Trying to learn Neo4j, graph DB and using a test setup where i'm representing users who want to trade fruits.
Im trying to find a situation where there exists a "3 person trade" or a direct cycle between 3 or more persons in the system.
This is the scenario i'm trying to store
userA has apples , wants cherries
userB has bananas, wants apples
userC has cherries , wants bananas
So a trade is possible in the above scenario,if the 3 parties are involved in the trade. I need a query that will return the names of the traders/persons.
Need help representing this and writing the code to be able to solve this query. For the scenario, this is the cypher i'm using:
(userA)-[r:has]->(apples) (userA)-[r:wants]->(cherries)
(userB)-[r:has]->(bananas) (userB)-[r:wants]->(apples)
(userA)-[r:has]->(cherries) (userA)-[r:wants]->(bananas)
Also tried using this :
find the group in Neo4j graph db , but that query didnt work ..
thanks for any info, that can help!
The initial approach would be something like this:
MATCH (userA:User)
WHERE (userA)-[:WANTS]->() AND (userA)-[:HAS]->()
MATCH (userA)-[:WANTS]->()<-[:HAS]-(userB)-[:WANTS]->()<-[:HAS]-(userC)-[:WANTS]->()<-[:HAS]-(userA)
RETURN DISTINCT userA, userB, userC
That said, you may need to adjust this based on how big your graph is, and how fast the query runs on your graph.
Is there a service/API that will take a postal/zip code and return the bounding(perimeter) coordinates so I can build a Geometry object in a MS SQL database?
By bounding coordinates, I mean I would like to retrieve a list of GPS coordinates that construct a polygon that defines the US zip code.
An elaboration of my comment, that ZIP codes are not polygons....
We often think of ZIP codes as areas (polygons) because we say, "Oh, I live in this ZIP code..." which gives the impression of a containing region, and maybe the fact that ZIP stands for "Zone Improvement Plan" helps the false association with polygons.
In actuality, ZIP codes are lines which represent, in a sense, mail carrier routes. Geometrically, lines do not have area. Just as lines are strings of points along a coordinate plane, ZIP code lines are strings of delivery points in the abstract space of USPS-designated addresses.
They are not correlated to geographical coordinates. What you will find, though, is that they appear to be geographically oriented because it would be inefficient for carriers to have a route completely irrelevant of distance and location.
What is this "abstract space of USPS-designated addresses"? That's how I am describing the large and mysterious database of deliverable locations maintained by the US Postal Service. Addresses are not allotted based on geography, but on the routes that carriers travel which usually relates to streets and travelability.
Some 5-digit ZIP codes are only a single building, or a complex of buildings, or even a single floor of a building (yes, multiple zip codes can be at a single coordinate because their delivery points are layered vertically). Some of these -- among others -- are "unique" ZIPs. Companies and universities frequently get their own ZIP codes for marketing or organizational purposes. For instance, the ZIP code "12345" belongs to General Electric up in Schenectady, NY. (Edit: In a previous version of Google Maps, when you follow that link, you'd notice that the placement marker was hovering, because it points to a ZIP code, which is not a coordinate. While most US ZIP codes used to show a region on Google Maps, these types cannot because the USPS does not "own" them, so to speak, and they have no area.)
Just for fun, let's try verifying an address in a unique ZIP code. Head over to SmartyStreets and punch in a bogus address in 12345, like:
Street: 999 Sdf sdf
ZIP Code: 12345
When you try to verify that, notice that... it's VALID! Why? The USPS will deliver a piece to the receptacle for that unique ZIP code, but at that point, it's up to GE to distribute it. Pretty much anything internal to the ZIP code is irrelevant to the USPS, including the street address (technically "delivery line 1"). Many universities function in a similar manner. Here's more information regarding that.
Now, try the same bogus address, but without a ZIP code, and instead do the city/state:
Street: 999 Sdf sdf
City: Schenectady
State: NY
It doesn't validate. This is because even though Schenectady contains 12345, where the address is "valid," it geometrically intersects with the "real" ZIP codes for Schenectady.
Take another instance: military. Certain naval ships have their own ZIP codes. Military addresses are an entirely different class of addresses using the same namespace. Ships move. Geographical coordinates don't.
ZIP precision is another fun one. 5-digit ZIP codes are the least "precise" (though the term "specific" might be more meaningful here, since ZIP codes don't pinpoint anything). 7- and 9-digit ZIP codes are the most specific, often down to block or neighborhood-level in urban areas. But since each ZIP code is a different size, it's really hard to tell what actual distances you're talking.
A 9-digit ZIP code might be portioned to a floor of a building, so there you have overlapping ZIP codes for potentially hundreds of addresses.
Bottom line: ZIP codes don't, contrary to popular belief, provide geographical or boundary data. They vary widely and are actually quite un-helpful unless you're delivering mail or packages... but the USPS' job was to design efficient carrier routes, not partition the population into coordinate regions so much.
