September 2023
Intermediate to advanced
272 pages
4h 26m
Japanese
Content preview from Pythonによる地理空間データ分析 ―例題で学ぶロケーションインテリジェンスBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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10章Pythonによる気候データの測定
Pythonや地理空間データ分析の技術的なスキルや学習手順を習得することは重要ですが、コンテキストを提供したり、共有するストーリーを作ったりしない限り、すべては机上のデータにすぎません。
この最後の章では、Landsat（https://www.usgs.gov/landsat-missions）、China-Brazil Earth Resources Satellite（CBERS、https://www.eoportal.org/satellite-missions/cbers-1-2）、Sentinel（https://www.sentinel-hub.com/）からの衛星画像レイヤにアクセスして、時系列データを探索する3つのアプローチを調べます。地理空間データ分析のスキルを活用して、気候変動や森林破壊に関する問題を検討します。
空間モデリングは、地球の気温上昇と森林破壊のリアルタイムな状況を予測、監視する重要なツールです。その結果、これらの現象がもたらす結果を予測し、それに対して介入または準備することができるでしょう。
本節では、Pythonの強力なパッケージの中から、3つの例（Xarray、WTSS（Web Time Series Service）、FAR（Forest at Risk））を紹介します。これらは新しいツールのように見えますが、以前の章で依存パッケージとしてすでに利用しています。最後の例は、森林破壊の分析に使用する予測モデリング用に設計されたパッケージの統計的能力をより深く掘り下げたものです。付属のNotebook（https://github.com/datamongerbonny/geopy-notebooks/blob/main/10_wtss.ipynb ...
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