"},{"ID":317582,"user_firstname":"Phoebe","user_lastname":"Gelbard","nickname":"Phoebe Gelbard","user_nicename":"phoebe-gelbard","display_name":"Phoebe Gelbard","user_email":"pgelbard@esri.com","user_url":"","user_registered":"2022-08-31 15:08:30","user_description":"(she\/they) Phoebe Gelbard is a Product Engineering Writer for ArcGIS Online and ArcGIS Data. With a background in sustainability, food systems, and GIS, Phoebe enjoys creating maps that engage people with their community. In their free time, Phoebe can be found biking, baking bread, and pursuing all kinds of type II fun.","user_avatar":"
"}],"short_description":"Learn how to use ArcGIS Tapestry data in a suitability analysis, allowing you to locate more donors for a campaign.","flexible_content":[{"acf_fc_layout":"content","content":"If you\u2019ve been following along with this series, you already know how ArcGIS Tapestry data can be used to understand community dynamics and demographics \u2013 people’s lifestyles, their spending patterns, and household composition.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2974459,"id":2974459,"title":"tapestry_target_marketing","filename":"tapestry_target_marketing-scaled.png","filesize":2469060,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-scaled.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/tapestry_target_marketing","alt":"Map of ZIP codes in Los Angeles, California color coded by target group","author":"317582","description":"","caption":"Output layers from the Target Marketing workflow can help you prioritize outreach based on the predominant ArcGIS Tapestry segment in a given area. ","name":"tapestry_target_marketing","status":"inherit","uploaded_to":2969324,"date":"2026-07-01 18:09:56","modified":"2026-07-02 19:14:01","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":2560,"height":1606,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-scaled.png","medium-width":416,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-scaled.png","medium_large-width":768,"medium_large-height":482,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-scaled.png","large-width":1722,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-1536x963.png","1536x1536-width":1536,"1536x1536-height":963,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-2048x1285.png","2048x2048-width":2048,"2048x2048-height":1285,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-741x465.png","card_image-width":741,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-1722x1080.png","wide_image-width":1722,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/tapestry_target_marketing-scaled.png"},{"acf_fc_layout":"content","content":"
But getting to know who your customers are is only half the challenge. The real insight happens when you start asking where to find more of them, which is where suitability analysis can help. In this article, we\u2019ll walk through how ArcGIS Business Analyst Pro\u2019s suitability analysis lets you identify a successful area for fundraising and create a ranked, scored map of your best potential markets \u2013 all in one workflow.<\/p>\n
This article is the third in a series aimed at diving into the world of ArcGIS Tapestry using ArcGIS Business Analyst Pro, an extension to ArcGIS Pro. If you missed it, check out ArcGIS Tapestry in Business Analyst Pro: Identify target markets<\/a> and ArcGIS Tapestry in Business Analyst Pro: The basics<\/a> to learn more.<\/p>\n At its core, suitability analysis is about finding places that resemble our best-performing locations or audiences. In Business Analyst Pro, this means combining multiple variables \u2013 demographic, behavioral, and geographic \u2013 into a single map layer that highlights the areas that best match our ideal conditions.<\/p>\n The process starts by defining what \u201csuitable\u201d means for our specific goal. Imagine we work for a statewide conservation nonprofit looking to expand our reach to sustaining donors: our goal is to identify priority neighborhoods for fundraising outreach based on high concentrations of Tapestry segments known for civic engagement and discretionary income, within state boundaries, and outside areas already targeted by other outreach efforts. Each of these inputs becomes a factor in our analysis.<\/p>\n It’s important to note that when choosing variables, we’ll need to use numeric variables \u2013 for example, “2025 City Greens (K6) Tapestry Adult Population (Esri).\u201d Categorical variables, such as \u201c2025 Dominant Tapestry Segment (Esri),\u201d can\u2019t be ranked or weighted, so they can\u2019t be used as criteria in our model.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2974684,"id":2974684,"title":"tapestry_variable_example","filename":"tapestry_variable_example.png","filesize":178073,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/tapestry_variable_example","alt":"List of Tapestry Adults variables found in the data browser","author":"317582","description":"","caption":"","name":"tapestry_variable_example","status":"inherit","uploaded_to":2969324,"date":"2026-07-02 16:10:52","modified":"2026-07-08 20:18:15","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":1180,"height":645,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example.png","medium-width":464,"medium-height":254,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example.png","medium_large-width":768,"medium_large-height":420,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example.png","large-width":1180,"large-height":645,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example.png","1536x1536-width":1180,"1536x1536-height":645,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example.png","2048x2048-width":1180,"2048x2048-height":645,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example-826x452.png","card_image-width":826,"card_image-height":452,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example.png","wide_image-width":1180,"wide_image-height":645}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_variable_example.png"},{"acf_fc_layout":"content","content":" From there, variables are standardized and ranked so they can be compared on the same scale. This step is critical because our inputs likely come in different units, such as percentages, counts, or indexes, and need to be normalized before they can be meaningfully combined.