Geospatial Viz Showroom

Every Dataset has a Story to Tell…

Sometimes even more than one!

Combining datasets provide exponentially greater stories to tell.

Dynamic maps can better represent large amounts of data than can static maps, when there are a variety of permutations and end users of the data. Certainly in any dynamic dataset there are static maps that can be extracted to tell the story, or the most interesting story, but quite often it is the exploration of dynamic data that brings it to life. Static maps are more often the true story tellers, because their capture of a place in space and time allows for a narrative to shine through, or even be accompanied in text.

Some of the examples in this list are clearly stories from static maps, and others clearly define the dynamic story of a dataset.

ESRI Zip Tapestry

In case there was ever any doubt about the type of demographic analysis that can be done with Census data, our pals at ESRI took things to another level when they created a nation-wide demographic viewer that breaks down populations by their zip code into politically correct and quaint stereotypically accurate sometimes-alliterating categories – The Zip Tapestry. Demogrpahics and Lifestyle they call it – others might call it neighborhood profiling. Kinda tough to believe that there wasn’t some market data thrown in with the census data to really characterize these populations. Just check it out. One thing particularly interesting about these ESRI web services maps is that they look dramatically different depending on the browser window. Below is the chrome view, followed by the mozilla view.



NREL US Energy Potential Map

Explore the Nation’s potential for any and all types of renewable energy with the NREL Renewable Energy Atlas. Turn layers on and off to see what regions have potential to benefit from more than one renewable resource, and what regions are prime candidates to begin to reduce dependency on natural gas and petroleum. This is a great map as well for designers and sustainability planners, because the built-scape of any environment should always be created to maximize the environmental surroundings while simultaneously minimizing the impact.



Judgemental Maps

This site is a collector for the convergence of ‘sense of place’ and ‘mental map’ – people are asked to create a map of their location based on the common understanding or lay-interpretation of what different neighborhoods of a city are characterized by. Read more on Judgemental Maps in this WMA May 2014 post.

Judgmental Map Seattle

Judgmental Map Seattle

Judgmental Map Denver

Judgmental Map Denver

Denver Area Farmer’s Market Deserts

This visualization is from the winning team of the first ever Go Code Colorado Data Jam Hackathon – held Saturday March 28 at the Turing School in downtown Denver. This team combined two datasets from the Colorado Information Marketplace: ACS Block Groups, ACS Zip Codes, and 2014 Colorado Farmer’s Markets – to create this analysis of Farmer’s Market Deserts. As a spin from the concept of a food desert. A full food desert analysis involves extensive data gathering on the type and quality of available food sources to given locations (this is a major use of a state sponsored open dataset of retail locations!!), in addition to a route analysis based on differing modes of transportation. In lieu of having all that data available, this team was smart in showing farmers markets as indicator locations of communities likely to be under-served by access to fresh foods and produce. Of course there are other factors at play – like even things as simple as open space availability to host the farmers market, but certainly this simple mashup shows some patterns and suffices as evidence for a need for more research in this arena.

Colorado Farmers Market Deserts

Zip Codes in Colorado with Over 90% Occupancy (either owner occupied or renter occupied)

This data is packaged as “Census Data by Zip Code” on the Colorado Information Marketplace, but many people do not know that Census data includes data on housing and households in addition to the more well known attributes of “population”, “gender” and “race”. In this zip code example you can see the booming phenomena of population in Colorado. As a higher density urban area, constrained by urban growth boundaries, transportation infrastructure and quality of life in centralized areas, Denver has high rental and occupancy rates in its urban areas. From this data you can see the patterning of urban areas across the landscape, with density being defined as rental occupancy over 90% distributed by zip code (sorry! a couple zip codes have “no data” and are see-through). Explore all of the Colorado Census Data – curated by DOLA, so it is combined with TIGER geometry: export it as a CSV or SHP! What Data Visualization can you make with Census Data?

Delinquent Cartography

Meet Doug McCune. His art is all data, and almost all gov’t data. Starting with this SF crime data, which is eerily pretty despite what it represents, when transformed through his visualization “Bay Area Homicide Constellation Map.” Things get vertical with his representation of seismic data from a recent South Napa Earthquake. Take the trip to his blog, Delinquent Cartography, to see this 3D model come to life! …and explore a few other gems that aren’t featured here. BONUS – he’s got code (AND tutorials!) for those who want to use these visualization techniques on Colorado datasets.

delinquent cartography

Colorado Avalanche Map

The DNR has made it super easy to share their avalanche map.


FACTUAL – Combine Open Datasets with this API to create cool Apps

Definitive global location data with tools to analyze, connect, clean, and map data for developers to use in building personalized, contextually relevant mobile apps. Factual has a database of over 65 million local businesses and points of interest in 50 countries accessible via API or download. Database includes 43 restaurant specific attributes for restaurants of every type, over 1 million physician, dentist, and healthcare provider listings with the key data you need to make informed decisions, and 140,000 hotel listings with over 35 attributes covering everything you need to know about a hotel. The API also allows you to transform location signals into contextual data, enabling you to deliver personalized and relevant app experiences, content, and ads to mobile users. Real-world user profiles with demographic, geographic, and behavioral information generated by analyzing geo-behavioral patterns. High volume, low latency geofencing to deliver contextually relevant content, experiences, and ads based on where mobile users are. Global reverse geocoding to easily tag digital content with meaningful location information.

