There's a Map for That

Do you “check in” at the gym, tweet your post-zumba heart rate, or post the mileage of your latest run? Or, do you virtually “like” reality TV shows, tag you and friends watching a Dexter marathon, or comment on the latest E! special? If so, you may have contributed data to the fitness maps below.

A recent study published in PLOS One (Chunara et al 2013) looked at more than 57 million Facebook users in the United States and more than 8 million in New York City alone to study users’ interests in two separate categories: (1) outdoor fitness, health and wellness, and (2) television. “Interest” is based on status updates, likes and profile categories. This information was cross-referenced and geotagged with CDC data on Body Mass Index (BMI) and EpiQuery Community Health Survey data in NYC.

The results? Those with higher percentages of “active interests” (exercise and wellness) have lower obesity rates, and those with higher TV interests have higher obesity rates.

The maps below show results from this study.

In a separate aggregation of activity and wellness data, a recent map from ESRI shows a nationwide perspective of likelihood of attending the gym twice per week.

Global climate change is understood to produce “winners” and “losers” who vary by regional scale. Climate change “winners” may enjoy increased agricultural productivity, improved access to water and other natural resources, and decreased climate-related expenses. Climate change “losers” may suffer from increased floods, draughts and other natural hazards, limited resource availability, and increased climate-related human mortality. A pressing question amidst the severe climate induced changes exists: what will become of global wine supplies (In addition, of course, to questions concerning the access and availability of food, fiber, water and shelter)?!?

 A team of researchers recently published results regarding the impact of climate change on wine-growing regions. The article by Hannah et al (2013) can be found in the most recent issue of the Proceedings of the National Academies of Sciences. A review of the article by the Guardian can be found here.

Hannah et al (2013) use 17 Global Climate Models (GCMs) to highlight regions of the world that will be most and least suitable for wine production at the present time and into the year 2050.

As a general overview, on the maps below:

RED areas are good for wine production now, bad by 2050

GREEN areas are good for wine production now, and good into 2050

BLUE areas are bad for wine production now, good by 2050

A more detailed explanation of the color codes:

Red areas are currently suitable for wine production, but that suitability will decrease by mid-century (according to over 50% of the GCMs

Light green areas are currently suitable for wine production and retain that suitability into mid-century (according to over 50% of the GCMs)

Dark green areas are currently suitable for wine production and retain that suitability into mid-century (according to over 90% of the GCMs)

Light blue areas are currently not suitable for wine production, but that suitability will increase by mid-century (according to over 50% of the GCMs)

Dark blue areas are currently not suitable for wine production, but that suitability will increase by mid-century (according to over 90% of the GCMs)

Drastic wine production declines are seen in:

Europe: 85% decrease in production in Bordeaux, Rhone and Tuscany

Australia: 74% decrease

California: 70% decrease

South Africa (Cape area): 55% decrease

Chile: 40% decrease

Even though nobody really knows where our taxes are going, thanks to ESRI, we now know where they come from! ESRI put together a series of interactive visualizations regarding income and property tax levels in the United States, as well as a map of tax software use. Although these three visualizations all focus on the United States, their scales of analysis vary: income taxes at the State scale, property taxes at the county scale and tax software use at the zip-code scale.

Income Taxes at the State Scale (click on individual States to learn more about taxes for each bracket):

Property Taxes at the County Scale (median tax rates per $1000 of value):

Tax Software Use at the Zip-code Scale (by likelihood of households to own tax preparation software):

March Madness: alongside the athleticism, authenticity and excitement the NCAA Men's Bball tourney brings, exists an interesting perspective in sports geography. Like many major sporting events, madness loyalties are a reflection of a group's cultural landscape. The cultural landscape may be quite dispersed as college grads tend to be a mobile population. I love seeing the sports culture in high gear every March, through the human landscape of college apparel, team mascots, school flags and the like.

ESRI put out two maps on college basketball fan-dom. The maps show who attends and who views college basketball games. In both cases, the maps are overlayed with the locations of teams included in this year's NCAA tournament.

Interestingly (or maybe very predictably) enough, the NCAA tourny sites are largely not located in regions with the densest and most wide-spread NCAA viewership. If that were the case, we'd all travel to the upper midwest to watch the games in person. They are, however, located in areas with higher likihoods of going to a game in person, which makes me wonder which came first and which is the causal factor here. For more analysis on why these spatial distributions are the way they are, check out Pam Allison's Smartblog post. She gets into ESRI's Tapestry Segmentation System, which classifies zip codes based on socioeconomic and other indicators.

Islands of high income are areas that are spatially bound by a high annual household earning levels per county. More commonly known as, "rich areas." Using American Community Survey 5-year estimates, the Census put out an interactive map displaying islands of high income.

Beyoncé’s famous feminine anthem is only partially true. Although, I don’t think B intended it as an accurate statement regarding international female political power. In any case, girls do run certain regions of the world.

An interactive map compiled by Lustlab’s Lizzy Malcom highlights women’s political prowess through space and time.