<\/p>\n Once standardized, each variable is assigned a weight to reflect its relative importance. Not all criteria contribute equally, and weighting allows us to emphasize what matters most. In this fundraising outreach scenario, Tapestry alignment might carry more weight than geographic proximity to existing donors, but both still factor into the final score.<\/p>\n The weighted variables are then combined to create a composite suitability score for each geographic unit, allowing us to identify and prioritize areas with higher scores, as these most closely match our ideal conditions \u2013 in this case, places most likely to yield new donors with a genuine connection to conservation.<\/p>\n Something that makes suitability analysis uniquely powerful is its flexibility. You get to adjust the criteria, refine weighting, and iterate quickly to test different scenarios, turning the analysis into a dynamic decision-making process with actionable results rather than a one-time output.<\/p>\n In the previous article, we used Tapestry to identify which neighborhoods had the highest concentration of our target segments. These represented the communities most likely to benefit from a mobile health clinic.<\/p>\n Suitability analysis takes that logic a step further: rather than simply flagging where our target segments are located, it helps us evaluate and rank candidate areas based on a combination of criteria, with Tapestry as one of several inputs. Think of it as moving from \u201cwho lives here?\u201d to \u201cwhere should we focus, given everything we know?\u201d<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2974748,"id":2974748,"title":"tapestry_vs_suitability","filename":"tapestry_vs_suitability.png","filesize":619413,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/tapestry_vs_suitability","alt":"Comparison of a map showing ZIP codes color-coded by dominant Tapestry segment and ZIP codes in graduated colors reflecting suitability score","author":"317582","description":"","caption":"Mapping ZIP codes by the dominant Tapestry segment (left) shows where different types of people live, and mapping suitability scores (right) shows you where people who meet a set of criteria that you define are concentrated. ","name":"tapestry_vs_suitability","status":"inherit","uploaded_to":2969324,"date":"2026-07-02 18:45:07","modified":"2026-07-02 19:48:12","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":2112,"height":794,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability.png","medium-width":464,"medium-height":174,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability.png","medium_large-width":768,"medium_large-height":289,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability.png","large-width":1920,"large-height":722,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability-1536x577.png","1536x1536-width":1536,"1536x1536-height":577,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability-2048x770.png","2048x2048-width":2048,"2048x2048-height":770,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability-826x311.png","card_image-width":826,"card_image-height":311,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability-1920x722.png","wide_image-width":1920,"wide_image-height":722}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/07\/tapestry_vs_suitability.png"},{"acf_fc_layout":"content","content":" For a statewide conservation nonprofit trying to expand its donor base, that distinction matters. We already know our most reliable donors tend to cluster near a major wildlife preserve, and that these households have strong ties to the outdoors, conservation-minded values, and the discretionary income to allocate resources to them.<\/p>\n Suitability analysis lets us take that profile, translate it into Tapestry segment population or household counts, and bring those counts into the same model as other factors that paint a holistic picture of our potential donor base. Each input is weighted by importance, and the result is a prioritized map of ZIP codes across the state where the right conditions converge \u2013 not just demographically similar areas, but the ones where our time and resources for outreach can be used most effectively.<\/p>\n The exciting thing about suitability analysis is that the workflow is designed to calculate the most suitable location for our needs\u2014and we <\/em>define what \u201csuitable\u201d means. As such, there are infinite customization options for this workflow and many ways to incorporate ArcGIS Tapestry data.<\/p>\n While suitability analysis is often used for site selection in the real estate market, or resource allocation in the public health space, its malleability invites other uses as well. For example, imagine we work at a nonprofit that relies on donor contributions to fund a wildlife sanctuary. We want to reach out and solicit support from people who are likely to donate to our organization.