"}],"short_description":"Richard Zhang's summer internship experience with the Raster Analysis team at Esri.","flexible_content":[{"acf_fc_layout":"content","content":"T<\/span>he world of GIS <\/span>first <\/span>became known <\/span>to me<\/span> when I was in a spatial data science course while earning my undergraduate degree towards Data Science at UC San Diego.<\/span> I <\/span>began<\/span> with <\/span>using <\/span>ArcGIS<\/span> Online <\/span>APIs, and then move<\/span>d<\/span> forward to ArcGIS Pro,<\/span> for which I <\/span>had to<\/span> prepare<\/span> scripts in advance <\/span>in order to<\/span> get everything tested during the 1-hour session on the <\/span>school\u2019s virtual <\/span>machine<\/span>.\u00a0<\/span> That limitation didn\u2019t hold me back\u2014if anything, it drove me deeper. As I pored over documentation, each page revealed new dimensions of the geospatial world, unfolding its vast potential before me.<\/span> So<\/span> when I came across Esri\u2019s internship openings at a career fair, it felt like recognizing an old friend I had never met. Two months later, I interviewed with the company, and<\/span> received <\/span>an <\/span>offer <\/span>after earning my <\/span>Master\u2019s<\/span> degree in Analytics<\/span>. <\/span>That moment wasn\u2019t <\/span>simply <\/span>about <\/span>an offer<\/span>\u2014it was the gateway to a summer where long-held aspi<\/span><\/span>rations <\/span>would finally take shape<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n <\/p>\n"},{"acf_fc_layout":"content","content":" For my 12-week internship<\/span> from <\/span>May<\/span><\/span> to August of 2025<\/span>, I <\/span>completed <\/span>two<\/span> proj<\/span>ect<\/span>s<\/span> using ArcGIS Pro and <\/span>Python<\/span> scripts with <\/span>Arcpy<\/span>. <\/span>The main project of my internship <\/span>was <\/span>titled<\/span> Optimizing<\/span> Locations of Vertical Farms with Suitability Modeling. The idea was to <\/span>answer a pressing question for the future of urban food systems: where should <\/span>vertical farms<\/span>, an <\/span>modern kind of <\/span>agricultural facility that takes up the <\/span>entire<\/span> buildings in cities,<\/span>\u00a0<\/span>be placed to maximize their impact? Drawing on data about cropland availability, population distribution, housing affordability, and transportation infrastructure, <\/span>I create<\/span>d<\/span> a model that could identify both national-scale patterns and neighborhood-level opportunities. The final<\/span> suitability<\/span> map(Figure 1.) not only highlighted metropolitan regions where vertical farms could thrive but also revealed specific urban areas where they could address food insecurity\u2014such as sites in New York City that aligned closely with the city\u2019s <\/span><\/span>Food Supply Gap report<\/span><\/span><\/span><\/span><\/a>. <\/span>The project revealed me<\/span>\u00a0how GIS can bring together diverse datasets to create insights that are practical, targeted, and relevant to real-world needs.<\/span> <\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2930181,"id":2930181,"title":"VF_output_1","filename":"VF_output_1.png","filesize":1129620,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/analytics\/internship-with-the-spatial-analyst-team-at-esri-summer-2025\/vf_output_1","alt":"Vertical Farm Suitability Map showing more suitability in the green and less suitability in reds","author":"394161","description":"Vertical Farm Suitability Map showing more suitability in the green and less suitability in reds ","caption":"Fig 1. Vertical Farm Suitability Map showing more suitability in the green and less suitability in reds ","name":"vf_output_1","status":"inherit","uploaded_to":2929821,"date":"2025-08-14 22:43:14","modified":"2025-08-14 22:43:31","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":913,"height":707,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1.png","medium-width":337,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1.png","medium_large-width":768,"medium_large-height":595,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1.png","large-width":913,"large-height":707,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1.png","1536x1536-width":913,"1536x1536-height":707,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1.png","2048x2048-width":913,"2048x2048-height":707,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1-600x465.png","card_image-width":600,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/VF_output_1.png","wide_image-width":913,"wide_image-height":707}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" As part of the project deliverables, I chose to present my suitability analysis using ArcGIS <\/span>StoryMaps<\/span>, a powerful ArcGIS Online application for combining interactive maps with narrative text and visuals. You can <\/span>acc<\/span>e<\/span>ss<\/span> <\/span>the full report<\/span><\/span><\/a> through <\/span>the link or <\/span>directly <\/span>at the window below<\/span>.<\/span> Use the top navigation bar to jump directly to each <\/span>submodel<\/span>, or freely browse the embedded interactive maps to examine areas of interest across the nation.<\/span><\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"storymap","title":"Vertical Farm Suitability Report","description":"","static":false,"storymap_url":"https:\/\/arcg.is\/0uHKXD<\/a>"},{"acf_fc_layout":"content","content":" <\/p>\n For my second project<\/span>, I worked <\/span>on<\/span>\u00a0<\/span>Air Quality Interpolation with<\/span> Geographically Neural Network Weighted Regression (GNNWR<\/span>)<\/span>. The aim of the project is to demonstrate <\/span>how neural-network-based methods can enhance geographically weighted regression to model air quality more accurately. Using environmental variables such as temperature, wind, and vegetation indices, along with air quality monitoring data, I developed workflows to integrate site-observational data with raster layers, process predictors, and run the models for prediction and interpolation of the PM 2.5 particle concentration(Fig 2.).