Azavea Atlas

Maps, geography and the web

4 Cartography Color Tips Hue Should Know

Recently attending the NACIS Annual Meeting provided significant inspiration on cartography and visual map design.  This encouraged me to assemble some tips on the thoughtful use of color in cartographic design.

1. Never Use the Default Colors

A frequently mentioned recommendation during the conference was to avoid defaults at all cost.  One specific set of defaults to avoid in mapmaking are the default color schemes (and even classification styles) used for symbolizing data in your maps.  John Nelson made this specific warning during his presentation on 20 Unrequested Map Tips (see tip #3 “Defaults are Evil”).  His map below highlights the worst of the worst of relying on defaults in ArcMap.

Bad_map

Map highlights worst of defaults in ArcMap by John Nelson

Consider using the ColorBrewer web tool (Cynthia Brewer’s collection of beautiful color choices for ramped or categorical  data) as an alternative to Arcmap’s uninspired defaults .  You can even import ColorBrewer schemes directly into ArcMap or other GIS or data analysis software of your choice (GDAL, QGIS, R, TileMill, etc). Happily, a favorite web mapping tool, CartoDB, already includes these lovely schemes.

2. Palette Inspiration

Color selection need not be traditional and stuffy.  Inspire yourself to design a more creative color scheme by browsing some existing color palettes and even create your own. Here are a few I like:

Palette_Adrift

Palette_Terra

Palette_Empirical

Or browse old maps for inspiration:

Isochronic_Map

Isochronic Distances Map of Travel Time in Days from London, 1914

The above map was used as inspiration for the color palette below

Palette_TimeTravel

3. Logo or Photo Inspiration

Are you designing a map for a client? Try coordinating their company colors with your map design.  Identify the RBG codes of the colors in a logo or site by using a free eyedropper tool (in your Chrome browser or desktop) or add the Eyedropper tool to your toolbar in ArcMap. Now that you have identified a few of the predominant colors in your logo, decide how they will be used in your cartography (would these colors be suitable for categorical, divergent or sequential data?)  You can simply use these colors for inspiration or you can use a color blender tool like this one or this one to create a range from two colors.  Adjust saturation and transparency to suit your mapped data.  Below is a logo, a ramp and a map created using this method.  I decided to just use a palette made from only the blue in the logo but you can also find a divergent palette using both the red and blue here.

Logo of Inspiration: the Philadelphia Phillies Logo:

Phillies

A palette created from the primary blue of the logo:

Palette_PhilliesBlue

Finally, a map symbolized using the blended color palette:

Find the color palette of the above scheme here.

4. Design for Accessibility

Color deficiency or blindness is an important consideration in cartographic design.  Red to Green color palettes are used frequently when mapping divergent data.  The inability to differentiate these two colors causes significant challenges in interpreting data.  Tip #17 from 20 Unrequested Tips and this fantastic blog post does a great job at describing the shortcomings of some color palettes and makes fantastic (and beautiful) recommendations for accessible colors. When selecting your accessible palette, consult Colorbrewer which has an option to select only colorblind safe palettes.

Other areas of consideration for design accessibility for vision impairment include tactile maps which deserve their own blog post, but I’ll say PATCO’s Tactile Transit Map is worth checking out for inspiration. (Routes and stop amenities are uniquely symbolized using tactile features for easy navigation).

Conclusions

It is important to consider how the colors in your maps communicate and whether it is successful in sharing the correct message.  The book How to Lie with Maps by Mark Monmonier devotes an entire chapter on the misleading use of color in maps.  As a cartographer, you have the power to make creative and thoughtful color choices. Use these tips and guidelines to bring careful color consideration and planning to your maps. This will help you communicate better with a wider audience and help your map stand out in a sea of ArcMap defaults.

For the Love of Maps

nacis_2014_pittsburghLast week, Data Analytics Project Manager Sarah Cordivano and I attended the North American Cartographic Information Society conference in beautiful Pittsburgh. After searching around for some of the best GIS and mapping related conferences to attend, NACIS was one of the mostly highly recommended, and I think it lived up to its reputation.

