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Object(m.a)(t,e),Object(c.a)(t,[{key:"render",value:function(){var e=this,t=this.state.selectedOption,a=this.props.nameList,o=D()({options:a});return l.a.createElement("div",{className:"Main"},l.a.createElement("div",{id:"background-blur"}),l.a.createElement("div",{className:"content"},l.a.createElement("div",{id:"top"},l.a.createElement("div",{className:"box intro"},l.a.createElement("h1",null,"Townology"),l.a.createElement("h3",null,"A typology system for America\u2019s small towns"),l.a.createElement("p",null,"In 2019 a Sasaki team of data analysts and designers undertook a study to unpack the nuance in American small towns, to explore the varied character in these places through demographic, economic, and environmental data. 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They are extremely remote, with rugged topography but excellent environmental quality. Industries with strong ties to the land are most prevalent here out of all of the types, such as agriculture and mining. <br/>The average home value of Out West Outposts is $111,000, with an average household income of $41,000. The median age is 43.7, and average population size 1,357.",themes:[{title:"Defined by the Western region",text:"Almost all of these towns are located west of the Mississippi, with benefits of wide open spaces such as excellent access to natural amenities, as well as challenges, such as high drought risks. These towns also have the most rugged and varied topography.",img:Re.a,rightlabel:"Very Flat",leftlabel:"Very Rugged",variable:"RugIndexme",setMap:function(e){e.changeColorBy("RugIndexme"),e.setViewport({latitude:40.061,longitude:-105.024,zoom:4.75,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"The most remote places",text:"Out West Outposts are some of the most remote of all towns studied. They have extremely low connectivity scores, being the furthest from airports and higher education institutions, as well as major logistics facilities and other towns. Highly rugged terrain likely contributes to this remoteness.",img:Se.a,rightlabel:"Least Remote",leftlabel:"Most Remote",variable:"vor_area",setMap:function(e){e.changeColorBy("vor_area"),e.setViewport({latitude:38.96,longitude:-115.401,zoom:6,pitch:20,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Close to the land",text:"Agriculture and mining have the strongest industrial presence here compared to other town types, with the lowest manufacturing presence, but the economy is otherwise highly diverse. Despite the presence of agriculture, access to food is the lowest among all the types.",img:te.a,rightlabel:"Little Agriculture",leftlabel:"Lots of Agriculture",variable:"pc_ag",setMap:function(e){e.changeColorBy("pc_ag"),e.setViewport({latitude:38.902,longitude:-111.359,zoom:7,pitch:40,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Most representative places",text:"The following towns align most closely to the characteristics of this type: <ol><li>Stites, ID / 48.5%</li><li>Philipsburg, MT / 46.5%</li><li>Nucla, CO / 44.4%</li><li>Gettysburg, SD / 42.7%</li><li>Hobson, MT / 42.6%</li><li>Saratoga, WY / 42.6%</li><li>Kanosh, UT / 41.8%</li><li>Alturas, CA / 41.9%</li><li>Llano, TX / 41.4%</li><li>Emery, UT / 41.3%</li></ol>",img:z.a,className:"representative",setMap:function(e){e.changeColorBy("ind7_CL7_Z"),e.setViewport({latitude:38.299,longitude:-96.752,zoom:4,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}}]},"rustic-retreat":{id:6,logo:H.a,title:"Rustic Retreat",subtitle:"Escape to the country",text:"Distributed fairly evenly throughout the contiguous United States, Rustic Retreats are places of natural beauty, with the lowest percent of developed land. They exhibit a slower economy and quieter pace of life, with the lowest levels of new business starts and self-employment. These retreats also have the oldest populations, with the lowest percentage of residents under the age of 18 and highest above the age of 65.