I have been stuck with a question for quite a while now. I am trying to get maxspeed data of the motorway A95 in Germany with coordinates. I am still new to Overpass Api and have tried to access this data with the following code:
https://overpass-turbo.eu/s/1pO2
[out:csv(maxspeed,
conditional,
wet,
length,
::lat,
::lon)]
[timeout:500];
area["ISO3166-2"="DE-BY"]->.a;
way(area.a)["highway"][highway=motorway][ref="A 95"];
convert result ::=::,::geom=geom(),length=length();
out geom;
I cross checked the coordinates I currently get (I have an existing set of data) and they don't really make sense, I think they represent the middle of the way tagged with a speed limit. Is there a way to extract the coordinates at the beginning/ending of the maxspeed tag?
The goal is to get a csv output listing all speed limits on the motorway with coordinates of their beginning and/or ending. Ideally, I would also want to include data on conditional speed limits.
Thank you very much, I look forward to hearing your opinions!
Related
I have a high volume dataset with keys like this:
lat:6.897585,
long:52.785805,
speed:12,
bearing:144
Basically it is a dataset of records of various trips on cars. The data was stored every few seconds during each trip. The main goal of this project is to be able to visualize only u-turns (turn arounds) on a map. But for now, I am trying to at least show the data on specifc roads. For that, I am using Overpass API
With the help of Overpass Turbo, I can get a dataset with all the roads I need.
However, in the dataset, the road's geometry is represented with LineString type.
My question is, How can I get a bounding box(es) of the roads from Overpass API, so later on, I can display events that happened only on the given roads? Or maybe you have a better solution on how to achieve this?
A bounding box wouldn't be very helpful here, as using it to filter your points would show everything that falls within the box (which could include other nearby roads)
It sounds like getting a buffer around a linestring might get you closer, but could still include points that are within the buffer but not on the road you are inspecting.
The smarter way to do this would be to assign each event to a road segment using some logic based on their attributes/properties, so you don't have to depend on a spatial filter.
For a project on geospatial data analytics, we are currently extracting road type and speed limit data of certain roads along a track by using Overpass' polygon query (where we define the roads by a buffer zone around them). The problem is that in the case of separate tracks, we can end up with disconnected polygons which often lead to a significant increase in computation time. In this situation, we were wondering how Overpass' polygon query actually works. Does the algorithm actually query only the data inside this polygon/these polygons, or does it query inside a bounding box, after which it filters out the data inside the polygons?
The algorithm checks if nodes are inside the defined polygon, or if a way crosses the polygon. It's not based on bounding boxes as you mentioned.
From your description it's not quite clear why disconnected polygons pose an issue. You should get decent performance with a lz4-based backend and a reasonable number of lat/lon pairs in your (poly: ) filter (the more pairs you provide, the more expensive the computation gets).
BTW: The best approach to tackle this issue would be something I described in this blog post: https://www.openstreetmap.org/user/mmd/diary/42055 - unfortunately, this feature is not yet available in the official branch. If you see some use for it, please upvote here: https://github.com/drolbr/Overpass-API/issues/418
I'm working on a Unity3D project to determine some personal properties of a body tracked by the Kinect (V2). Getting a persons length is no issue, but i'm struggeling to get someone weight.
I'm trying to calculate a body's volume (in M2) and multiply by an average BMI, but getting the volume seems hard.
I created a point-cloud by using a particle-system from the depth-image, but i can't wrap my head around the depth-values, which are crucial for determing a persons volume. I need to know the distance from a depth-pixel to the kinect-camera and calculate a volume from that somehow.
Has anyone done something similair or already has something to calculate a persons weight from the kinectData?
Now this is a kind of challenging question. in order to do this you will need to separate the body into shapes. For example head is like a globe and neck as a cylinder. so as you consider head like a globe then you can get the usual 'd' and calculate volume same as the cylinder. For the lower body you can treat it as a box having height and width.There you can get the depth by getting the side view of the person. Anyway it is impossible you to calculate exact amount of volume with a different way.
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 database with the current coordinates of every online user. With a push of a button the user can update his/her coordinates to update his current location (which are then sent off to server). The app will allow you to set the radius of a circle (where the user is in the center) in which you can see the other users on a map. The users outside the circle are discarded.
What is the optimal way to find the users around you?
1) The easiest solution is to find the distance between you and every user and then see if it's less than the radius. This would place the sever under unnecessarily great load as comparison has to be made with every user in the world. In addition, how would one deal with changes in the locations?
2) An improved way would be to only calculate and compare the distance with other users who have similar latitude and longitude. Again in order to be efficient, if the radius is decreased the app should only target users with even closer coordinates. This is not as easy as it sounds. If one were to walk around the North Pole with, say, 10m radius then every step around the circumference would equal to a change of 9 degrees longitude. Every step along the equator would be marginal. Still, even being very rough and assuming there aren't many users visiting the Poles I could narrow it down to some extent.
Any ideas regarding finding users close-by and how to keep them up to date would be much appreciated! :)
Andres
Very good practice is to use GeoHash concept (http://geohash.org/) or GeoModel http://code.google.com/p/geomodel/ (better for BigTable like databases). Those are efficient ways of geospatial searches. I encourage you to read some of those at links I have provided, but in few words:
GeoHash translates lon and lat to unique hash string, than you can query database through those hashes. If points are closer to each other similar prefix will bi longer
GeoModel is similar to GegoHash with that difference that hashed are squares with set accuracy. If square is smaller the hash is longer.
Hope I have helped you. But decision, which you will pick, is yours :).
Lukasz
1) you would probably need a two step process here.
a) Assuming that all locations go into a database, you can do a compare at the sql level (very rough one) based on the lat & long, i.e. if you're looking for 100m distances you can safely disregard locations that differ by more than 0.01 degree in both directions. I don't think your North Pole users will mind ;)
Also, don't consider this unnecessary - better do it on the server than the iPhone.
b) you can then use, for the remaining entries, a comparison formula as outlined below.
2) you can find a way to calculate distances between two coordinates here http://snipplr.com/view/2531/calculate-the-distance-between-two-coordinates-latitude-longitude/
The best solution currently, in my opinion, is to wrap the whole earth in a matrix. Every cell will cover a small area and have a unique identifier. This information would be stored for every coordinate in the database and it allows me to quickly filter out irrelevant users (who are very far away). Then use Pythagoras to calculate the distance between all the other users and the client.