Unique Location Crowdsourced Weather Data for Colorado 1999-2015

This heat map shows the point locations of data contributed over the course of time. It could also be inferred to a degree that the heat map is showing some representation of the weather patterns, as measurements are of precipitation. Data Source –

PLUTO and Parcels – Could you do this with CO Data?

PLUTO is the New York City’s Extensive Land Use and Geographic Data at the Tax Lot Level in comma–separated values (CSV) file format. The PLUTO files contain more than seventy fields derived from data maintained by city agencies. That’s right. It’s the city’s parcel layer – open, free and crazy interesting. With a Data Dictionary that is off the hook, to boot! Take a trip through the dataset in the way that only Carto DB could do it best – Andrew Hill’s interactive tour of the dataset is both entertaining and informative”857,879 tax lots across New York City…And many beautiful errors. cartodb_pluto_tour

Colorado’s new Statewide Parcel Layer is in its first phase of conception. co statewide parcels_first 19_600   The 2014 edition includes only 19 of 64 counties, but still covers the area of 45% of state residents (2010 population of 19 counties = 2259300, 45% of 5052196 total state population). Who’s up for the challenge of creating a Colorado Statewide Parcels Tour?

Colorado Railroads as Potential for Tourism Development

It is expected that certain basic information will be included with data on railroad lines, including whether or not the rail line has been abandoned. The “hidden” aspect of interest in this scenario, is that abandoned rail lines can promote tourism and economic development through conversion to bike and pedestrian trail. The color coding below also identifies rail lines with Amtrak service, in addition to a selected number of lines designated by CDOT as “Tourism Tracks.” Select a line and look at the passenger code – V = VIA Line, A = Amtrak Line, C = Commuter Line, T = Tourist Line, R = Rail line now used for rapid transit, X = previous passenger route/line, Y = undesignated service. Lines coded in Green are potential for recreation/trail, lines coded in blue are tourist lines, and lines coded in red are Amtrak, commuter or rapid transit lines. Consider combining this dataset with the data provided by RTD – and check out the RTD Developer resources page while you’re at it! The Rails to Trails program is a national effort to maintain and preserve old rail lines while simultaneously increaseng the mileage of greenways and interconnected trail systems -across states and across the nation. This photo below is from the cross country ski segment of the ?? trail between Aspen and Vail.

 I Quant New York

Illustrating that sense of a place can be described by its components. As the sum is greater than its parts, it could also be said that understanding individual parts of their own accord is a good exercise for having a better understanding of how exactly the sum is characterized. NPR recently showcased I Quant NY – a blog dedicated to showcasing the findings of Data Scientist Ben Wellington as he wrangles and munges his way through NYC’s open data catalog. The most interesting stories are often discovered in the least likely of datasets. Data Science challenges people to think about the multiple facets of everything. Data Science to the Rescue. Perhaps the most exciting part of opening up data is discovering what solutions to common problems can be discovered through making good use of the data. For many entrepreneurs, this is commonly identified as “finding the pain points” – what would people pay money to not have to do anymore? The most obvious place to start looking for pain points is in the areas where the blood of Democracy is thundering through societal veins: new industries and new policies. New Industry Example: What possible linkage our use for Economic Development could come from utilizing Department of Motor Vehicle data – isn’t that just types of cars and locations of owners? Sure, of course it is, but it is also locations and records of alternative fuel vehicles and fleet vehicles for governments, organizations and private entities. Certainly the diversification of the energy market is tied to the Economic Development of a region that supports it. The specific needs of this burgeoning industry include an app that shows the location of all alternative fueling charging stations, preferred parking spots and other amenities designed to encourage diversification of the nation’s energy portfolio. Enjoy a few headlines from I Quant NY:

Sea Level Rise in SF and Books on Bikes Map

Forecast modeling of climate change model of 200′ sea level rise in San Francisco would create a dramatically different geography for the city. In this visualization, the remaining exposed land area is named for the  “The impact of coast erosion due to the surprisingly rapid disintegration of the East Antarctic ice cap is now reflected in the NCCS (Northern California Coast Survey) topographic map of the San Francisco Archipelago. It shows the 200 foot sea level rise compared to the 2012 sea level datum.” This Burrito Justice blog also features a map to create an interactive experience of biking the city to visit places featured in or notoriously frequented by famous authors through time.

burrito justice


Food Desert – the Living Building Challenge Chicago Design Inspired by Data

In the 2013 Design Competition sponsored by the Living Building Challenge : Chicago organization, contestants were asked to surmise and plan for a site that would draw on the needs of the local community to create a facility that would unify the citizens and encourage sustainable development. Using data to first assess the site led to the clear conclusion that the area was prime for placement of grocery and restaurants: populations of residents (grocery) and staff (restaurant) for large facilities (hospital and university). A preliminary assessment can be found at the project design website – Center For Permaculture and APropriate Technology of grocers (Table 1) in a one mile Euclidean radius shows a majority are wholesale providers (28) and a handful of retail grocers selling produce (7). The half mile radius shows a drastic reduction in all store types, further iterating the site is at the heart of a food desert. If you don’t have a design site to follow the methodology of this example, consider taking on the challenge of starting a statewide Farmer’s Market Accessibility Map and Impact Assessment. What is the demographic profile from comparing Colorado 2014 Farmer’s Markets to Census Blocks and applying a 1 mile walking radius buffer, 10 mile biking radius buffer and 30 mile driving radius buffer? 


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