<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2930221,"id":2930221,"title":"Air_output_2","filename":"Air_output_2-scaled.jpg","filesize":485319,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/analytics\/internship-with-the-spatial-analyst-team-at-esri-summer-2025\/air_output_2","alt":"Geographically Neural Network Weighted Regression Air Interpolation Map showing PM2.5 Particle concentration from low(in blues) to high(in reds)","author":"394161","description":" Geographically Neural Network Weighted Regression Air Interpolation Map showing PM2.5 Particle concentration from low(in blues) to high(in reds) ","caption":"Fig 2. Geographically Neural Network Weighted Regression Air Interpolation Map showing PM2.5 Particle concentration from low(in blues) to high(in reds) ","name":"air_output_2","status":"inherit","uploaded_to":2929821,"date":"2025-08-14 23:20:04","modified":"2025-08-14 23:20:23","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":2560,"height":1978,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-scaled.jpg","medium-width":338,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-scaled.jpg","medium_large-width":768,"medium_large-height":593,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-scaled.jpg","large-width":1398,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-1536x1187.jpg","1536x1536-width":1536,"1536x1536-height":1187,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-2048x1583.jpg","2048x2048-width":2048,"2048x2048-height":1583,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-602x465.jpg","card_image-width":602,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Air_output_2-1398x1080.jpg","wide_image-width":1398,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" One of the biggest takeaways from this internship was learning how to work with large, complex datasets efficiently. Vertical farm suitability modeling required processing and integrating high-resolution population grids, agricultural data, and transportation layers\u2014datasets that were not only large in but also different in structure, resolution, and source. Managing these dataset, preparing them for analysis, and ensuring that processing workflows could handle the scale of computation taught me techniques that will be valuable in any future data-intensive projects.<\/span>\u00a0<\/span><\/p>\n Equally important was the realization that developing GIS tools goes far beyond writing functional scripts. It means cultivating a coherent and precise methodology that holds up across multidisciplinary contexts\u2014whether the subject matter is agriculture, transportation, environmental science, or urban planning. I learned to think not just about whether the code is functional but also whether the analytical design is robust, well-structured, and aligned with the needs of the end user.<\/span>\u00a0<\/span><\/p>\n <\/p>\n"},{"acf_fc_layout":"content","content":" The internship wasn\u2019t all heads-down analysis. I took part in Esri\u2019s Hackathon, working with other interns to brainstorm, design, and prototype a GIS solution in just two days. The rush of moving from idea to implementation with a new team reminded me how much creativity thrives under a little time pressure.<\/span>\u00a0<\/span><\/p>\n I also had the chance to attend the Esri User Conference at San Diego. Walking through the sessions and showcases, I was struck by the breadth of applications for GIS technology\u2014from conservation and public health to logistics and infrastructure\u2014and by the shared sense of purpose in using spatial analysis to address global challenges.<\/span>\u00a0<\/span><\/p>\n \u00a0<\/span><\/p>\n This summer gave me more than just new skills, it expanded my perspective on the role of GIS in solving complex problems. Through the vertical farming and air quality projects, I saw how spatial analysis can integrate data from vastly different domains into a unified framework for decision-making. From the hackathon and user conference, I experienced the energy and creativity of the broader GIS community. And through the daily work with my team, I came to appreciate the importance of precision, clarity, and collaboration in professional geospatial analysis.<\/span>\u00a0<\/span><\/p>\n As I look ahead, I\u2019m excited to carry forward what I\u2019ve learned: how to approach big challenges with structured thinking, how to manage and analyze complex data effectively, and how to design GIS workflows that are not just functional, but meaningful. This internship marked a step forward not only in my technical abilities, but in how I see myself contributing to the community of geospatial technologies.<\/span>\u00a0<\/span><\/p>\n \u00a0<\/span><\/p>\n References<\/span><\/b> Du, Z., Wang, Z., Wu, S., Zhang, F., & Liu, R. (2020). Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity.\u00a0International Journal of Geographical Information Science, 34(7), 1353-1377.<\/span><\/i>\u00a0<\/span><\/p>\n Yin, Z., Ding, J., Liu, Y., Wang, R., Wang, Y., Chen, Y., Qi, J., Wu, S., and Du, Z. (2024). GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity. Geoscientific Model Development, 17 (22), 8455\u20138468.<\/span><\/i>\u00a0<\/span><\/p>\n"}],"related_articles":"","show_article_image":true,"card_image":false,"wide_image":false},"yoast_head":"\nSummer Internship Projects<\/span><\/b>\u00a0<\/span><\/h4>\n
Taking GIS Beyond the Desk<\/span><\/b>\u00a0<\/span><\/h4>\n
Reflections and Looking Ahead<\/span><\/b>\u00a0<\/span><\/h4>\n
\n\u00a0<\/span><\/p>\n