In addition to a global perspective, map users can click on individual countries to get more information about that state and state-wide information regarding political and human development for women. Information includes the presence or lack of legislative action on domestic violence, marital rape and sexual harassment, and statistics on the use of contraception, # of deaths through childbirth and other indicators. Statistics are also compared to world averages in each category. Click here to check it out.

Americans spend more hours of the year commuting to work (100 hours) than on vacation (80 hours). Maybe that explains why so many of us look like the walking dead on the Metro Trains or Yosemite Sam in gridlock the Beltway. A map put out by WNYC uses United States Census data to show areas of the country with the longest commute times. The data is at the zip code scale and map users can zoom into specific zip codes.

Buzzfeed put together a pretty awesome and hilarious compilation of maps.

#25: Most popular last names in USA:

 My favorites are #s 25 and 26, my least favorites are #s 32 – 38.

 I actually saw these latter maps presented at a previous Association of American Geographers (AAG) conference and couldn’t get over the overt subjectivity of their methods. It seems as though webpage describing their methods has been removed, however from my previous (at least 4 years ago) sleuthing I found the following measures associated with this project:

Greed: Average income compared with number of people living below the poverty line.

Lust: Number of STD cases reported per capita

Gluttony: Number of fast-food restaurants per capita

Envy: Total thefts (robbery, burglary, larceny, and grand theft auto) per capita

Wrath: Number of violent crimes (murder, assault, and rape) per capita.

Sloth: Expenditures on art, entertainment, and recreation compared with employment.

Once again the worlds of social media and digital mapping collide, this time to depict the geographic extent of “happiness” in the United States based on Twitter content. The University of Vermont’s Vermont Complex Systems Center used geo-tagged tweets to show happiness variations between States. The last State to the Union is the first State on the happiness scale – if you’ve ever been to Duke’s at sunset you know why. Louisiana is ranked as least happy.

On the map below, high ranks and warmer colors indicate higher levels of happiness:

At a more local scale the happiest cities show a distinct regional pattern with the top five as: Napa, CA, Longmont, CO, San Clemente, CA, Santa Fe, NM and Santa Cruz, CA. Moving from the joy-filled West in an Eastern direction, the least happy cities are: Beaumont, TX, Albany, GA, Texas City, TX, Shreveport, LA and Monroe, LA. Happiness rankings also mirror social trends from 2011 Census data, with wealthier individuals living in happier States, and higher rates of obesity located in sadder States.      

Happiness categorizations are a result of the Vermont Complex System Center’s analysis of more than 10 million geo-tagged tweets from 2011. The Center selected and ranked tweets according to how "happy" the word seemed. Examples of happy (and thus higher ranked) words include: "LOL", "haha", "sleep", and "wine," while sad (and thus lower  ranked) words include: "mad", "hate", "smoke", and "jail.” According to analysis from The Atlantic researchers overlooked several essential issues, including tweets in languages other than English and tweets from high tourist (rather than permanent population) areas.

From their inception after the first agricultural revolution cities have always had “good areas” and “bad areas.” “Good areas” often provide an array of goods and services and offer aesthetically pleasing landscapes. The real estate markets have taught us that renters can charge more to live in “good areas,” which can perpetuate the income and tax base with which to keep these areas “good.” Today’s cities are increasingly geographically polarized by class and may continue to be so due to social, political and economic structures preventing inter-class interaction.

A series of interactive maps use Census data to spatially depict regional variations of household income and/or rents. Although the original link allows users to search for certain areas in the United States using States or specific addresses, other maps were created for specific urban centers, including Washington, DC. Geographic class-based clustering is evident in many cases. Some clusters are surrounded by gradually lower or higher income classes, but others are spatially proximate (in some cases bordering) populations with much larger differences on the SES scale. These later instances may suggest areas of current or near-future gentrification, or disinvestment and out-migration from previously “good” areas (perhaps the “vote with your feet” model).

The Google-based maps allow users to examine surrounding physical and human characteristics of the landscape, including national parks, mountain ranges, river, travel infrastructure, services and points of interest. Such landscapes may influence the SES and rent distributions in the regions. For example, the map of Washington, DC explicitly overlays the Metro station locations with rent data, showing certain train lines correspond to certain levels of economic development.

Aside from all the geographic, social, political and economic analysis these maps spur, they’re also just a great way to decide where to rent an apartment!

As a complement to the data provided through the income and rent maps, here you can find ESRI maps that show the spatial distribution of household income, unemployment and demographic information at the State level. The unemployment map shows the percent change in unemployment from 2009 – 2012, during Obama’s first administration.

In the age of big data, merchants know more about who you are than ever before. Forget the days of correlating widely dispersed demographic information with your potential preference in consumer goods. Too much ambiguity. With the emergence of crowd sourcing and other data mining techniques, retailers can now pinpoint your preference in goods and services down to the name brand. Maps made with ESRI data provide a spatial perspective of flowers, jewelry and diamond purchases across the United States….Happy Valentine’s Day!

  Not surprisingly, the spatial clusters of high consumption are centered on populations with high socioeconomic statuses (SES). However, ESRI provides an interesting way to distinguish amongst these high SES populations. Using their Tapestry Segmentation system, ESRI classifies U.S. residential neighborhoods into 65 unique market segments based on socioeconomic and demographic characteristics.