The conference started off with Practical Cartography Day, an entire day of sessions devoted to real-life examples of map-making. The talks touched on a diverse array of topics in mapping, which made it particularly valuable. If these talks were scheduled on topic-area specific tracks, I probably would have missed some interesting examples and discussions. One of my favorite speakers on Practical Cartography Day, John Nelson, offered his 20 Unrequested Map Tips. It’s really great advice for those just starting out in cartography, especially in academia, where often the ArcGIS defaults are the only thing taught. It made me reflect on how important user interface and design is to cartography and how that education is really lacking within today’s GIS programs.

Some other sessions and talks that were particularly good:

    • Alan McConchie of Stamen Design gave out a bunch of good tips for manipulating custom CartoCSS. It’s a must-see for anyone who uses Tilemill and wants to learn some cool tricks for customization.

    • Exemplifying the challenge of customizing maps, Nicki Dlugash of Mapbox talked about the design challenges in creating a basemap of the entire world styled optimally at all zoom levels using OpenStreetMap data.

    • Though not strictly map-related, Miles Barger of the US National Park Service presented on a recent project to create a 3D model of the Grand Staircase, a major geologic feature in Utah. He touched on the need to “fiddle” with settings to create the perfect diagram and also started a bit of controversy (mostly from one attendee) when he suggested it’s okay to manipulate or exaggerate features for the purpose of creating a user-friendly design.

      grand_staircase

 

  • Also of the US National Park Service, Mamata Akella presented on the beautiful custom maps and tools the park service has been working on. They’ve put everything on GitHub (except the internal stuff, of course).

  • Patrick Kennelly, of Long Island University, showed the results of using a three dimensional helix model to visualize daily temperature data over time at over 250 weather stations across the US. Patrick and his team used the Blender API for data manipulation.

  • During the Transportation Maps session, Nate Wessel, student at the University of Cincinnati, presented his bicycle map of the Cincinnati area. Contrary to the typical government produced bike map based on subjectivity of conditions, Nate based his map on more objective conditions agnostic to the type of rider, such as elevation change, speed limit, and road condition. Nate’s map was also a runner up for the student competition.

In addition to attending the conference, Sarah and I also presented on some topics of personal interest. I introduced General Transit Feed Specification data and gave some examples of how to the data is being used in mapping and analysis today. Sarah talked about the importance of open data and open source tools which brought up a lively discussion of how the issue relates to cartography and spatial analysis. Overall, the conference had a nice balance between real-world and academic examples of cartography and analysis. Next year, the conference will be in Minneapolis and it’s definitely a must attend for anyone who loves maps.

 

Five Ways Your Foundation or Nonprofit Can Get Started With Spatial Analysis

Taking advantage of new technology in your foundation or nonprofit can sometimes be a difficult process. Fortunately, the Knight Digital Media Center (KDMC) hosts a series of workshops across the US for nonprofit foundations to learn about technology. I was able to speak at the most recent KDMC workshop in Charlotte, North Carolina, along with Amy Gahran, independent journalist, Dan X. O’Neil, Executive Director of the Smart Chicago Collaborative, and Sarah K. Goo, creator of the Pew Research blog Fact Tank.

In my workshop I talked about five ways nonprofits and foundations can get started with spatial analysis, and I used three case studies from Azavea’s Summer of Maps program to help.

The bicycle crash maps I made last summer for the Bicycle Coalition of Philadelphia fostered a public conversation about crash reporting policies, raising awareness about an issue the Bike Coalition cared about. The spatial analysis work I completed for the Greater Philadelphia Coalition Against Hunger as a Summer of Maps Fellow helped amplify their door-to-door efforts and target their limited advertising dollars. Finally, 2013 Summer of Maps Fellow Lena Ferguson’s work for DVAEYC had a huge impact, helping win one million dollars in grant money to improve early childhood education programs in Philadelphia.

Your foundation or nonprofit may be interested in taking advantage of spatial analysis to raise your profile, amplify your message, and target your efforts. Here are five steps you can take to get started:

  1. Take the Maps and the Geospatial Revolution class at Coursera to learn more about spatial analysis. The course is online and free, and takes about five weeks to complete. Have a data analyst in your organization take it too.
  2. Collect address-level data about every interaction your organization has with clients and the public, because addresses need to be in a specific format to be put on a map. Here are some best practices for preparing and maintaining your organization’s address data.
  3. Check out TechSoup to find discounted licenses for ArcGIS, or download the free and open-source alternative, QGIS. You may not be ready to use a GIS desktop software now, but having one on hand will enable an analyst, consultant, or intern to get started working on spatial analysis right away.
  4. Check out the presentation, sample maps, and some resources I collected for the workshop with this Bitbucket of links.
  5. Tell each of your grantees (if you’re a foundation) to do steps 1-4.

