Azavea Atlas

Maps, geography and the web

Teaching a New Map Old Tricks: Using Archival Color

Despite my academic training as a geographer, I’ve found it difficult to incorporate traditional cartography into my day to day work.  While I truly appreciate the aesthetic of archival maps (I have many hanging on my walls, including an 1860’s Johnson and Ward), the focus of my work remains interpretation of data, analysis and visualization for print and web.

From The Atlas Nova Totivs Terrarvm Orbis Geographica Ac Hydrographica Tabula. Published By Hondius, Henricus In 1633.

From The Atlas Nova Totivs Terrarvm Orbis Geographica Ac Hydrographica Tabula. Published By Hondius, Henricus In 1633.

So, after attending NACIS and exploring some best practices for color in modern cartography in a previous blog post and even blending colors on the fly, I set my sights on exploring some of the beautiful and classic color palettes of archival maps. Though I don’t have the opportunity to do classical cartographic work like digital recreation or archiving, I can find significant inspiration from the muted and sometimes striking color palettes of these maps.

With our instantaneous digital (or print) access to every color on the visible spectrum, it’s fun to imagine the limitation (or inspiration) cartographers once faced in selecting from a finite number of inks and dyes derived from natural materials that were both scarce and expensive to produce.

The below images represent a short selection of some of the most colorful and enchanting archival maps and their corresponding color palettes.

TimeTravel

London Travel Time Map, 1914 Source: Reddit/r/mapporn

 

Geological

Carte Geologique De L’Europe. Andrew Dumont, 1875. Source davidrumsey.com

 

ColoradoMap

Rand, McNally & Co.’s New Geological Map of Colorado. 1879. Source davidrumsey.com

 

Constellation

Plate 1: Ursa Major, Ursa Minor, Perseus, and other constellations, 1693. Source: davidrumsey.com

You can find an entire selection of these mappy color palettes on my ColourLovers account.  Each palette includes a link to the corresponding map that inspired the palette.  I hope these will ignite your imagination and inspire some archival charm in your modern map making.

Palettes inspired by Archival Maps. Colourlovers Account: Mapadelphia

Palettes inspired by Archival Maps. Colourlovers Account: Mapadelphia

Five Easy Steps to Add Districts to Your Salesforce Nonprofit Account

We’re excited to announce an integration that allows users of the Salesforce Nonprofit Starter Pack to verify addresses and append legislative district information to their Salesforce records. The Nonprofit Starter Pack uses the Cicero API to geocode household addresses, and parses the response to extract the district and attach it to the address record. Cicero will also update the record with the CASS-certified address using our geocoding services. CASS-certified addresses have a much better automation postage rate and helps mail carriers deliver more accurately.

what_if_i_told you

As of right now, the integration will only append Congressional district information. But with a little tweaking of the code, it could easily be configured to append other district information from Cicero — like state legislative district, school district or census identifiers.

The Cicero API will verify the address and append Congressional district information to the address record. It’s important to note the address record is associated with the household in Salesforce.

Setting up Cicero in Salesforce

Once you have the Nonprofit Starter Pack installed, integrating the Cicero API is quite simple.

  1. First, you’ll want to make sure you have a Cicero API account, which you can sign up for here. You’ll need the unique API key from your account.
  2. To start using the Cicero API, navigate to the Nonprofit Starter Pack Application Settings page.
  3. On the left side menu, under People, click the Address Verification option. Once there, choose to Enable Address Verification.
  4. Authentication ID can be left blank, but you’ll fill in the Authentication Token box with your unique Cicero API key. Your API key can be found on the Cicero Profile page.
  5. For Verification Service, choose Cicero. This will populate the Address Verification URL with the legislative_district Cicero endpoint (https://cicero.azavea.com/v3.1/legislative_district).

API_Account_Settings

You can then choose to update all of your households at once. As soon as Address Verification is activated, Salesforce will update new records with Cicero information whenever they’re added.
mass_verify_addresses

Boom! Now you’ve got a Congressional district for every verifiable address record in Salesforce.

address_detailOf course, the Cicero API free trial will give you 1,000 credits to use just for signing up. This will essentially allow you to verify and append district information for 1,000 addresses (applicable to legislative district information only). If you’re a TechSoup member, that’s 5,000 Cicero API credits. For additional addresses, you can easily purchase a bucket of credits by logging in to your Cicero account. We also offer discounted high-volume pricing for more than 100,000 credits and always offer a 10% discount to nonprofits. Questions? Email or tweet us and we’ll be happy to help you get started.

The History and Future of Disease Analysis and Visualization

ODI

While recently attending the ODI Summit Discovery Day in London, I had the opportunity to hear from Max Van Kleek about the broad possibilities of data visualization. He shared many examples of effective infographics and visualizations. Max explained that data analysis and visualization is used most often to do one of two things: to solve problem, or to communicate complex idea in a simple manner.

