 2 sides to your question:
1- A Database System, that supports storage and lookup of spatial data. Many of the free/open source RDBMS have spatial extensions: MySQL and Postgres (PostGIS) in particular. Spatial data are stored like any other data with the addition of spatial geometry attribute, which describes the shape of your data instance (point, rectangle, polygon, ellipse, ...). You can query spatial data entities, with spatial filters. And of course, spatial queries support joints and unions and almost all kind of SQL constructs.
2- A client/server api that would support rendering of spatial data (with the usual functions such as zoom in, zoom out, pan, etc.), caching and drill-down. As far as I know, there isn't one api that support all these features together, out the box. But there are some interesting apis that you might want to investigate.
Hope this helps.
We're building a GIS interface to display GPS track data, e.g. imagine the raw data set from a guy wandering around a neighborhood on a bike for an hour. A set of data like this with perhaps a new point recorded every 5 seconds, will be large and displaying it in a browser or a handheld device will be challenging. Also, displaying every single point is usually not necessary since a user can't visually resolve that much data anyway.
So for performance reasons we are looking for algorithms that are good at 'reducing' data like this so that the number of points being displayed is reduced significantly but in such a way that it doesn't risk data mis-interpretation. For example, if our fictional bike rider stops for a drink, we certainly don't want to draw 100 lat/lon points in a cluster around the 7-Eleven.
We are aware of clustering, which is good for when looking at a bunch of disconnected points, however what we need is something that applies to tracks as described above. Thanks.
A more scientific and perhaps more math heavy solution is to use the Ramer-Douglas-Peucker algorithm to generalize your path. I used it when I studied for my Master of Surveying so it's a proven thing. :-)
Giving your path and the minimum angle you can tolerate in your path, it simplifies the path by reducing the number of points.
Typically the best way of doing that is:
Determine the minimum number of screen pixels you want between GPS points displayed.
Determine the distance represented by each pixel in the current zoom level.
Multiply answer 1 by answer 2 to get the minimum distance between coordinates you want to display.
starting from the first coordinate in the journey path, read each next coordinate until you've reached the required minimum distance from the current point. Repeat.