 The analysis concerning the spatial distribution of Valentine’s Day consumer goods covers those who are most and least likely to buy. Some interesting connections are made between Tapestry categories and consumers concerning purchasing power, internet access, family structure and cultural traits.

As one of the Five Themes of Geography, “place” plays an important role in understanding and analyzing phenomena. “Sense of place” comes from one’s actual, lived experience of a certain space, but perhaps the more interesting facet of place is “perception of place” – one’s belief of what a space is like.

The International Cartographic Association’s (ICA) Children’s World Map Competition offers maps of our planet as perceived by children. According to a National Geographic blog, the purpose of the competition is to, “promote children’s creative representation of the world, to enhance their cartographic awareness and to make them conscious of their environment.” Winning maps from previous years can be seen at this National Geographic blog.

Aside from a few regions (including Central America and most of Africa) ICA members states are fairly representative of the world.

If you've ever taken public transportation in the Washington, DC Metropolitan Area, you are no stranger to lengthy wait times. I myself have come to loathe the phrase "weekend track work." A 1.5 mile Metro ride might be up to 45 minutes on Sunday morning. Due to weekend track work, rush hour schedules ("rush +"), and other various and sundry delays, the time it takes to complete a Metro train route doesn't necessarily correlate to that route's distance.

MV Jantzen's "Metro Distortion Map" shows the WMATA's Metro system based on the time-distance it takes to travel between stations. The user selects a starting station, and the map rearranges itself based on the time (rather than just the distance) it takes to get to other stations from that starting station. Stations that are a shorter temporal ride away are displayed as more spatially proximate to the starting station on the Distortion Map.

The map below is based on the starting station of Friendship Heights.

Jantzen included several added features to the Metro Distortion Map, which are explained here. The hands-down best feature is the one which allows users to transform the map into an operational Pacman game. Awesome.

"A cultural landscape is fashioned from a natural landscape by a cultural group...culture is the agent; the natural environment the medium," says Carl Sauer, one of most famous and influential cultural geographers of the 20th century.

Humans culturally shape the natural environment through linguistic, architectural, national, religious and other influences. But enough with the Human Geography 101 lecture. The ever-changing cultural landscapes of two major American cities, New York and Chicago, are mapped out by the Chicago Magazine and the New York Times, respectively.

The map of Chicago focuses on linguistic trends, using city data to map out neighborhoods that are most and least linguistically diverse, as well as clusters of specific languages. Click on each neighborhood to get linguistic statistics.

The map of NYC focuses on racial and national diversity, using Census and American Community Survey data to map out new ethnic enclaves and the most and least diverse neighborhoods. Break out maps beneath the main map show ethnic shifts over time, between the 2000 and 2010 Census.

If you have yet to "put a ring on it" and want to know the spatial extent of others flying solo, check out today's maps. Spatial analysis tools are used to efficiently and precisely locate many a sought-after item: real estate, eateries, travel routes, careers, and now (with the help of the US Census Buereas American Community Survey): a mate (or just a date, depending on what you're in the market for).

The maps below from The Boston Globe and Pharyngula's Science Blog outline  approximate number of single males and females in the United States surrounding major metro areas. Based on these projections, it seems as though Southern California is home to more single men, while the Northeastern United States has more single women. If you are a (hetero-normative) man in SoCal or a (hetero-normative) woman in DC and found your mate (or date) with ease...consider yourself an expert. Or just awesome. If you are on the look out, consult these analyses before you make your next (literal) move!

Forget McEnearney.The Washington City Paper's Neighborhood Guide provides raw and witty reviews of the Districts' "Hoods" through its "Neighborhood Rankinator."

This creative spatial tool allows users to explore DC neighborhoods based on ranking from 6 categories: Kid-Friendliness, Housing, Eats, Consumer Goods, Nightlife and Culture, and Intangibles. Does your preferred combination of categories qualify you to live in the "Nats Flats"? Or perhaps you are more geared towards the land of C-SPANistan. Rank the categories to find the perfect DC neighborhood match for you! The Rankinator also allows you to explore the hoods with its interactive map and an address search tool. Worried your new apartment may be located in "Laptopia," where "Front-yard square footage is low, but baby-stroller numbers are astronomical"? Use the address search feature to make sure!

Rankinator Hood descriptions include a combination full essays, categorical rankings and reviews, photos, demographic information and archived City Paper articles. A caution that the demographic information is based on the 2000, rather than 2010 Census.

From the User Experience Blog of IDVSolutions comes an in depth analysis of drunk driving in the United States. The authors use spatial analysis of Fatality Analysis Reporting System (FARS) data from the National Highway Traffic Safety Administration to better understand the regional role of intoxication in fatal car accidents. See this link and a second link here for statistical analysis and ranking of the data, a detailed explanation of the hexagonal meshing technique used for the mapping, and a series of scenarios describing the regional variation. The authors display the data through maps, charts and graphs showing variability across American regions and cities.