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969331,"id":2969331,"title":"Image 0 - bear logo","filename":"Image-0-bear-logo.png","filesize":77163,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-0-bear-logo","alt":"Fictional wildlife sanctuary","author":"78361","description":"","caption":"","name":"image-0-bear-logo","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:35:02","modified":"2026-06-08 23:35:18","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":400,"height":354,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png","medium-width":295,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png","medium_large-width":400,"medium_large-height":354,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png","large-width":400,"large-height":354,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png","1536x1536-width":400,"1536x1536-height":354,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png","2048x2048-width":400,"2048x2048-height":354,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png","card_image-width":400,"card_image-height":354,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png","wide_image-width":400,"wide_image-height":354}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-0-bear-logo.png"},{"acf_fc_layout":"content","content":" How will we find these mythological potential donors? Well, we know that we currently receive the majority of our individual contributions from people in one ZIP code: the ZIP code where the sanctuary is located. We reason that, if we can find people who are similar to our current donors, we can perhaps tap into a new pool of funding.<\/p>\n We have two things going for us: we know who our best donors are, and we have access to demographic data that will guide us to find more of them. ArcGIS Tapestry data and suitability analysis are the perfect tools for this challenge. Using Tapestry data does more than map demographic data points\u2014it lets us assess our donors as more three-dimensional characters, and we can focus on locating others in our state with the same values, resources, and interests. At the end, we\u2019ll have a ranking of each ZIP code in the state, based on how similar they are to our \u201cbest donor\u201d qualities. Let\u2019s dive in!<\/p>\n <\/p>\n So how do we figure out which areas in our state contain populations similar to our current donors? We’ll start by quickly mapping all the ZIP codes in our state using the Generate Standard Geography Trade Areas<\/a> tool. This layer is handy for the rest of the analysis, so name it something eye-catching like Minnesota_ZIPs.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969333,"id":2969333,"title":"Image 2 - Minnesota ZIP codes","filename":"Image-2-Minnesota-ZIP-codes.png","filesize":669072,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-2-minnesota-zip-codes","alt":"Minnesota ZIP codes","author":"78361","description":"","caption":"","name":"image-2-minnesota-zip-codes","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:45:34","modified":"2026-06-08 23:45:41","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":1526,"height":833,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes.png","medium-width":464,"medium-height":253,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes.png","medium_large-width":768,"medium_large-height":419,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes.png","large-width":1526,"large-height":833,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes.png","1536x1536-width":1526,"1536x1536-height":833,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes.png","2048x2048-width":1526,"2048x2048-height":833,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes-826x451.png","card_image-width":826,"card_image-height":451,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes.png","wide_image-width":1526,"wide_image-height":833}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-2-Minnesota-ZIP-codes.png"},{"acf_fc_layout":"content","content":" Now we\u2019ll run an infographic to learn the most important bit of information: which Tapestry segment predominates in the ZIP code where the sanctuary is located. On the Map <\/strong>tab in the Inquiry <\/strong>group, click Infographics<\/strong>, then click the ZIP code where the sanctuary is located.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969334,"id":2969334,"title":"Image 3 - Infographics button","filename":"Image-3-Infographics-button.png","filesize":112973,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-3-infographics-button","alt":"Infographics button","author":"78361","description":"","caption":"","name":"image-3-infographics-button","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:46:44","modified":"2026-06-08 23:46:48","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":596,"height":353,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png","medium-width":441,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png","medium_large-width":596,"medium_large-height":353,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png","large-width":596,"large-height":353,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png","1536x1536-width":596,"1536x1536-height":353,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png","2048x2048-width":596,"2048x2048-height":353,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png","card_image-width":596,"card_image-height":353,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png","wide_image-width":596,"wide_image-height":353}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-3-Infographics-button.png"},{"acf_fc_layout":"content","content":" In the infographic window, change the template to Tapestry Profile<\/strong>. This infographic displays key demographic indicators\u2014such as population and income stats\u2014as well as a breakdown of the most predominant Tapestry segments in the area.