<br/>The average home value of Rustic Retreats is $143,000, with an average household income of $40,000. The median age is 47.8, and average population size 779.",themes:[{title:"Natural beauty abounds",text:"With the lowest residential density, the lowest percent of developed land, and the highest tree canopy coverage, these places exhibit an untouched, natural character.",img:fe.a,rightlabel:"Little Tree Canopy",leftlabel:"Lots of Tree Canopy",variable:"tree_mean",setMap:function(e){e.changeColorBy("tree_mean"),e.setViewport({latitude:38.413,longitude:-88.803,zoom:4.75,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Quieter pace of life",text:"Small towns can be economically bustling, but in the case of Rustic Retreats, the pace is a bit slower. These towns have the lowest GDP, percent of new business starts, and self-employment levels, with the highest vacancy levels.",img:ke.a,rightlabel:"Low GDP",leftlabel:"High GDP",variable:"gdp_avg",setMap:function(e){e.changeColorBy("gdp_avg"),e.setViewport({latitude:38.651,longitude:-81.361,zoom:6,pitch:20,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Oldest population",text:"Not only do these towns have the highest percentage of the population over the age of 65, but they also have the lowest percentage of working age residents and the lowest percentage of the population under the age of 18.",img:ve.a,rightlabel:"Few Adults over 65",leftlabel:"Many Adults over 65",variable:"pc_popo65",setMap:function(e){e.changeColorBy("pc_popo65"),e.setViewport({latitude:35.596,longitude:-94.583,zoom:7,pitch:40,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Most representative places",text:"The following towns align most closely to the characteristics of this type: <ol><li>Greers Ferry, AR / 51.0%</li><li>Evening Shade, OK / 46.5%</li><li>Jackson Lake, CO / 46.0%</li><li>Plover, IA / 45.6%</li><li>Kickapoo, KS / 43.9%</li><li>Gerster, MO / 42.4%</li><li>Big Arm, MT / 42.4%</li><li>Colony, OK / 41.8%</li><li>Montgomery Creek, CA / 41.8%</li><li>Holiday Pocono, PA / 41.6%</li></ol>",img:z.a,setMap:function(e){e.changeColorBy("ind7_CL7_Z"),e.setViewport({latitude:38.299,longitude:-96.752,zoom:4,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}}]},"shrinking-stead":{id:2,logo:L.a,title:"Shrinking Stead",subtitle:"A different side of the American dream\n",text:"With concentrations in the southeast United States, Shrinking Steads are defined by a difficult quality of life. These towns are shrinking on average, with high vacancy levels -- but they also have the worst scores of all the types for indicators like poverty, educational attainment, unemployment, and health. People of color have the highest representation in these places, pointing to a racial dynamic of inequality.<br/>The average home value of Shrinking Steads is $94,000, with an average household income of $35,000. The median age is 38.4, and average population size 3,333.",themes:[{title:"Small towns getting smaller",text:"These towns have the highest levels of outward migration, as well as indicators of transience such as high vacancy and lowest percentages of owner-occupied housing (indicating many renters).",img:Oe.a,rightlabel:"Outward Migration",leftlabel:"Inward Migration",variable:"migr_avg",setMap:function(e){e.changeColorBy("migr_avg"),e.setViewport({latitude:36.643,longitude:-89.558,zoom:4.75,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Difficult quality of life",text:"Many quality of life indicators here are low: Shrinking Steads have the worst poverty, educational attainment, and unemployment levels, as well as the lowest access to healthcare and worst population health.",img:re.a,rightlabel:"Low Poverty",leftlabel:"High Poverty",variable:"pc_poverty",setMap:function(e){e.changeColorBy("pc_poverty"),e.setViewport({latitude:36.014,longitude:-107.346,zoom:6,pitch:20,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Disproportionate effects for people of color",text:"These towns point toward a