In public health, these two purposes of data visualization are invaluable for epidemiologists to understand and track dangerous disease outbreaks. For example, in seeking to understand the Cholera outbreak in 1854 Soho neighborhood in London, physician John Snow (1813-1858) conducted a geographic survey of the locations of Cholera deaths. Through his analysis he found that the outbreak was not airborne, as commonly thought, but linked to a contaminated water source. Once he was able to use his analysis to convince the community of the source of the outbreak, the handle of the contaminated pump was removed, preventing further spread of the disease. The analysis John Snow conducted is commonly believed to be the first ever use of geographic analysis to understand and solve a complex problem. While in London at the Summit, I visited the site of the pump which was once the cause of hundreds of deaths, but now serves as a symbol of how data driven analysis helps us solve health problems and save lives.

JohnSnow

A modern version of John Snow’s Map of Cholera Deaths in Soho, London

waterpump

A replica of the water pump determined to be the source of the Cholera outbreak.

The second important purpose of data analysis and visualization in Public Health is to advocate and communicate effectively with a wide audience. Florence Nightingale (1820-1910) used patient data and a visualization of causes of mortality to prove that significantly more soldiers during the Crimean War were suffering from diseases related to contamination in hospitals than from actual war injuries. This advocacy helped to improve hospital conditions and encourage sanitary practices in medical facilities, which subsequently saved millions of lives. Nightingale’s effective data driven communication of this serious public health concern was integral in changing policy.

FN

Florence Nightingale’s visualization of cause of deaths during the Crimean War

APHA

Historical uses of public health mapping have paved the way for methods used by epidemiologists today. At a recent GeoPhilly Meetup about the confluence of spatial analysis and Public Health, local researchers shared how they use these tools in various public health applications. For instance, Joan Bloch, PhD, CRNP at Drexel shared her public health work using geographic tracking. By mapping the time and cost of transportation,  she was able to demonstrate the challenges low-income women face finding transportation to maternity-related care visits. This research is used to advocate for more thoughtful distribution of services to encourage better utilization of these key resources. Joan is presenting her work at the upcoming American Health Planning Association conference this month in New Orleans.

As analytical tools continue to evolve, spatial analysis and visualization will become even more valuable in understanding and tracking epidemic outbreaks like the current spread of Ebola in West Africa. Researchers are already charting the rate of disease spread geographically to determine how to best plan their health resources to limit future cases. With the increased use of GPS-enabled devices to record health data, the importance of using spatial analysis and visualization will become even more vital in fighting disease and promoting a healthier society.

Ebola tracking map created by the World Health Organization

Ebola tracking map created by the World Health Organization

 

2014 Geo Open Source Conference hosted by GeoPhilly and LocationTech is Coming!

2014 Geo Open Source

Azavea is pleased to announce our participation in the 2nd annual LocationTech Tour which will feature a stop in Philadelphia. Registration is now open for the 2014 Geo Open Source Conference on November 20th in Philadelphia at ph.ly/opengeo.

Azavea is now in its second year as members of the Eclipse Foundation and its working group, LocationTech.  We are glad to be joined by the likes of Boundless, IBM, Oracle, Google, and others.  This group functions as a thriving community for open source geospatial software to which we are proud to contribute.  The LocationTech Tour is a federated series of global events demonstrating and discussing new technologies and concepts with open source geospatial software and open data.

GeoPhilly, Philadelphia’s meetup group for map enthusiasts, is involved in the organization and presentation of this event.  Since its founding in fall 2013, the group has grown to over 375 members and has held 12 events.  Philadelphia’s LocaitonTech tour event, the 2014 Geo Open Source Conference presented by GeoPhilly and LocationTech will be held on Thursday, November 20th and feature a speaker series in the afternoon and a social event in the evening.

This year’s event will feature talks by:

Register

Additionally, a LocationTech Code Sprint will follow on Friday, November 21st to be held at Azavea’s office.  This event is a one day code sprint featuring opportunities to work on LocationTech project code with experts.  Join us to learn and network with industry experts in a friendly collaborative atmosphere with plenty of camaraderie.

Last year’s event in Philadelphia had a turnout of over 100 individuals interested in geospatial and open source technology.  You can find a more information about last year’s events as well as videos of presentations in this recap.  Our deep gratitude to the organizers, speakers, participants, and supporters who make the Tour a success. Founding supporters of the 2014 Tour include Azavea, Boundless, Mapzen, Oracle, and the Open Source Geospatial Foundation (OSGeo).

Mission Emission: Analyzing and Mapping CO2 Emissions

The People’s Climate March on September 20th brought over 300,000 people to the streets of New York City to voice support for policies that reduce the man-made effects of climate change across the globe. It couldn’t be any more timely that an international research team led by scientists at Arizona State University released the Fossil Fuel Data Assimilation System. It’s a global database of CO2 emission estimates at the 0.1 decimal degree resolution (about 8-10 kilometers in the continental U.S., depending on latitude) containing hourly and yearly data from 1997 to 2010. You can visualize the data by year here, and it’s available for download in a few different file formats, including text and csv. It’s quite an incredible database, and the first of its kind at that resolution. The CO2 is estimated using a combination of existing data sources such as population, remotely sensed nighttime lights and the location of power plants. The methodology can be found in Rayner et al. (2010) and Asefi-Najafabady et al. (2014).

ffdas_2010

My first question when discovering this dataset was “Where might co2 emissions have increased or decreased since 1997?”. My hunch was that overall emissions have increased, but that the spatial distribution might show some interesting trends across the United States. For example, the spatial distribution may have been affected by the fact that in 1997 the U.S. was in a period of strong economic growth, while in 2010 the country was still recovering from the Great Recession.