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969335,"id":2969335,"title":"Image 4 - Tapestry Profile infographic","filename":"Image-4-Tapestry-Profile-infographic.png","filesize":310272,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-4-tapestry-profile-infographic","alt":"Tapestry Profile infographic","author":"78361","description":"","caption":"","name":"image-4-tapestry-profile-infographic","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:47:22","modified":"2026-06-08 23:47:27","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":1920,"height":1032,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic.png","medium-width":464,"medium-height":249,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic.png","medium_large-width":768,"medium_large-height":413,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic.png","large-width":1920,"large-height":1032,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic-1536x826.png","1536x1536-width":1536,"1536x1536-height":826,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic.png","2048x2048-width":1920,"2048x2048-height":1032,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic-826x444.png","card_image-width":826,"card_image-height":444,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic.png","wide_image-width":1920,"wide_image-height":1032}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-4-Tapestry-Profile-infographic.png"},{"acf_fc_layout":"content","content":" From the infographic, it is easy to see that almost every household in this area is part of the I5 Rural Resort Dwellers<\/a> segment. A quick look at the ArcGIS Tapestry data documentation for the segment reveals helpful insights:<\/p>\n"},{"acf_fc_layout":"quote","author_name":"ArcGIS Tapestry","author_profession_organization":"I5: Rural Resort Dwellers","image":false,"text":"Neighborhoods in this segment are distributed throughout the country and are concentrated in resort locations and areas with seasonal recreation. With approximately half of the population aged 55 and over, the senior age dependency rate is high. Nearly half of households are comprised of married couples without children. While most of this segment is rural and remote, some communities are within commuting distance (though often long commutes) of major urban job centers. Residents tend to have skilled jobs in construction and manufacturing. Rates of self-employment and government employment are higher than average, and there is a notable veteran population. There is a high number of second homes used for recreation, with one in three housing units designated for seasonal or occasional use."},{"acf_fc_layout":"content","content":" Armed with a clear idea of what our donors are like, we can now seek areas that are demographically similar using suitability analysis<\/a> and its target site<\/a> feature. Once we launch the workflow from the Business Analysis<\/strong> menu and create a suitability analysis layer to work in, our Minnesota ZIP codes are now \u201ccandidate sites\u201d for the analysis.<\/p>\n Now we\u2019ll add criteria that determine the \u201csuitability\u201d of each ZIP code for our fundraising drive. The juiciest nugget is the knowledge of the Tapestry segment we\u2019re looking for, which encapsulates all sorts of qualities the describe a \u201ctype\u201d of person we\u2019re looking for. In addition, we\u2019ll add three index variables to ascertain areas with unusually high nature nonprofit contribution potential:<\/p>\n Right away, the map shows a breakdown of these criteria throughout the state, ranking each ZIP code by how much of the criteria it contains. This is good intel! But we\u2019re going to make it even more useful by setting our home ZIP code, the one where we know <\/em>we\u2019re doing well in our fundraising efforts, as the target site.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969337,"id":2969337,"title":"Setting target site","filename":"Setting-target-site.png","filesize":21609,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/setting-target-site","alt":"","author":"78361","description":"","caption":"","name":"setting-target-site","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:51:09","modified":"2026-06-08 23:51:19","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":243,"height":174,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site-213x174.png","thumbnail-width":213,"thumbnail-height":174,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png","medium-width":243,"medium-height":174,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png","medium_large-width":243,"medium_large-height":174,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png","large-width":243,"large-height":174,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png","1536x1536-width":243,"1536x1536-height":174,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png","2048x2048-width":243,"2048x2048-height":174,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png","card_image-width":243,"card_image-height":174,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png","wide_image-width":243,"wide_image-height":174}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Setting-target-site.png"},{"acf_fc_layout":"content","content":" Once we set the ZIP code as the target site, we see a very <\/em>red map, indicating a great number of counties with similar traits to our home ZIP.