racial inequality trend in the nation, with these places also having the highest percentage of black and multi-racial populations, very high ethnic diversity overall, and the lowest percentage of white populations.",img:xe.a,rightlabel:"Small % Black Population",leftlabel:"Large % Black Population",variable:"pc_black",setMap:function(e){e.changeColorBy("pc_black"),e.setViewport({latitude:33.402,longitude:-90.177,zoom:7,pitch:40,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Most representative places",text:"The following towns align most closely to the characteristics of this type: <ol><li>Artesia, MS / 55.6%</li><li>Wittenberg, WI / 53.1%</li><li>Wautoma, WI / 51.6%</li><li>Ashdown, AR / 49.6%</li><li>Potts Camp, MS / 47.3%</li><li>Crofton, KY / 47.1%</li><li>Okeechobee, FL / 47.1%</li><li>Etowah, AR / 46.5%</li><li>Hodge, LA / 45.5%</li><li>Alma, GA / 45.4%</li></ol>",img:z.a,setMap:function(e){e.changeColorBy("ind7_CL7_Z"),e.setViewport({latitude:38.299,longitude:-96.752,zoom:4,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}}]},"provincial-patch":{id:3,logo:Q.a,title:"Provincial Patch",subtitle:"Mom-and-apple-pie average places",text:"Distributed heavily throughout the \u201cHeartland\u201d of America, these towns are distinguished by their remarkably average scores across multiple indicators. They are set apart from other types by low levels of diversity -- both ethnic and political diversity -- and are often politically conservative. These towns are, on average, neither excessively thriving nor struggling.<br/>The average home value of Provincial Patches is $107,000, with an average household income of $42,000. The median age is 43.0, and average population size 1,463.",themes:[{title:"Little cultural diversity",text:"On average, these towns have the highest percentage of white populations with lowest levels of ethnic diversity. They also have the lowest share of arts organizations per person.",img:ue.a,rightlabel:"Small % White Population",leftlabel:"Large % White Population",variable:"pc_white",setMap:function(e){e.changeColorBy("pc_white"),e.setViewport({latitude:38.929,longitude:-89.661,zoom:4.75,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Politically conservative and homogenous",text:"Provincial Patches tend to vote very Republican (in both the 2012 and 2016 elections) and have the lowest levels of political diversity.",img:ie.a,rightlabel:"Low Political Diversity",leftlabel:"High Political Diversity",variable:"pol_div2",setMap:function(e){e.changeColorBy("pol_div2"),e.setViewport({latitude:40.554,longitude:-96.037,zoom:6,pitch:20,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Overall, an average kind of place",text:"These towns scored middle-of-the-road for many variables, with slight extremes in that they have low access to natural amenities, low GDP, and relatively high self-employment.",img:$.a,rightlabel:"Low Natural Amenities Access",leftlabel:"High Natural Amenities Access",variable:"natam_sc",setMap:function(e){e.changeColorBy("natam_sc"),e.setViewport({latitude:43.073,longitude:-91.281,zoom:7,pitch:40,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Most representative places",text:"The following towns align most closely to the characteristics of this type: <ol><li>Viola, WI / 68.1%</li><li>Littleville, AL / 51.2%</li><li>Cedar Grove, IN / 50.3%</li><li>Clearfield, IA / 50.1%</li><li>Grant, AL / 49.3%</li><li>Homer, GA / 48.9%</li><li>Plainville, IN / 47.4%</li><li>Damascus, AR / 47.3%</li><li>Round Lake, MN / 46.9%</li><li>New Hope, AL / 46.7%</li></ol>",img:z.a,setMap:function(e){e.changeColorBy("ind7_CL7_Z"),e.setViewport({latitude:38.299,longitude:-96.752,zoom:4,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}}]},"vibrant-village":{id:4,logo:j.a,title:"Vibrant Village",subtitle:"Miniature urban centers",text:"Clustered closely around metropolitan areas, Vibrant Villages are the most urban of all the types and reflect characteristics shared with their larger metropolitan counterparts. They are highly connected and highly developed, with flat topography and high flood risks. They tend to be culturally vibrant, with high ethnic diversity and numbers of arts organizations, and generally identify as liberal-voting constituents.