{"id":9128,"date":"2025-12-16T07:02:24","date_gmt":"2025-12-16T07:02:24","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/16\/geospatial-exploratory-data-analysis-with-geopandas-and-duckdb\/"},"modified":"2025-12-16T07:02:24","modified_gmt":"2025-12-16T07:02:24","slug":"geospatial-exploratory-data-analysis-with-geopandas-and-duckdb","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/16\/geospatial-exploratory-data-analysis-with-geopandas-and-duckdb\/","title":{"rendered":"Geospatial exploratory data analysis with GeoPandas and\u00a0DuckDB"},"content":{"rendered":"<p>    Geospatial exploratory data analysis with GeoPandas and\u00a0DuckDB<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>In this article, I\u2019ll show you how to use two popular Python libraries to carry out some geospatial analysis of traffic accident data within the UK. I was a relatively early adopter of DuckDB, the fast OLAP database, after it became available, but only recently realised that, through an extension, it offered a large number [\u2026]<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/geospatial-exploratory-data-analysis-with-geopandas-and-duckdb\/\">Geospatial exploratory data analysis with GeoPandas and\u00a0DuckDB<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Thomas Reid<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/geospatial-exploratory-data-analysis-with-geopandas-and-duckdb\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Geospatial exploratory data analysis with GeoPandas and\u00a0DuckDB In this article, I\u2019ll show you how to use two popular Python libraries to carry out some geospatial analysis of traffic accident data within the UK. I was a relatively early adopter of DuckDB, the fast OLAP database, after it became available, but only recently realised that, through [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,4434,401,82,1978,160,157,238,280],"tags":[232,84,2690],"class_list":["post-9128","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-data-exploration","category-data-engineering","category-data-visualization","category-geo-analytics","category-programming","category-python","category-statistics","category-technology","tag-analysis","tag-data","tag-geospatial"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9128"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=9128"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9128\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=9128"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=9128"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=9128"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}