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969338,"id":2969338,"title":"Image 6 - Target site in suitability analysis","filename":"Image-6-Target-site-in-suitability-analysis.png","filesize":651825,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-6-target-site-in-suitability-analysis","alt":"","author":"78361","description":"","caption":"","name":"image-6-target-site-in-suitability-analysis","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:51:55","modified":"2026-06-08 23:52:00","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":1405,"height":861,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis.png","medium-width":426,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis.png","medium_large-width":768,"medium_large-height":471,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis.png","large-width":1405,"large-height":861,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis.png","1536x1536-width":1405,"1536x1536-height":861,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis.png","2048x2048-width":1405,"2048x2048-height":861,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis-759x465.png","card_image-width":759,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis.png","wide_image-width":1405,"wide_image-height":861}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-6-Target-site-in-suitability-analysis.png"},{"acf_fc_layout":"content","content":" The map shows that in the north of Minnesota, there are plenty of spots with populations similar to the one we do well with. But what if we want to up our chances of raising donations for the wildlife refuge? We can do this by changing the target site\u2019s influence to find the same amount of Tapestry households with I5 as our target site, but higher amounts of the other variables.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969339,"id":2969339,"title":"Image 7 - Suitability influences","filename":"Image-7-Suitability-influences.png","filesize":716429,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-7-suitability-influences","alt":"Suitability influences","author":"78361","description":"","caption":"","name":"image-7-suitability-influences","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:52:26","modified":"2026-06-08 23:52:32","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":1586,"height":858,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences.png","medium-width":464,"medium-height":251,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences.png","medium_large-width":768,"medium_large-height":415,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences.png","large-width":1586,"large-height":858,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences-1536x831.png","1536x1536-width":1536,"1536x1536-height":831,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences.png","2048x2048-width":1586,"2048x2048-height":858,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences-826x447.png","card_image-width":826,"card_image-height":447,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences.png","wide_image-width":1586,"wide_image-height":858}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-7-Suitability-influences.png"},{"acf_fc_layout":"content","content":" To make sure we\u2019re focusing on finding our target Tapestry segment, let\u2019s pop over to the suitability analysis Settings <\/strong>tab and adjust the weighting, so that the Tapestry variable is weighted the heaviest, at 50%.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969341,"id":2969341,"title":"Image 8 - Criteria weighting","filename":"Image-8-Criteria-weighting.png","filesize":23487,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-8-criteria-weighting","alt":"Weighting criteria","author":"78361","description":"","caption":"","name":"image-8-criteria-weighting","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:53:01","modified":"2026-06-08 23:53:06","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":387,"height":551,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting.png","medium-width":183,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting.png","medium_large-width":387,"medium_large-height":551,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting.png","large-width":387,"large-height":551,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting.png","1536x1536-width":387,"1536x1536-height":551,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting.png","2048x2048-width":387,"2048x2048-height":551,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting-327x465.png","card_image-width":327,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting.png","wide_image-width":387,"wide_image-height":551}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-8-Criteria-weighting.png"},{"acf_fc_layout":"content","content":" While we\u2019re here, we\u2019ll also go into the Scoring <\/strong>section and enable filtering, so we see only the top five ZIP codes in the analysis:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969342,"id":2969342,"title":"Image 9 - Filtering","filename":"Image-9-Filtering.png","filesize":18850,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-9-filtering","alt":"","author":"78361","description":"","caption":"","name":"image-9-filtering","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:53:33","modified":"2026-06-08 