<br/>The average home value of Vibrant Villages is $192,000, with an average household income of $57,000. The median age is 38.5, and average population size 10,752.",themes:[{title:"Connected and accessible",text:"Vibrant Villages are the most connected of all of the types, with the closest proximity to airports, higher education institutions, and other towns. They also have the best access to food and broadband, and have the highest percentage of developed land.",img:oe.a,rightlabel:"Little Developed Land",leftlabel:"Lots of Developed Land",variable:"perc_dev2",setMap:function(e){e.changeColorBy("perc_dev2"),e.setViewport({latitude:40.23,longitude:-88.15,zoom:4.75,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Politically liberal",text:"Like the metropolitan regions that they are closely located near, these towns also identify as politically liberal, with the strongest record of Democratic voters in both the 2012 and 2016 elections.",img:de.a,rightlabel:"Democrat (2016 Election)",leftlabel:"Republican (2016 Election)",variable:"el2016_avg",setMap:function(e){e.changeColorBy("el2016_avg"),e.setViewport({latitude:37.298,longitude:-117.923,zoom:6,pitch:20,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Culturally vibrant",text:"These towns offer cultural vibrancy in the high number of arts organizations per person and high ethnic diversity -- while at the expense of natural amenities and environmental quality, both indicators in which these towns scored low.",img:Ne.a,rightlabel:"Low Ethnic Diversity",leftlabel:"High Ethnic Diversity",variable:"ethnic_div",setMap:function(e){e.changeColorBy("ethnic_div"),e.setViewport({latitude:40.533,longitude:-74.84,zoom:7,pitch:40,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Most representative places",text:"The following towns align most closely to the characteristics of this type: <ol><li>Ephrata, PA / 67.6%</li><li>Springdale, SC / 62.8%</li><li>Elizabethtown, PA / 59.5%</li><li>Canonsburg, PA / 56.5%</li><li>Fraser, MI / 56.5%</li><li>Central Gardens, TX / 56.4%</li><li>North Amityville, NY / 56.3%</li><li>Pearl, MS / 55.1%</li><li>Utica, MI / 54.5%</li><li>Washington, NJ / 53.9%</li></ol>",img:z.a,setMap:function(e){e.changeColorBy("ind7_CL7_Z"),e.setViewport({latitude:38.299,longitude:-96.752,zoom:4,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}}]},"blue-collar-burg":{id:1,logo:F.a,title:"Blue Collar Burg",subtitle:"Swing states and manufacturing",text:"Many of the Blue Collar Burgs are concentrated in the Rust and Corn Belts throughout the industrial north of the Midwest. These are growing, with the highest percentage of manufacturing industry presence, highest occupational diversity, and lowest inequality. They also have the highest political diversity, with many of the towns being located in swing states.<br/>The average home value of Blue Collar Burgs is $122,000, with an average household income of $52,000. The median age is 41.4, and average population size 1,743.",themes:[{title:"Manufacturing growth",text:"With the highest levels of inward growth, these towns also show the strongest presence of manufacturing industry amongst all of the types.",img:ge.a,rightlabel:"Little Manufacturing",leftlabel:"Lots of Manufacturing",variable:"pc_manu",setMap:function(e){e.changeColorBy("pc_manu"),e.setViewport({latitude:40.588,longitude:-88.531,zoom:4.75,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Swing state territory",text:"Many towns of this type are located in the traditional \u201cswing states\u201d of Ohio, Iowa, Minnesota, Pennsylvania, Wisconsin, and Michigan -- and the data reflects this political landscape, showing the highest levels of political diversity among all of the