{"id":965771,"date":"2020-07-14T14:45:00","date_gmt":"2020-07-14T21:45:00","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=965771"},"modified":"2022-01-10T07:27:16","modified_gmt":"2022-01-10T15:27:16","slug":"introduction-to-spatial-and-temporal-analysis-of-civic-data","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-hub\/announcements\/introduction-to-spatial-and-temporal-analysis-of-civic-data","title":{"rendered":"Introduction to Spatial and Temporal analysis of Civic Data"},"author":7461,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341,37101,37131],"tags":[173712,204432,431792,31601,24341],"industry":[],"product":[36811],"class_list":["post-965771","blog","type-blog","status-publish","format-standard","hentry","category-analytics","category-announcements","category-constituent-engagement","tag-arcgis-hub","tag-civic-engagement","tag-civic-tech","tag-data-science","tag-python","product-arcgis-hub"],"acf":{"short_description":"Learn how to make sense of the spatial and temporal attributes in civic data From ArcGIS Hub using Python ","flexible_content":[{"acf_fc_layout":"content","content":"

Welcome to Week 1 of ArcGIS Hub’s Civic Analytics with Notebook Series. If you missed our\u00a0announcement<\/a>\u00a0of our new\u00a0Civic Analytics with Hub community,<\/strong> I invite you to check it out and join our community.<\/p>\n

Introduction to spatial and temporal analysis<\/strong><\/h3>\n

This week we start with notebooks that focus on exploring and understanding the spatial<\/a> and temporal<\/a> aspects of street crashes in Ottawa Canada in 2018. We also have a guide to walk you through the process of finding and using data<\/a> in notebooks.<\/p>\n

For those looking, here is a great interactive Python tutorial<\/a> and resource for the ArcGIS API for Python<\/a>. And if you are new to ArcGIS Notebooks and would like to learn how to get started, we have this step-by-step tutorial<\/a> for you.<\/p>\n

Spatial analysis<\/a> is how we understand the space around us and the world. It helps us solve complex location-analytics problems. A temporal understanding of data helps us see if patterns are consistent over time and to detect unusual patterns if any. We can check for the presence of cyclic patterns by time of day, day of the week, or season or we can compare spatial patterns that match socioeconomic or infrastructure and access. With these insights we can then advocate for improvements to services or civic infrastructure.<\/p>\n

We focus on fetching data and highlighting the distribution of crashes across space and time to gain a more visual perception of civic data beyond the usual tabular structure. Starting with a simple spatial analysis that gives us a bird’s eye view of the volume of crashes in Ottawa we proceed to aggregate them by each municipality to get a visual sense of high-impact zones. The temporal analysis aggregates and visualizes these crashes based on day, time of day and month of year giving us a visual summary of data condensed as shown below.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":938751,"id":938751,"title":"ottawa_density","filename":"ottawa_density.png","filesize":599751,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-hub\/announcements\/introducing-urban-analytics-with-arcgis-hub\/ottawa_density","alt":"spatial analysis","author":"7461","description":"","caption":"","name":"ottawa_density","status":"inherit","uploaded_to":937051,"date":"2020-07-14 17:27:03","modified":"2020-08-04 21:31:42","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1710,"height":559,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density.png","medium-width":464,"medium-height":152,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density.png","medium_large-width":768,"medium_large-height":251,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density.png","large-width":1710,"large-height":559,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density-1536x502.png","1536x1536-width":1536,"1536x1536-height":502,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density.png","2048x2048-width":1710,"2048x2048-height":559,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density-826x270.png","card_image-width":826,"card_image-height":270,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/ottawa_density.png","wide_image-width":1710,"wide_image-height":559}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/arcgishub.maps.arcgis.com\/home\/item.html?id=019c702ddded4b7198268b35bbfe55f2#preview"},{"acf_fc_layout":"image","image":{"ID":938761,"id":938761,"title":"temporal_pattern","filename":"temporal_pattern.png","filesize":51758,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-hub\/announcements\/introducing-urban-analytics-with-arcgis-hub\/temporal_pattern","alt":"chart_time","author":"7461","description":"","caption":"","name":"temporal_pattern","status":"inherit","uploaded_to":937051,"date":"2020-07-14 17:27:33","modified":"2020-07-14 17:27:45","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":481,"height":456,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern.png","medium-width":275,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern.png","medium_large-width":481,"medium_large-height":456,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern.png","large-width":481,"large-height":456,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern.png","1536x1536-width":481,"1536x1536-height":456,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern.png","2048x2048-width":481,"2048x2048-height":456,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern.png","card_image-width":481,"card_image-height":456,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/07\/temporal_pattern.png","wide_image-width":481,"wide_image-height":456}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/arcgishub.maps.arcgis.com\/home\/item.html?id=fd77ebf8bd1d4ea8bc116653501d7d61#preview"},{"acf_fc_layout":"content","content":"

You can participate in the discussion thread for this week’s notebooks on our Geonet thread<\/a>. Additionally, I welcome you to share your analysis notebooks and results of your experiments with our notebooks in our Geonet community. Use this community as a platform to share results from your experiments with these notebooks, exchange ideas, start discussions and get in touch with like minded community members to understand and explore civic data to new depths.<\/p>\n

Links to this week’s notebooks:<\/p>\n

    \n
  1. Understanding civic data spatially<\/a><\/li>\n
  2. Understanding civic data temporally<\/a><\/li>\n<\/ol>\n

    Click here<\/a> to learn more about Week 2 of Civic Analytics with Hub.<\/em><\/p>\n"}],"authors":[{"ID":7461,"user_firstname":"Manushi","user_lastname":"Majumdar","nickname":"Manushi Majumdar","user_nicename":"mmajumdar_dcdev","display_name":"Manushi Majumdar","user_email":"MMajumdar@esri.com","user_url":"","user_registered":"2018-03-21 18:21:20","user_description":"Product Engineer - Applied Data Science with ArcGIS API for Python. Or in other words, a (Data, Maps, Analyses, Python, Books) Nerd.","user_avatar":""}],"related_articles":"","card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/08\/banner-civica.jpg","wide_image":false},"yoast_head":"\nIntroduction to Spatial and Temporal analysis of Civic Data<\/title>\n<meta name=\"description\" content=\"Learn how to make sense of the spatial and temporal attributes in civic data From ArcGIS Hub using Python\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-hub\/announcements\/introduction-to-spatial-and-temporal-analysis-of-civic-data\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Introduction to Spatial and Temporal analysis of Civic Data\" \/>\n<meta property=\"og:description\" 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