That's more the job of the census bureau. They've compiled a list of cartographic boundaries since ZIP codes are "convenient" to work with. To do this, they sectioned bunches of addresses into census blocks. Then, they aggregated USPS ZIP code data to find the relation between their census blocks (which has some rough coordinate data) and the ZIP codes. Thus, we have approximations of what it would look like to plot a line as a polygon. (Apparently, they converted a 1D line into a 2D polygon by transforming a 2D polygon based on its contents to fit linear data -- for each non-unique, regular ZIP code.)
From their website (link above):
A ZIP Code tabulation area (ZCTA) is a statistical geographic entity
that approximates the delivery area for a U.S. Postal Service
five-digit or three-digit ZIP Code. ZCTAs are aggregations of census
blocks that have the same predominant ZIP Code associated with the
addresses in the U.S. Census Bureau's Master Address File (MAF).
Three-digit ZCTA codes are applied to large contiguous areas for which
the U.S. Census Bureau does not have five-digit ZIP Code information
in its MAF. ZCTAs do not precisely depict ZIP Code delivery areas, and
do not include all ZIP Codes used for mail delivery. The U.S. Census
Bureau has established ZCTAs as a new geographic entity similar to,
but replacing, data tabulations for ZIP Codes undertaken in
conjunction with the 1990 and earlier censuses.
The USCB's dataset is incomplete, and at times inaccurate. Google still has holes in their data, too (the 12345 is a somewhat good example) -- but Google will patch it eventually by going over each address and ZIP code by hand. They do this already, but haven't made all their map data perfect quite yet. Naturally, access to this data is limited to API terms, and it's very expensive to raise these.
Phew. I'm beat. I hope that helps clarify things. Disclaimer: I used to be a developer at SmartyStreets. More information on geocoding with address data.
Even more information about ZIP codes.
What you are asking for is a service to provide "Free Zip code Geocoding". There are a few out there with varying quality. You're going to have a bad time coding something like this yourself because of a few reasons:
Zip codes can be assigned to a single building or to a post office.
Zip codes are NOT considered a polygonal area. Projecting Zip codes to a polygonal area will require you to make an educated guess where the boundary is between one zipcode and the next.
ZIP code address data specifies only a center location for the ZIP code. Zip code data provides the general vicinity of an address. Mailing addresses that exist between one zipcode and another can be in dispute on which zipcode it actually is in.
A mailing address may be physically closer to zipcode 11111, yet its official zip code is a more distant zip code point 11112.
Google Maps has a geocoding API:
The google maps API is client-side javascript. You can directly query the geocoding system from php using an http request. However, google maps only gives you what the United States Postal Service gives them. A point representing the center of the zipcode.
https://developers.google.com/maps/#Geocoding_Examples
map city/zipcode polygons using google maps
Thoughts on projecting a zipcode to its lat/long bounding box
There are approximately 43,000 ZIP Codes in the United States. This number fluctuates from month to month, depending on the number of changes made. The zipcodes used by the USPS are not represented as polygons and do not have hard and fast boundaries.
The USPS (United States Postal Service) is the authority that defines each zipcode lat/long. Any software which resolves a zipcode to a geographical location would be in need of weekly updates. One company called alignstar provides demographics and GIS data of zipcodes ( http://www.alignstar.com/data.html ).
Given a physical (mailing) address, find the geographical coordinates in order to display that location on a map.
If you want to reliably project what shape the zipcode is in, you are going to need to brute force it and ask: "give me every street address by zipcode", then paint boxes around those mis-shapen blobs. Then you can get a general feel for what geographical areas the zipcodes cover.
http://vterrain.org/Culture/geocoding.html
If you were to throw millions of mailing address points into an algorithm resolving every one to a lat/long, you might be able to build a rudimentary blob bounding box of that zipcode. You would have to re-run this algorithm and it would theoretically heal itself whenever the zipcode numbers move.
Other ideas
http://shop.delorme.com/OA_HTML/DELibeCCtpSctDspRte.jsp?section=10075
http://www.zip-codes.com/zip-code-map-boundary-data.asp
step 1:download cb_2018_us_zcta510_500k.zip
https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html
if you want to keep them in mysql
step 2: in your mysql create a db of named :spatialdata
run this command
ogr2ogr -f "MySQL" MYSQL:"spatialdata,host=localhost,user=root" -nln "map" -a_srs "EPSG:4683" cb_2018_us_zcta510_500k.shp -overwrite -addfields -fieldTypeToString All -lco ENGINE=MyISAM
i uploaded the file on github(https://github.com/sahilkashyap64/USA-zipcode-boundary/blob/master/USAspatialdata.zip)
In the your "spatialdata db" there will be 2 table named map & geometry_columns .
