I'm working on a feature that uses the foursquare API to find venues nearby. Is startMonitoringSignificantLocationChanges accurate enough for this? I.e., does foursquare use startMonitoringSignificantLocationChanges or do they use startUpdatingLocation, or both? If both, how should you use them together?
Even after reading this data analisys of startMonitoringSignificantLocationChanges, I'm still not sure which to use?
Also, what about for an app like Grindr?
From looking at that article I would say no but it depends entirely on what you mean by nearby. Remember that in a shopping centre it is extremely easy for their to be numerous venues near you in a 250 - 500 metre radius. I mean quite a few shops are 10 metres wide or so, same with coffee shops etc. But then again it depends what you mean by venue.
Personally if I was making the app though I'd want something more predictable and accurate than the significant location change method. That seems to be geared more towards finding out a persons locale rather than finding what they are near too within said locale. For proper location services I'd still want to use the fully fledged GPS data.
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I am a beginner user of Foursquare API.
Most of the Foursquare techniques I learned so far is to do query such as search for or explore from a single location point (e.g. a café or a hotel) in a single geographical coordinate pair of longitude and latitude.
My question is, if it is possible to make an query to explore venues within an area, instead of in the vicinity of a single location point. By ‘an area’, I specifically mean the unit of the administrative division, such as a borough, of a neighbourhood.
In other words, my intending query, if possible, would start from an area specification of the administrative division of my interest (e.g. borough), such as its name or its border’s geographic coordinates—as a 'key' to link with Foursquare data, rather than start from a single location point.
I downloaded GeoJson file that already defined the geographical coordinates of the border of the administrative division of neighbourhoods in a city that I am interested in (link: http://cdn.buenosaires.gob.ar/datosabiertos/datasets/barrios/barrios.geojson). Just FYI, in this link, the neighbourhood is described as ‘barrio’ and the border is defined in the form of 'Polygon'.
I just wonder if I can use an area specification—either the name of an administrative division or a set of the geographic coordinates of an administrative division's border—as a key to make an query about venues such as restaurants, hospitals, and polices within the unit of an administrative division (e.g. borough) from corner to corner.
I guess that the underlying question is if Foursquare side has such info stored in somewhere in its system: if not, my contemplated approach would not work.
Or there might be a totally different workaround to achieve my goal.
If anyone can advise me on this matter, I would highly appreciate it.
Thanks
Given the parameters listed in the docs I think that the best approach would be to use the ll or near parameters and also include a radius so you can limit the search for a given area or region.
To get the middle point for the polygon I guess you would need to do some math but shouldn't be that difficult.
Besides this I think there doesn't seem to be any other parameter in Foursquare API to search by area or by a coordinates array (polygon).
Anyways, I would suggest that you go through the Foursquare API docs for both search and explore endpoints and check for yourself.
since you already have the polygon of interested region:
you could fit many small radius circles within to cover majority of the area.
this is not recommended as it may be rate limited or get you blacklisted but: the foursquare website has a 'draw' tool that allows you to draw a polygon and search venues within. (open networks tab under inspect in your browser and see request) I have noticed that it also can't take very complex polygons, no enclaves, and it further aggressively simplifies polygons to remove holes/dents/land bridges.
here's my demo requests. polygon search isn't strict and might show some venues outside the border.
the url:
https://foursquare.com/explore?mode=url&polygon=35.957999786220704%2C-80.41236877441406%3B35.897393965545646%2C-80.38215637207031%3B35.87847989454576%2C-80.55107116699219%3B35.954664894270834%2C-80.54901123046875%3B35.994118756097%2C-80.386962890625%3B35.957999786220704%2C-80.41236877441406
the corresponding get for venues:
https://api.foursquare.com/v2/search/recommendations?locale=en&explicit-lang=false&v=20210302&m=foursquare&limit=30&intent=bestnearby&polygon=40.8252411857252%2C-74.00630950927733%3B40.817446884558805%2C-73.99772644042969%3B40.81147063339219%2C-73.99875640869139%3B40.80757278825516%2C-74.00768280029297%3B40.80887209540822%2C-74.01729583740234%3B40.81406906961218%2C-74.02175903320312%3B40.8197852710803%2C-74.02210235595702%3B40.826280356677124%2C-74.01695251464844%3B40.8252411857252%2C-74.00630950927733&wsid={}&oauth_token={}
I am trying to create an app that will help users find restaurants/movie theaters/malls/etc. to hang out based on ratings and distance. Other than just the place itself, I would also like to know more detailed information about the place. For example, if I were to look for parks, I would also like to know if theres a basketball or tennis court there. Ratings and popularity would also be an important aspect to prioritize suggestions.
After looking through all three of the APIs, I could not really find any substantial differences other than their search limits. Could anyone really differentiate each API for me? Maybe even recommend one based on my specific need?
Thanks!
The Foursquare API would fit this use case perfectly because you can supply very specific filters through the API. Also, they have extensive coverage around the world, unlike Google or Yelp.
I would check out the venues/explore endpoint and use a categoryId of Parks. You can use a query parameter of "basketball" or "tennis" to find parks that have courts for these.
