I've been trying to get longitude and latitude coordinates from Japanese addresses. There are a number of ways to do this, but most of them (such as Google Maps) only allow a limited number of queries a day (I have ~15000), and many do not support Japanese addresses.
Here is an example form of the addresses that I am using:
東京都千代田区丸の内1-9-1
However, recently I found that the 3D maps tool in Excel 365 can plot addresses on a map, and it's fast enough to handle all of my addresses. However, although I can see these points on the Excel, I don't know if there's a way to export these points to longitude-latitude coordinate pairs.
Does anyone know a way to get the longitude-latitude pairs from Excel's 3D maps feature?
I've been working exactly same issue for weeks and i think best soluition is Google API
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I'm a newbie in programming and using machine learning. This is my first post here as I've just recently stumbled upon the first unresolved -of probably many- question.
So I have an extensive database with data on the real-state market of my country and I want to predict the price of the houses -a pretty standard theme ikr- using the latitude and longitude as one variable.
So far I have found Waddell & Besharati-Zadeh's study: https://arxiv.org/pdf/2011.14924.pdf in which they reconstruct the geodata by combining it with other libraries and obtaining string variables as to if certain activities are within a walking distance of 500 meters. So this is a cool alternative but I'm worried there's no accurate data of the walking distance and establishments to do certain activities in my country, not even on google maps. Is there any way in which the combination of the latitude and longitude alone can be used as a predictor variable?
Two different institutions are using the same dataset for spatial interpolation using Ordinary Kriging. However, the resulting maps shows deviances.What are the potential causes of differences in maps?
Some options which come to mind would be:
Variogram fit parameter differences
Neighborhood radius limit
Interpolated grid density (if interpolating into a grid)
Also, you may have better luck on gis.stackexchange.com, and I’d recommend you include a minimal publishable example and whatever other details you can find (what softwares/libraries in which company, etc).
I am looking to use Cassandra for a nearby search type query. based on my lon/lat coordinates I want to retrieve the closest points. I do not need 100% accuracy so I am comfortable in using a bounding box instead of a circle (better performance too), but I can't find concrete instructions (Hopefully with an example) how to implement a bounding box.
From my experience, there's no easy way to have a generic geospatial index search on top of Cassandra. I believe you only have two options:
Geohashing, split your dataset into square/rectangular elements: for example, use integer parts of lat/lon as an indexes in a grid. Upon doing search, you can load all elements in an enclosing grid element and perform full neighbour scan inside your application.
works well if you have an evenly distributed dataset, like grid points in NWP similation that I've had.
works really bad on a datasets like "restaurants in USA", where most of the points are herding around large cities. You'll have unbalanced high load on different grid elements like New York area and get absolutely empty index buckets located somewhere in the Atlantic Ocean.
External indexes like ElasticSearch/Solr/Sphinx/etc.
All of them have geospatial indexing support out-of-the-box, no need to develop your own in your application layer.
You have to setup a separate indexing service and keep cassandra/index data in sync. There's some cassandra/search integrations like DSE (commercial), stargate-core (I've never heard about anyone using this in production), or you can roll your own, but all of these require time and effort.
This issue was touched on in the Euro Cassandra Summit in 2014.
RedHat: Scalable Geospatial Indexing with Cassandra
The presenter explains how he created a spatial index using User Defined Types that is very suitable to querying geospatial data using a region or bounding box based lookup.
The general idea is to break up your data into regions that are defined by bounding boxes. Each region then represents a rowkey, which you can then use to access any data associated with that region. If you have a location of interest, you query the keyspace on the regions which fall inside that area.
I am interested in building a domain-specific image search application capable of searching for images similar to a given image. With a little google-fu I managed to find this question on this site. If I understand the top rated answer correctly then what I am looking to do is achievable by storing the luminosity data for each image in my library.
This is all well and good, but I need a way to quickly search through and compare against 25,000+ records. I have used PostgreSQL and so I immediately thought of it. The problem I find myself facing is that to store luminance data for 256 discrete possible values across 3 colors, I would need a table with 768 columns (r0,g0,b0,...,r255,g255,b255) and in order to effectively search across all records for similarities I would need 768 indices. I have never really worked with large scale data at this level before but that number seems a little unwieldy to me (although I don't know, my experience doesn't extend into this realm).
My other idea is to store the luminance data in one large text column (formatted like this: r0:rrr g0:ggg b0:bbb ... r255:rrr g255:ggg b255:bbb) and construct a full text search index on that column in order to allow searches across the data for similar images.
Another possibility is using the hamming distance between a query histogram and a stored histogram, but I do not believe that is possible to do quickly against all records in the database.
Am I even approaching this the right way? I am also open to any alternatives to relational databases that could provide fast, real-time search across my dataset.
It looks like you are putting each image into a 3 dimensional space -- have you tried looking at any geospatial / multidimensional query engines. Similar images should be near each other in 3-space with your approach.
If I want to find all restaurants within a zip code, I can do a string search on the address, if I want to find all restaurants with 10 miles of a zip code, I need to do a location search. I have a database full of addresses and Geocodes should be no problem. But how do I compute the bounding box of an irregular shaped area, like a zip code, or city, or state or Metro Area?
Is there a tool around that does this? is this information for sale somewhere?
My initial solution is to create an estimate of the areas by searching for all addresses within them and deriving the simplest polygon that surrounds them and using that as a bounding box. However this seems a really brute force way to do this. Do I do this calculation for every city, state, and zip in my database and store it? How have other people solved this problem?
Companies such as Maponics have polygon data on neighborhoods, counties, cities, states, provinces, townships, etc. There may be other providers.
Many of these polygons have huge numbers of points, so you should either:
compute bounding boxes, or
precompute the zip, neighborhood, city, etc. identifier for each address, and index a search collection by these regions.
But why build your application by storing a database of places and computing geographic data on your own? You can partner with providers such as CityGrid; they provide APIs for places that can be searched by neighborhood, zip, etc.; you can use their data for free in your own local application.
If you happen to be using PostgreSQL for your database, you can use box(geometry) or a variation thereof to compute the bounding box for a geometry. You can also implicitly use the bounding box for a geometry in your SQL. For example (from Using PostGIS: Data Management and Queries):
SELECT road_id, road_name FROM roads WHERE roads_geom && ST_GeomFromText('POLYGON((...))',-1);
where && "tells whether the bounding box of one geometry intersects the bounding box of another".
To get the bounding box for a collection of geometries, you can first use Collect or Union to aggregate or combine all the geometries together.
Of course, if you are not using PostGIS, the functionality really comes from GEOS, which is the underlying library that PostGIS actually uses. The basic geometry functions can be used directly (from python for example) to do what you want.