Data Processing

For this exercise, I converted the text files into polygons and sampled at the county level to get estimates of CO2 change between 1997 and 2010. Alternatively, the NetCDF files could be used to generate rasters for visualization of the data. QGIS has a NetCDF browser plugin for doing just that. Since the dataset is at the 0.1 degree resolution, it lends itself to creating a raster quite easily. Originally, I planned to vectorize the raster dataset to display it in CartoDB (as of now, CartoDB does not support raster files). However, I thought it would be more interesting and useful to aggregate the data to a more common unit of analysis that people would understand, such as counties.

There are a few ways to go about this. I’ll describe how to do this in ArcGIS using the ET GeoWizards plugin, but it’s also possible to do this analysis in QGIS. ET Geowizards is available as a free ArcGIS plugin. There’s also a paid version with some more advanced features that requires a license.

First, I had to convert the text files containing the coordinates and CO2 emission value into a vector dataset. Originally thinking I was going to create a raster visualization, I used the Point to Raster tool in ArcGIS to create a raster surface of the data, but later decided to calculate the data at the county level, and vectorized the raster data with the Raster to Polygon tool. This produced polygons of raster cells at the 0.1 degree resolution. The data itself is measured in Kilograms of Carbon (kgC) per square meter, so I transformed the data to total kgC, then converted to metric tons. Since the vector polygons are smaller than counties, I was able to resample the data using the Transfer Attributes tool in ET GeoWizards. This tool applied a proportion of the CO2 emissions total to each county polygon based on the proportion of the CO2 emissions polygons that overlapped each county. If the CO2 emissions polygon was entirely inside the county, the total amount was applied. Next, I summarized CO2 emissions by county. Quite conveniently, the Transfer Attributes tool will do all this.

If you don’t have access to ArcGIS, the entire attribute transfer process can be accomplished using the QGIS Intersect and Dissolve tools. The county and smaller CO2 emissions polygons can be intersected in QGIS, and the area of the resulting polygons can be divided by old polygon areas to get a ratio. That ratio can then be applied to the CO2 emissions polygons and summarized at the county level.

These processes work under the assumption that the value for the CO2 emissions polygons are a total amount of emissions for the entire area of the polygon. However, CO2 emissions are rarely uniformly distributed — therefore, using this coarse resolution CO2 at any geographic level smaller than a county is probably not appropriate.

Below you’ll find a map of estimated CO2 emissions change in percentage between 1997 and 2010 for every county in the United States, visualized in CartoDB.

Increase in Carbon Dioxide Emissions

In the Tableau visualization at the bottom of the page, you can also view the ten counties with the largest increase in CO2 emissions in metric tons, compared to their population change. Most notable are the increases in Collin and Denton County, Texas. Both counties are in suburban Dallas. Other counties are in similarly fast growing areas in California and Florida, along with Allegheny County, Pennsylvania (home to Pittsburgh). It makes sense that rapidly growing areas would see such an increase in CO2 emissions. Allegheny County is a different story, since it lost population during the data’s time frame. Overall, all major Texas metros saw significant increases, as did Florida — with the notable exception of Miami. Southern California, Las Vegas and Phoenix saw increases. There’s also a noticeable trend of increase across the central Midwest, from Illinois through Ohio.

Decrease in Carbon Dioxide Emissions

The counties with the largest decrease in carbon dioxide emissions are mostly in parts of the country that haven’t done too well economically over the past 20 years, such as the Detroit area. New England and most of the Northeast in general also saw a decrease, with the cities of Boston, Philadelphia and Baltimore. CO2 emissions decreased across the Northwest, including Portland and Seattle. Perhaps the most stark trend visible is the decrease across the Great Plains, surely related to the decrease in population in that part of the country.

Largest Counties in the U.S.

Of the ten largest counties in the U.S., most saw increases in CO2 emissions. The exceptions here being the two counties (boroughs) in New York (Kings and Queens) and Miami-Dade, Florida. Both King and Queens in New York City saw only small population increases, but Miami-Dade saw a rather significant increase in population. Cook County, Illinois was the only county in the top ten to see an increase in emissions but a decrease in population. We find that in Maricopa County, home to Phoenix, and Harris County, home to Houston, CO2 emissions percentage growth has outpaced population growth in the time period.

Further Study

There are certainly opportunities to try to better understand some of what the data is indicating. For example, we might expect areas with rapidly growing populations to experience an increase in CO2, but what about counties that declined during the time period, such as Allegheny County, PA? There’s also some interesting trends to try to better understand. This is a great start, but I hope to see a higher resolution version of this dataset released in the future.