In 'map' there will be a column named "shape".
shape column is of type "geometry" and it contains polygon/multipolygon files
In 'geometry_columns' there will will be srid defined
how to check if point falls in the polygon
SELECT * FROM map WHERE ST_Contains( map.SHAPE, ST_GeomFromText( 'POINT(63.39550 -148.89730 )', 4683 ) )
and want to show boundary on a map
select zcta5ce10 as zipcode, ST_AsGeoJSON(SHAPE) sh from map where ST_Contains( map.SHAPE, ST_GeomFromText( 'POINT(34.1116 -85.6092 )', 4683 ) )
"ST_AsGeoJSON" this returns spatial data as geojson.
Use http://geojson.tools/
"HERE maps" to check the shape of geojson
if you want to generate topojson
mapshaper converts shapefile to topojson (no need to convert it to kml file)
npx -p mapshaper mapshaper-xl cb_2018_us_zcta510_500k.shp snap -simplify 0.1% -filter-fields ZCTA5CE10 -rename-fields zip=ZCTA5CE10 -o format=topojson cb_2018_us_zcta510_500k.json
If you want to convert shapefile to kml
`ogr2ogr -f KML tl_2019_us_zcta510.kml -mapFieldType Integer64=Real tl_2019_us_zcta510.shp
I have used mapbox gl to display 2 zipcodes
example: https://sahilkashyap64.github.io/USA-zipcode-boundary/
code :https://github.com/sahilkashyap64/USA-zipcode-boundary
SQL Server Solution
Download the Shape files from the US Census:
https://catalog.data.gov/dataset/2019-cartographic-boundary-shapefile-2010-zip-code-tabulation-areas-for-united-states-1-500000
I then found this repository to import the shape file to SQL Server, it was very fast and required no additional coding: https://github.com/xfischer/Shape2SqlServer
Then I could write my own script to find out which zip codes are in a polygon I created:
DECLARE #polygon GEOMETRY;
DECLARE #isValid bit = 0;
DECLARE #p nvarchar(2048) = 'POLYGON((-120.1547 39.2472,-120.3758 39.1950,-120.2124 38.7734,-119.6590 38.8162,-119.6342 39.3672,-120.1836 39.2525,-120.1547 39.2472))'
SET #polygon = GEOMETRY::STPolyFromText(#p,4326)
SET #isValid = #polygon.STIsValid()
IF (#isValid = 1)
SET #polygon = #polygon.MakeValid();
SET #isValid = #polygon.STIsValid()
IF (#isValid = 1)
BEGIN
SELECT * FROM cb_2019_us_zcta510_500k
WHERE geom.STIntersects(#polygon) = 1
END
ELSE
SELECT 'Polygon not valid'
I think this is what you need it uses US Census as repository: US Zipcode
Boundaries API: https://www.boundaries-io.com
Above API shows US Boundaries(GeoJson) by zipcode,city, and state. you should use the API programatically to handle large results.
Disclaimer,I work here
I think the world geoJson link and the google map geocode api can help you.
example: you can use the geocode api to code the zip,you will get the city,state,country,then,you search from the world and us geoJson get the boundry,I have an example of US State boundry,like dsdlink
in my project we have an entity called Trip. This trip has two points: start and finish. Start and finish are geo coordinates with some added properties like address atc.
what i need is to query for all Trips that satisifies search criteria for both start and finish.
smth like
select from trips where start near 16,16 and finish near 18,20 where type = type
So my question is: which database can offer such functionality?
what i have tried
i have explored mongodb which has support for geo indexes but does not support this use case. current solution stores the points as separate documents which have a reference to a Trip. we run two separate quesries for starts and finishes, then extract ids of their associated trips and then select trip ids that are found both in starts and finishes and finally return a collection of trips.
on a small sample it works fine but with a larger collection it gets slow and it's like scratching my left ear with my right hand.
so i am looking for a better solution.
i know about neo4j and its spatial plugin but i couldn't even make it work on windows. would it support our use case?
or are there any better solutions? preferably with a object mapper written in php.
like edze already said Postgres (PostGIS) or SQLite(SpatiaLite) is what your looking for
SELECT
*
FROM
trips
WHERE
ST_Distance(ST_StartPoint(way), ST_GeomFromText('POINT(16 16)',4326) < 5
AND ST_Distance(ST_EndPoint(way), ST_GeomFromText('POINT(18 20)',4326) < 5
AND type = 'type'
Background:
I need to store the following data in a database:
osm nodes with tags;
osm edges with weights (that is an edge between two nodes extracted from 'way' from an .osm file).
Nodes that form edges, which are in the same 'way' sets should have the same tags as those ways, i.e. every node in a 'way' set of nodes which is a highway should have a 'highway' tag.
I need this structure to easily generate a graph based on various filters, e.g. a graph consisting only of nodes and edges which are highways, or a 'foot paths' graph, etc.
