{"id":2527682,"date":"2025-03-10T06:00:43","date_gmt":"2025-03-10T13:00:43","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2527682"},"modified":"2025-10-06T10:47:08","modified_gmt":"2025-10-06T17:47:08","slug":"spatial-data-science-using-arcgis-notebooks","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/spatial-data-science-using-arcgis-notebooks","title":{"rendered":"Spatial data science using ArcGIS Notebooks Blog 1: Mapping Gentrification in US Cities"},"author":154341,"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],"tags":[387782,555752,760452,24341,759592],"industry":[],"product":[36841,36561],"class_list":["post-2527682","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-arcgis-api-for-python","tag-arcgis-notebooks","tag-data-engineering","tag-python","tag-spatial-data-science","product-api-python","product-arcgis-pro"],"acf":{"authors":[{"ID":154341,"user_firstname":"Nicholas","user_lastname":"Giner","nickname":"Nick Giner","user_nicename":"nginer","display_name":"Nicholas Giner","user_email":"NGiner@esri.com","user_url":"","user_registered":"2021-01-07 14:31:25","user_description":"Nick Giner is a Product Manager for Spatial Analysis and Data Science. Prior to joining Esri in 2014, he completed Bachelor\u2019s and PhD degrees in Geography from Penn State University and Clark University, respectively. In his spare time, he likes to play guitar, golf, cook, cut the grass, and read\/watch shows about history.","user_avatar":""},{"ID":358272,"user_firstname":"Halle","user_lastname":"Martinucci","nickname":"Halle Martinucci","user_nicename":"hmartinucci","display_name":"Halle Martinucci","user_email":"hmartinucci@esri.com","user_url":"","user_registered":"2024-06-18 18:43:42","user_description":"Halle is an Associate Product Marketing Manager on Esri\u2019s Spatial Analytics & Data Science team.","user_avatar":""}],"short_description":"Use an ArcGIS Pro Notebook for a complete spatial data science workflow to map gentrification in US cities","flexible_content":[{"acf_fc_layout":"content","content":"

Introduction<\/strong><\/h2>\n

This blog series walks through the steps taken to replicate the recent research paper \u201cA small area index of gentrification, applied to New York City<\/a><\/strong>\u201d, from a 2022 issue of the International Journal of Geographical Information Science <\/em>(Johnson et al. 2022).\u00a0 Gentrification is commonly defined as a process by which the geography of urban areas is reshaped by an influx of wealthy businesses and residents.\u00a0 It is viewed positively by some as an economic strategy to attract new investment and real estate development, and negatively by others as it can drive up property values and displace local, often long-term residents and business owners.\u00a0 As such, it is an important topic of study for urban geographers, planners, and social scientists.<\/p>\n

In the paper, the authors collect US census tract data from two different time periods (2000 and 2020) about five gentrification indicators.\u00a0 These indicators were chosen by domain experts based on an extensive review of the gentrification literature.<\/p>\n

They then calculate how these indicators change between 2000 and 2020, combine them to create one overall gentrification index for each census tract, then map it for New York City.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2942083,"id":2942083,"title":"workflow_graphic_v2","filename":"workflow_graphic_v2.jpg","filesize":96038,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/spatial-data-science-using-arcgis-notebooks\/workflow_graphic_v2","alt":"","author":"154341","description":"","caption":"","name":"workflow_graphic_v2","status":"inherit","uploaded_to":2527682,"date":"2025-10-06 17:46:59","modified":"2025-10-06 17:46:59","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1456,"height":601,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2.jpg","medium-width":464,"medium-height":192,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2.jpg","medium_large-width":768,"medium_large-height":317,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2.jpg","large-width":1456,"large-height":601,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2.jpg","1536x1536-width":1456,"1536x1536-height":601,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2.jpg","2048x2048-width":1456,"2048x2048-height":601,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2-826x341.jpg","card_image-width":826,"card_image-height":341,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/03\/workflow_graphic_v2.jpg","wide_image-width":1456,"wide_image-height":601}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

What\u2019s next<\/strong><\/h2>\n

In the following blogs, I\u2019ll cover the data engineering, data exploration, machine learning, and spatial analysis techniques I used to replicate the methodology in New York City, then take it a step further and apply it to other US metropolitan areas such as Philadelphia, Seattle, Washington DC, and San Diego.<\/p>\n

Jump to blog #2<\/a><\/strong> to learn how to get all of the data together and start preparing it for analysis…<\/p>\n"},{"acf_fc_layout":"content","content":"

References (for the blog series)<\/strong><\/h2>\n

Johnson, G.D., Checker, M., Larson, S., Kodali, H., 2022. A small area index of gentrification, applied to New York City.\u00a0International Journal of Geographical Information Science<\/em>. 36(1), 137-157.<\/p>\n

Hair Jr., J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 1998.\u00a0Multivariate Data Analysis (5th Edition)<\/span>. Prentice-Hall, Inc., Upper Saddle River, NJ.<\/p>\n"},{"acf_fc_layout":"sidebar","content":"

Spatial data science in ArcGIS Pro Notebooks<\/h2>\n

Here are the links to all the blogs in the series:<\/p>\n