types.",img:Ae.a,rightlabel:"Democrat (2012 Election)",leftlabel:"Republican (2012 Election)",variable:"el2012_avg",setMap:function(e){e.changeColorBy("el2012_avg"),e.setViewport({latitude:42.635,longitude:-88.767,zoom:6,pitch:20,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Healthy economic quality",text:"These towns seem to offer a healthy economic quality to their residents, with the lowest levels of inequality. These towns also have the lowest percentage of big businesses (with more than 1,000 employees) and the highest occupational diversity, indicating perhaps a trend toward traditional mom-and-pop small businesses.",img:ce.a,rightlabel:"Low Inequality",leftlabel:"High Inequality",variable:"gini_ineq",setMap:function(e){e.changeColorBy("gini_ineq"),e.setViewport({latitude:40.872,longitude:-83.129,zoom:7,pitch:40,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Most representative places",text:"The following towns align most closely to the characteristics of this type: <ol><li>New Bloomfield, MO / 60.2%</li><li>London, OH / 52.2%</li><li>Stockbridge, MI / 51.7%</li><li>Edneyville, NC / 51.5%</li><li>Dorchester, IL / 51.4%</li><li>Bancroft, MI / 50.6%</li><li>Mineral City, OH / 50.5%</li><li>North Fairfield, OH / 50.2%</li><li>Carbonado, WA / 49.5%</li><li>Jones Creek, TX / 48.9%</li></ol>",img:z.a,setMap:function(e){e.changeColorBy("ind7_CL7_Z"),e.setViewport({latitude:38.299,longitude:-96.752,zoom:4,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}}]},"bucolic-burb":{id:5,logo:Y.a,title:"Bucolic Burb",subtitle:"Affluence in the suburbs",text:"The defining characteristic of Bucolic Burbs is affluence. These towns have much higher-than-average home values and income levels, as well as excellent quality of life indicators and post-industrial economies. They are located primarily in suburban areas, near metropolitan regions but not necessarily directly adjacent.<br/>The average home value of Bucolic Burbs is $365,000, with an average household income of $93,000. The median age is 43.5, and average population size 6,612.",themes:[{title:"Affluence is prevalent",text:"Home values in these towns is more than three times the average of all the other types (at $365,000), while household income levels are more than twice the average of all other types (at $93,000).",img:U.a,rightlabel:"Low Household Income",leftlabel:"High Household Income",variable:"md_HHinc",setMap:function(e){e.changeColorBy("md_HHinc"),e.setViewport({latitude:39.466,longitude:-83.477,zoom:4.75,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"High quality of life",text:"Residents of these towns have the highest educational attainment levels and tend to be the healthiest. These towns have the lowest levels of poverty, vacancy, and unemployment, as well as the highest percentage of owner-occupied housing.",img:Ie.a,rightlabel:"Unhealthy Population",leftlabel:"Healthy Population",variable:"hout_avg",setMap:function(e){e.changeColorBy("hout_avg"),e.setViewport({latitude:45.755,longitude:-118.509,zoom:6,pitch:20,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Post-industrial economy",text:"The economic makeup of these towns is post-industrial, with the lowest percentage of agriculture and mining industries and highest rates of new business starts. Conversely, these towns have the lowest occupational diversity, pointing perhaps to an economic monoculture.",img:Be.a,rightlabel:"Few New Business Starts",leftlabel:"Lots of New Business Starts",variable:"newbus_avg",setMap:function(e){e.changeColorBy("newbus_avg"),e.setViewport({latitude:31.49,longitude:-97.398,zoom:7,pitch:40,transitionInterpolator:new b.a,transitionDuration:3e3})}},{title:"Most representative places",text:"The following towns align most closely to the characteristics of this type: <ol><li>Wylie, TX / 61.7%</li><li>Orono, MN / 58.9%</li><li>Millis-Clicquot, MA / 55.7%</li><li>Woxhall, PA / 54.7%</li><li>Weatogue, CT / 54.3%</li><li>Kendall Park, NJ / 53.8%</li><li>Loma Linda, MO / 53.1%</li><li>Fayetteville, NY / 52.0%</li><li>Cottage Grove, MN / 51.8%</li><li>Summerfield, NC / 49.3%</li></ol>",img:z.a,setMap:function(e){e.changeColorBy("ind7_CL7_Z"),e.setViewport({latitude:38.299,longitude:-96.752,zoom:4,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3})}}]}},a}return Object(m.a)(t,e),Object(c.a)(t,[{key:"componentDidMount",value:function(){this.props.closePopup()}},{key:"componentDidUpdate",value:function(e,t,a){this.props.match.params.type!