Is there a free database for venues (such as cinemas, restaurants, cafes, schools, etc.) which have attributes for the venues such as geographic coordinates, name, contact info, address, type or category, etc.
I know that there are alternatives like Foursquare Venues Platform. But Foursquare Venues Platform have limits on the number of results per request and square measure of the bounding box.
I need to get a collection of venues on a large area.
You should check out GeoNames (www.geonames.org)
It is not a perfect fit for what you are asking, but they do have a downloadable (free or paid) database of locations.
You can import it and then get as much data as you want.
It contains a lot of data, but we found that there is a lot of work to be done cleaning it up (we were using the free version)
Maybe the paid will have less noise.
Do note, that it will not be as complete as what Foursquare or Google places or Facebook has to offer, but if you need to query a large area with many results, it might fit your needs.
They also have an API (Web Services).
I'm getting some erratic results from Foursquare's venue search API and I'm wondering if anyone has any tips on how to process my input parameters for the most "intuitive" results.
For example, suppose I am searching for a venue called "Ise Sushi", around "New York, NY", which is equivalent to (lat: 40.7143528, lon: -74.00597309999999) using Google Maps API. Plugging into the Foursquare Venue API, we get:
https://api.foursquare.com/v2/venues/search?query=ise%20sushi&ll=40.7143528%2C-74.00597309999999
This yields pretty underwhelming results: the venue I'm looking for ends up rather far down the list, at 11th place. What's interesting is that reducing the precision of the coordinates appears to produce much better results. For example, suppose we were to round the coordinates to 3 significant digits:
https://api.foursquare.com/v2/venues/search?query=ise%20sushi&ll=40.7%2C-74.0
This time, the venue I'm looking for ends up in 2nd place, even though it is actually farther from the center of the search (1072 meters, vs. 833 meters using the first query).
Another modification that appears to help improve the quality of search is substituting underscores for spaces to separate our search terms. For example, here's the original query with underscores:
https://api.foursquare.com/v2/venues/search?query=ise_sushi&ll=40.7143528%2C-74.00597309999999
This produces the most intuitive-seeming results: the venue I'm looking for appears first, and is accompanied by just one other result, "Ise Restaurant" (which is tagged as a "sushi restaurant"). For what it's worth, this actually seems to be the result set of the same search conducted on Foursquare's own website.
I'm curious what lessons I should be learning from this. Should I be reducing the precision of my coordinates? Should I be connecting my search terms with underscores, and if so, does that limit how a user can order their search terms?
Although there are ranking improvements we can make on our end to find this distant exact match, it generally also helps to specify intent=browse (although it looks like in this case, for now, it may give you worse results). By default, /venues/search uses intent=checkin, which tries really hard to find close-by matches for checking in to, at the expense of other ways a venue might match your search. Learn more at https://developer.foursquare.com/docs/venues/search
Many websites today (including stackoverflow) and games allow people to perform voting, give feedback, enable additional features etc, according to a score: eg. reputation, or MMORPG credits.
As a programmer that will probably need to implement a community based website in the near future, I am interested in knowing about the existence of basic algorithms and decisions to be made so that everything is balanced. For example, the fact that one vote up grants 10 reputations and one down grants -2 was arbitrary or properly weighted ? How to decide the price of a given item and the rewards in a MMORPG, so that everything is balanced? I guess that WoW designers relied on their experience, but I am also sure that this experience can be found somewhere written down. Although this is a social problem, the pricing of a given feature and the reward for a given task are technical/mathematical ones, as you need to give a value to each feature according to some mathematical criteria (although not easy to devise, I guess)
Of course, this question could bring us far in terms of theory of economics, but I am sort of hoping that there are well defined and known simplified patterns and rules for this issue. I just don't know the keywords to query for.
Probably the most important thing to point out here is that this is a social problem not a technical one.
By that I mean that you could use the exact same system as SO on an MMORPG and it would flop or have really undesirable side effects. Whether a system works or not depends on the community you drop it into and the intended purpose. It can also depend on some luck whether people latch onto it or not. You may get early negative behaviour that sets the tone for future negativity and discourages positive involvement. Or it could go completely the other way.
There is no magic formula that made the vote/rep weighting what it is on SO other than long discussions about how to encourage certain behaviour and then some testing and fine-tuning. For example, a downvote costs 1 rep and is -2 rep to the recipient. The guiding principle was that downvotes should cost. After that, it was trial by error.
You might want to read The Value of Downvoting, or, How Hacker News Gets It Wrong and Vote Fraud for some of Jeff's and Joel's thoughts on that subject. Joel's Tech Talk on Stackoverflow at Google is also enlightening.
Voting is actually a very difficult problem. There are so many models of voting, and they all produce different results. For example, choosing your one favorite candidate versus ranking candidates produces a different result. Choosing your LEAST favorite candidate produces a different result. Organizing choices into good/bad produces different results.
Balancing then becomes something that can be done by asking the community. It's very difficult to balance games of that magnitude, simply because even your most exhaustive tests wont cover all of the cases. Having a properly established forum where users can give their opinions as well as having testers who watch out for balancing issues is probably the best way to go.
Oh, and if you want an abstract about the voting problem I mentioned, it's here:
http://www.cs.rochester.edu/~lane/computational-politics.html