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Summary

Project website postgis.net/workshops/postgis-intro
Project maintainers9 User avatar cvvergaraUser avatar dkastlUser avatar komzpaUser avatar astrid_emdeUser avatar dmateosUser avatar jsanzUser avatar andischiUser avatar jefUser avatar yellow73
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Source code repository https://github.com/postgis/postgis-workshops.git
Repository branch master
Last remote commit Update sql cheat sheet with makevalid example 5f99414
Paul Ramsey authored 10/30/2025
Last commit in Weblate Translated using Weblate (Ukrainian) 8adab23
Weblate Translation Memory authored 08/11/2025
Weblate repository https://weblate.osgeo.org/git/postgis-workshop/index/
File mask postgis-intro/sources/locale/*/LC_MESSAGES/3d.po
Translation file Download postgis-intro/sources/locale/ja/LC_MESSAGES/3d.po
Last change June 10, 2025, 7:23 p.m.
Last change made by None
Language Japanese
Language code ja
Text direction Left to right
Case sensitivity Case-insensitive
Number of speakers 117,608,755
Number of plurals 1
Plural type None
Plurals
Plural formula 0
02/23/2026

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Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 39 835 5,149
Translated 100% 39 100% 835 100% 5,149
Needs editing 0% 0 0% 0 0% 0
Read-only 0% 0 0% 0 0% 0
Failing checks 0% 0 0% 0 0% 0
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 0% 0 0% 0 0% 0
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Resource updated
The “postgis-intro/sources/locale/ja/LC_MESSAGES/3d.po” file was changed. 06/10/2025
User avatar None

Resource updated
The “postgis-intro/sources/locale/ja/LC_MESSAGES/3d.po” file was changed. 01/08/2023
User avatar yellow73

Translation uploaded
The results should be the same. In general the N-D index is very slightly slower than the 2-D index, so only use the N-D index where you are certain that N-D queries will improve the selectivity of your queries.
結果は同じなはずです。一般にN次元インデックスは2次元インデックスと比べて少し遅いです。N次元インデックスの使用は、N次元クエリで実行するクエリの選択性が向上すると確信できる場合だけにしてください。
09/15/2022
User avatar yellow73

Translation uploaded
To search the ``nyc_streets`` table using the N-D index, just replace the usual ``&&`` 2-D index operator with the ``&&&`` operator.
N次元インデックスを使って``nyc_streets``テーブル内を探索するには、2次元インデックス演算子``&&``から``&&&``演算子に変更するだけです。
09/15/2022
User avatar yellow73

Translation uploaded
Once you have the index built, you can use it in queries with the ``&&&`` index operator. ``&&&`` has the same semantics as ``&&``, "bounding boxes interact", but applies those semantics using all the dimensions of the input geometries. Geometries with mis-matching dimensionality do not interact.
インデックスを構築すると、クエリ内でインデックス演算子``&&&``によってインデックスを使えるようになります。``&&&``は「バウンディングボックスの相互作用」としては``&&``と同じ意味ですが、入力ジオメトリの次元の全てを使うという意味が適用されます。次元が合致しないジオメトリは相互作用しません。
09/15/2022
User avatar yellow73

Translation uploaded
You can create a multi-dimensional index on data of any dimensionality (even mixed dimensionality). For example, to create a multi-dimensional index on the ``nyc_streets`` table,
多次元インデックスを任意次元のデータに作ることができます (混合次元であっても)。たとえば、``nyc_streets``テーブル上に多次元インデックスを作るには次のようにします。
09/15/2022
User avatar yellow73

Translation uploaded
A set of GPS traces in X/Y/T space might be a *good* candidate for a 3-D index, if the GPS tracks overlapped each other frequently in all dimensions (for example, driving the same route over and over at different times), since there would be large variability in all dimensions of the data set.
X/Y/T空間でのGPSトラックは、GPSトラックが互いに周期的に全ての次元に及ぶ (たとえば同じ道程を違う時刻に何度も通る)場合には、3次元インデックスの「良い」候補になるかも知れません。データセットの全ての次元で大きなばらつきが出るためです。
09/15/2022
User avatar yellow73

Translation uploaded
A set of DEM points would probably be a *poor* candidate for a 3-D index, since the queries would usually be extracting a 2-D box of points, and rarely attempting to select a Z-slice of points.
DEMポイント集合は3次元インデックスの「不十分な」候補になるかも知れません。クエリはがポイントの2次元ボックスを展開するためです。まれに抽出することになり、ポイント集合をZ値で薄切りにしようとすることがありますはほぼありません。
09/15/2022
User avatar yellow73

Translation uploaded
Indexes are only useful when they allow the database to drastically reduce the number of return rows as a result of a WHERE condition. For a higher dimension index to be useful, the data must cover a wide range of that dimension, relative to the kinds of queries you are constructing.
インデックスはデータベースがWHERE条件の結果として返す行を大幅に削減するためには場合に限って使えます。高次元インデックスを有効に使用するには、データは次元の広い範囲にわたり、ユーザが作ろうとするクエリの種類ユーザが作ろうとするクエリに対してデータが次元の広い範囲にわたっていなければなりません。
09/15/2022
User avatar yellow73

Translation uploaded
Once you have data in higher dimensions it may make sense to index it. However, you should think carefully about the distribution of your data in all dimensions before applying a multi-dimensional index.
高次元データを持つと、インデックスを有効にすることになるかも知れません。しかしながら、多次元インデックスを適用する前に全ての次元内でのデータの分布について十分に検討するべきです。
09/15/2022
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