==e.match.params.type&&document.getElementById("top").scrollIntoView()}},{key:"render",value:function(){var e=this,t=function(e){return{__html:e}},a=this.details[this.props.match.params.type];return l.a.createElement("div",{className:"Detail"},l.a.createElement(K.FullPage,{scrollMode:"normal"},l.a.createElement(K.Slide,null,l.a.createElement("div",{id:"top",className:"wrapper"},l.a.createElement(G.a,null,l.a.createElement(J,{onVisible:function(){e.props.setViewport({latitude:35.059657720491046,longitude:-98.04486166802587,zoom:4,bearing:0}),e.props.changeFilter([a.id]),e.props.changeColorBy("ind7_CL7_Z")}},l.a.createElement("div",{className:"Hero"},l.a.createElement("div",{className:"box"},l.a.createElement("img",{alt:"Theme Hero",src:a.logo,className:"img"}),l.a.createElement("h1",null,a.title),l.a.createElement("h3",null,a.subtitle),l.a.createElement("p",{dangerouslySetInnerHTML:t(a.text)}),l.a.createElement("div",{className:"scroll"},l.a.createElement("i",{className:"retina-arrows-0007"}),l.a.createElement("h5",null,"Scroll to Explore"),l.a.createElement("i",{className:"retina-arrows-0007"})))))))),a.themes.map((function(a,o){return l.a.createElement(K.Slide,{key:"group"+o},l.a.createElement("div",{className:"wrapper"},l.a.createElement(G.a,{partialVisibility:!0},l.a.createElement(J,{onVisible:function(){return a.setMap(e.props)}},l.a.createElement("div",{className:"Group"},l.a.createElement("div",{className:"image"},l.a.createElement("img",{alt:"Theme",src:a.img})),l.a.createElement("div",{className:"copy box"},l.a.createElement("h3",null,a.title),l.a.createElement("p",{dangerouslySetInnerHTML:t(a.text)}),a.leftlabel&&l.a.createElement("div",{className:"legend-bar"},l.a.createElement("div",{className:"bar",style:{background:"linear-gradient(to left, ".concat(e.props.dataKeys[a.variable].colors[0],", ").concat(e.props.dataKeys[a.variable].colors[1],")")}}),l.a.createElement("h5",null,a.leftlabel),l.a.createElement("h5",null,a.rightlabel))))))))})),l.a.createElement(K.Slide,null,l.a.createElement("div",{className:"wrapper"},l.a.createElement(G.a,null,l.a.createElement(J,{onVisible:function(){e.setState({viewport:{latitude:38.299,longitude:-96.752,zoom:4,pitch:0,transitionInterpolator:new b.a,transitionDuration:3e3}})}},l.a.createElement("div",{id:"all-types"},l.a.createElement("div",{id:"explore-more"},l.a.createElement("div",{className:"box ss-button"},l.a.createElement("h4",null,"Explore another type"))),l.a.createElement("div",{id:"cards"},je.map((function(e){return l.a.createElement(u.b,{to:"/type/".concat(e.id),key:e.id,className:"box card",id:e.id},l.a.createElement("div",{className:"img"}),l.a.createElement("h1",null,e.name),l.a.createElement("p",null,e.desc),l.a.createElement("div",{className:"ss-button view-more"},"Learn about this type"))}))),l.a.createElement("div",{id:"skip"},l.a.createElement(u.b,{to:"/map"},l.a.createElement("div",{className:"ss-button nd"},"Skip to explore the full map"))))))))))}}]),t}(o.Component);var Fe=function(e){return l.a.createElement(o.Fragment,null,l.a.createElement("a",{className:"background-modal",onClick:e.toggleModal}),l.a.createElement("div",{className:"About"},l.a.createElement("div",{className:"About-main"},l.a.createElement("a",{className:"close",onClick:e.toggleModal}),l.a.createElement("h1",null,"About the Project"),l.a.createElement("p",null,"This project was funded through an internal research grants program at Sasaki, an award-winning planning and urban design firm based in Boston, MA. For questions and more