23:53:37","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":391,"height":587,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering.png","medium-width":174,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering.png","medium_large-width":391,"medium_large-height":587,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering.png","large-width":391,"large-height":587,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering.png","1536x1536-width":391,"1536x1536-height":587,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering.png","2048x2048-width":391,"2048x2048-height":587,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering-310x465.png","card_image-width":310,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering.png","wide_image-width":391,"wide_image-height":587}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-9-Filtering.png"},{"acf_fc_layout":"content","content":" The map now displays the five ZIP codes that contain large numbers of households that are part of the I5 Tapestry segment, and good demographic indication that they\u2019ll be amenable to donating to the preserve. There are a cluster of ZIP codes in the north-central part of Minnesota that would be good to target:<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969343,"id":2969343,"title":"Image 10 - Top five locations","filename":"Image-10-Top-five-locations.png","filesize":541523,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-10-top-five-locations","alt":"Top five locations for fundraising","author":"78361","description":"","caption":"","name":"image-10-top-five-locations","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:54:03","modified":"2026-06-08 23:54:07","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":1186,"height":806,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations.png","medium-width":384,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations.png","medium_large-width":768,"medium_large-height":522,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations.png","large-width":1186,"large-height":806,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations.png","1536x1536-width":1186,"1536x1536-height":806,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations.png","2048x2048-width":1186,"2048x2048-height":806,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations-684x465.png","card_image-width":684,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations.png","wide_image-width":1186,"wide_image-height":806}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-10-Top-five-locations.png"},{"acf_fc_layout":"content","content":" To learn more about these areas in a flash, we\u2019ll use the ArcGIS Pro Infographics tool<\/a> again to view a profile of the locations. A comparison of locations in the cluster of ZIPs, with our target site added in as the benchmark, reveals many useful insights, including the Tapestry makeup of these areas. It might behoove us to also tailor our outreach style to the J4 Silver and Gold Tapestry segment as well, given that this segment makes up a considerable share of our potential donor base by household count.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2969345,"id":2969345,"title":"Image 11 - Comparison infographic","filename":"Image-11-Comparison-infographic.png","filesize":256277,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/arcgis-tapestry-in-arcgis-business-analyst-pro-suitability-analysis\/image-11-comparison-infographic","alt":"Comparison infographic","author":"78361","description":"","caption":"","name":"image-11-comparison-infographic","status":"inherit","uploaded_to":2969324,"date":"2026-06-08 23:54:46","modified":"2026-07-08 20:25:09","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":1640,"height":768,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic.png","medium-width":464,"medium-height":217,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic.png","medium_large-width":768,"medium_large-height":360,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic.png","large-width":1640,"large-height":768,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic-1536x719.png","1536x1536-width":1536,"1536x1536-height":719,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic.png","2048x2048-width":1640,"2048x2048-height":768,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic-826x387.png","card_image-width":826,"card_image-height":387,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic.png","wide_image-width":1640,"wide_image-height":768}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Image-11-Comparison-infographic.png"},{"acf_fc_layout":"content","content":" A Tapestry-focused suitability analysis has provided our wildlife preserve with ample fundraising opportunities and a sharpened focus for outreach. ArcGIS Tapestry data offers a unique mix of demographic, lifestyle, and spending insights that assure us that we’re extending giving opportunities to the right people.<\/p>\n <\/p>\n This article uses ArcGIS Business Analyst Pro 3.7, as well as the ArcGIS Tapestry, Esri Market Potential, and Esri Updated Demographics datasets.<\/em><\/p>\n <\/p>\n To continue your Business Analyst journey, visit the following resources:<\/p>\nHow does suitability analysis work?<\/h2>\n
Where does ArcGIS Tapestry fit into suitability analysis?<\/h2>\n
Who are our donors?<\/h2>\n
Where are other possible donors?<\/h2>\n
\n
Resources<\/h2>\n
\n