Problem:
I have not heard about the spatial index before, so I just parsed an .osm file into a MySQL database:
all nodes to a 'nodes' table (with respective coordinates columns) - OK, about 9,000,000 of rows in my case:
(INSERT INTO nodes VALUES [pseudocode]node_id,lat,lon[/pseudocode];
all ways to an 'edges' table (usually one way creates a few edges) - OK, about 9,000,000 of rows as well:
(INSERT INTO edges VALUES [pseudocode]edge_id,from_node_id,to_node_id[/pseudocode];
add tags to nodes, calculate weights for edges - Problem:
Here is the problematic php script:
$query = mysql_query('SELECT * FROM edges');
$i=0;
while ($res = mysql_fetch_object($query)) {
$i++;
echo "$i\n";
$node1 = mysql_query('SELECT * FROM nodes WHERE id='.$res->from);
$node1 = mysql_fetch_object($node1);
$tag1 = $node1->tags;
$node2 = mysql_query('SELECT * FROM nodes WHERE id='.$res->to);
$node2 = mysql_fetch_object($node2);
$tag2 = $node2->tags;
mysql_query('UPDATE nodes SET tags="'.$tag1.$res->tags.'" WHERE nodes.id='.$res->from);
mysql_query('UPDATE nodes SET tags="'.$tag2.$res->tags.'" WHERE nodes.id='.$res->to);`
Nohup shows the output for 'echo "$i\n"' each 55-60 seconds (which can take more than 17 years to finish if the size of the 'edges' table is more than 9,000,000 rows, like in my case).
Htop shows a /usr/bin/mysqld process which takes 40-60% of CPU.
The same problem exists for the script which tries to calculate the weight (the distance) of an edge (select all edges, take an edge, then select two nodes of this edge from 'nodes' table, then calculate the distance, then update the edges table).
Question:
How can I make this SQL updates faster? Should I tweak any of MySQL config settings? Or should I use PostgreSQL with PostGIS extension? Should I use another structure for my data? Or should I somehow utilize the spatial index?
If I understand you right there is two things to discuss.
First, your idea of putting the highway-tag on the start and stop nodes. A node can have more than one edge connected, where to put the tag from the second edge? Or third or fourth if it is a crossing? The reason the highway tag is putted in the edges table in the first place is that from a relational point of view that is where it belongs.
Second, to get the whole table and process it outside the database is not the right way. What a relational database is really good at is taking care of this whole process.
I have not worked with mysql, and I fully agree that you will probably get a lot more fun if migrating to PostGIS since PostGIS has a lot better spatial capabilities (even if you don't need any spatial capabilities for this particular task) from what I have heard.
So if we ignore the first problem and just for showing the concept say that there is only two edges connected to one node and that each node has two tag-fields. tag1 and tag2. Then it could look something like this in PostGIS:
UPDATE nodes set tag1=edges.tags from edges where nodes.id=edges.from;
UPDATE nodes set tag2=edges.tags from edges where nodes.id=edges.to;
If you disable the indexes that should be very fast.
Again,
if I have understood you right.
PostgreSQL
Openstreetmap itself uses PostgreSQL, so I guess that's recommended.
See: http://wiki.openstreetmap.org/wiki/PostgreSQL
You can see OSM's database schema at: http://wiki.openstreetmap.org/wiki/Database_Schema
So you can use the same fields, fieldtypes and indexes that OSM uses for maximum compatibility.
MySQL
If you want to import .osm files into a MySQL database, have a look at:
http://wiki.openstreetmap.org/wiki/OsmDB.pm
Here you will find perl code that will create MySQL tables, parse a OSM file and import it into your MySQL database.
Making it faster
If you are updating in bulk, you don't need to update the indexes after every update.
You can just disable the indexes, do all your updates and re-enable the index.
I'm guessing that should be a whole lot faster.
Good luck
I run a project that deals with plays, mostly Shakespeare. Right now, for storage, we just parse them out into a JSON array formatted like this:
[0: {"title": "Hamlet", "author": ["Shakespeare", "William"], "noActs": 5}, | info
1: [null, ["Scene 1 Setting", "Stage direction", ["character", ["char line 1", "2"]] | act 1
...]
and save them to a file.
We'd like to now move to a relational database for a variety of reasons (foremost currently search) but have no idea how to represent things.
I'm looking for an outline of the best way to do things?
the topic is 'normalization'
begin by identifying your main classifications, such as PLAY, AUTHOR, ACT, SCENE
then add attributes - specifically, a primary key like play_id, act_id, etc.
then add still more attributes like NAME or other identifying information.
then finally, add some relationships between these object bby creating more tables like PLAY_AUTHOR which includes PLAY_ID and AUTHOR_ID.
Not much of a theater goer myself, but this may give you some ideas should you choose a relational DB.