information about the project, reach out to ",l.a.createElement("a",{href:"mailto:townology@sasaki.com",target:"_blank"},"townology@sasaki.com"),". For media inquiries, please contact ",l.a.createElement("a",{href:"mailto:communications@sasaki.com",target:"_blank"},"communications@sasaki.com"),"."),l.a.createElement("div",{id:"team"},l.a.createElement("h3",null,"Team"),l.a.createElement("p",null,"This research was conducted by a core team of designers and data analysts at Sasaki, supported by an extended team of advisors."),l.a.createElement("div",{className:"team-grid"},l.a.createElement("h4",null,"Core Team"),l.a.createElement("div",{className:"person",id:"gretchen"},l.a.createElement("div",{className:"photo"}),l.a.createElement("h5",null,"Gretchen Keillor"),l.a.createElement("p",null,"Research Lead")),l.a.createElement("div",{className:"person",id:"justin"},l.a.createElement("div",{className:"photo"}),l.a.createElement("h5",null,"Justin Kollar"),l.a.createElement("p",null,"Urban Planner")),l.a.createElement("div",{className:"person",id:"kai"},l.a.createElement("div",{className:"photo"}),l.a.createElement("h5",null,"Kai Ying Lau"),l.a.createElement("p",null,"Data Analyst")),l.a.createElement("div",{className:"person",id:"patrick"},l.a.createElement("div",{className:"photo"}),l.a.createElement("h5",null,"Patrick Murray"),l.a.createElement("p",null,"Web Developer")),l.a.createElement("div",{className:"person",id:"tori"},l.a.createElement("div",{className:"photo"}),l.a.createElement("h5",null,"Victoria Fisher"),l.a.createElement("p",null,"Communications & Narrative"))),l.a.createElement("div",{className:"team-grid"},l.a.createElement("h4",null,"Advisors"),l.a.createElement("div",{className:"person"},l.a.createElement("h5",null,"Randall Arendt"),l.a.createElement("p",null,"Author of ",l.a.createElement("u",null,"Rural by Design"))),l.a.createElement("div",{className:"person"},l.a.createElement("h5",null,"Dante Chinni"),l.a.createElement("p",null,"Author of ",l.a.createElement("u",null,"Our Patchwork Nation"),", Director of the American Communities Project")),l.a.createElement("div",{className:"person"},l.a.createElement("h5",null,"Richard Florida"),l.a.createElement("p",null,"Author of ",l.a.createElement("u",null,"The Rise of the Creative Class"),"; co-founder, CityLab")),l.a.createElement("div",{className:"person"},l.a.createElement("h5",null,"Ken Goulding"),l.a.createElement("p",null,"Director of Sasaki Strategies, Sasaki")),l.a.createElement("div",{className:"person"},l.a.createElement("h5",null,"James Miner"),l.a.createElement("p",null,"CEO, Sasaki")),l.a.createElement("div",{className:"person"},l.a.createElement("h5",null,"Mark Partridge"),l.a.createElement("p",null,"C. William Swank Chair of Rural-Urban Policy, Ohio State University")),l.a.createElement("div",{className:"person"},l.a.createElement("h5",null,"Andy Sell"),l.a.createElement("p",null,"Ecologist, Sasaki")),l.a.createElement("div",{className:"person"},l.a.createElement("h5",null,"Tim Wojan"),l.a.createElement("p",null,"Regional Economist, USDA Economic Research Service")))),l.a.createElement("div",{id:"methodology"},l.a.createElement("h3",null,"Methodology"),l.a.createElement("p",null,"The research process included five steps:"),l.a.createElement("ol",null,l.a.createElement("li",null,"Defining \u201csmall town\u201d"),l.a.createElement("li",null,"Gathering data"),l.a.createElement("li",null,"Analyzing data"),l.a.createElement("li",null,"Developing types"),l.a.createElement("li",null,"Applying the typology")),l.a.createElement("div",{className:"process-step"},l.a.createElement("h5",null,"DEFINING \u201cSMALL TOWN\u201d"),l.a.createElement("p",null,"There are a number of ways to define places that are small, starting with the question: what is a place? And then, what is the definition of small? Using existing categorical hierarchies from the Census Bureau and others, the research team defined \u201csmall town\u201d as: an incorporated area or Census-designated place with a population under 50,000, resulting in a dataset of roughly 29,000 places. ")),l.a.createElement("div",{className:"process-step"},l.a.createElement("h5",null,"GATHERING DATA"),l.a.createElement("p",null,"Data for these places is available at varying scales, depending on the topic area. In addition to Census place data, the Townology research team pulled data at the county and ZIP scales and then joined this data to the places using a weighted average method, and also created additional metrics (such as distance to airport) using geographic calculations. Over 150 metrics were gathered in total.")),l.a.createElement("div",{className:"process-step"},l.a.createElement("h5",null,"ANALYZING DATA"),l.a.createElement("p",null,"To make sense of this large dataset, the team:"),l.a.createElement("ul",null,l.a.createElement("li",null,"Selected the top ~40 metrics that seemed most meaningful according to discourse"),l.a.createElement("li",null,"Grouped these metrics into thematic indices (e.g., Vibrancy, Connectivity)"),l.a.createElement("li",null,"Examined correlations and a principal component analysis (PCA) to remove any metrics had a duplicative effect"),l.a.createElement("li",null,"Assigned weightings, informed by the PCA, to each metric (e.g., food access was weighted more heavily than the number of nearby logistics facilities)"),l.a.createElement("li",null,"Standardized the indices using z-scores so that each town was measured relative to the average score across the entire dataset"),l.a.createElement("li",null,"Tested a number of clustering options to determine the ideal number of clusters"))),l.a.createElement("div",{className:"process-step"},l.a.createElement("h5",null,"DEVELOPING TYPES"),l.a.createElement("p",null,"Using this refined weighted set of metrics, the team iterated through several rounds of k-means clustering analyses, adjusting metric weightings to tease out variability of indices between clusters, to result in the seven unique types. The types were then described using the index scores as well as demographic, economic, and physical characteristics, and branded using memorable names."),l.a.createElement("div",{id:"chart"})),l.a.createElement("div",{className:"process-step"},l.a.createElement("h5",null,"APPLYING THE TYPOLOGY"),l.a.createElement("p",null,"Finally, these unique types were applied to the dataset of small towns, to result in a personality profile for each town. For each town, the Euclidean distance of its index score from the cluster average was compared to the total Euclidean distance of the index score from all the cluster averages. This means that for every town, the dataset describes how strong of a match it is to the types (e.g., 20% type 1, 30% type 2), identifying not only its primary type, but the other types that it also closely matches."))),l.a.createElement("div",{className:"CTA"},l.a.createElement(u.b,{to:"/map",onClick:e.toggleModal},l.a.createElement("div",{className:"ss-button nd"},"Explore the map")),l.a.createElement("a",{href:"/Townology_Report.pdf",target:"_blank"},l.a.createElement("div",{className:"ss-button nl"},"Download the Report"))))))};var Ze=function(e){return l.a.createElement("div",{className:"Map"},l.a.createElement(b.c,Object.assign({width:"100%",height:"100%",onClick:e.handleClick,ref:e.mapRef},e.viewport,{mapStyle:e.mapStyle,mapboxApiAccessToken:"pk.eyJ1IjoidGFkaXJhbWFuIiwiYSI6IktzUnNGa28ifQ.PY_hnRMhS94SZmIR2AIgug",onViewportChange:e.setViewport}),e.children))};var Ye=function(e){return 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"},85:function(e,t,a){e.exports=a.p+"static/media/patch-fill.43abd437.png"},86:function(e,t,a){e.exports=a.p+"static/media/village-fill.98a39650.png"},87:function(e,t){e.exports="data:image/png;base64,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"},88:function(e,t){e.exports="data:image/png;base64,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"}},[[166,1,2]]]);
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