The five steps above should help your organization leverage the power of spatial analysis. You may already have questions about your data. If so, consider applying to the Summer of Maps program. Your organization will have a chance to receive pro-bono spatial analysis from Azavea-mentored Fellows. If you’d like to learn more about spatial analysis or Summer of Maps send me an email at tdahlberg@azavea.com.

Summer of Maps: Daytime Population Estimation and its Effect on Risk Terrain Modeling of Crime

This entry is part 6 of 6 in the series Summer of Maps 2014

Summer of Maps logo

Now in its third year, Azavea’s Summer of Maps Program has become an important resource for non-profits and student GIS analysts alike.  Non-profits receive pro bono spatial analysis work that can enhance their business decision-making processes and programmatic activities, while students benefit from Azavea mentors’ experience and expertise.   This year, three fellows worked on projects for six organizations that spanned a variety of topics and geographic regions.  This blog series documents some of their accomplishments and challenges during their fellowship.  Our 2014 sponsors, GoogleEsri and PennDesign helped make this program possible.  For more information about the program, please fill out the form on the Summer of Maps website.

 

When using Census data for research or analysis, sometimes the standard total population count for a region just doesn’t suffice. Transportation planners and crime analysts, for example, must account not only for residential populations but also “daytime” or “commuter-adjusted” population data, since many people spend most of their days working or running errands in different Census tracts, different towns, or even different regions from their homes. Nowadays we’re always on the go, so shouldn’t our population data reflect that?

I encountered this daytime population issue this summer while working as a Summer of Maps fellow with DataHaven to analyze the geographies of crime risk in New Haven, Connecticut. In this project, we used the Risk Terrain Modeling Diagnostics (RTMDx) Utility, a software application that uses crime data and the spatial influences of potential “risk factors” to model where conditions are ripe for crimes to occur in the future. One of the influencing factors of crime we used in our analysis was population density. While the exact effect of population density on crime rates is the focus of ongoing criminology research, in our study, we proposed that crimes would occur where high volumes of people were located. Since our focus here was on where people are “located” and not necessarily where they “live,” we incorporated commuter-adjusted population estimates to account for New Haven’s daytime population.

To acquire daytime population estimates, some data assembly is required. The Census Bureau provides instructions on calculating daytime population estimates using American Community Survey or Census 2000 population data. My first step in calculating daytime population was to download workplace geography data from Census Transportation Planning Products (CTPP), which includes Census data particularly useful to transportation planners. I selected “2006-2010 CTPP Tract-Tract work flow data” and followed the download instructions to get tract-to-tract population flow counts from residences to workplaces. I then queried the Access database to extract all records including a residence tract OR a workplace tract located in Connecticut to account for interstate commuting. With these statewide commuter counts, I was able to hone in on New Haven Census tracts and calculate the total number of workers working and/or living in New Haven. Lastly, I used the Census Bureau’s “Method 2” for calculating daytime population:

Total resident population + Total workers working in area – Total workers living in area

With both resident and commuter-adjusted population counts available, the next stage of the analysis was to incorporate this data into the RTMDx. I created risk terrain surfaces across four crime types (robbery, burglary, simple assault, and assault with a dangerous weapon) and two population counts (resident and daytime populations), producing eight risk maps in total. Each risk terrain model (RTM) included five risk factors in addition to the population count: foreclosures, bus stops, schools, parks, and job locations related to retail, entertainment, and food service (provided by Census LODES data via DataHaven).

In the figures below, we can compare the different risk terrain surfaces created for assaults using resident population and daytime population. The risk terrain surfaces are displayed with the “relative risk score” produced by the RTMDx Utility. To interpret this map, if an area has a risk value of 50, this means that the expected rate of assault in this location is 50 times higher than an area with a score of 1. The higher the score, the greater the risk of an assault based on the model.

image1

image2

 

In comparing the geographies of crime risk between resident and daytime population counts in central New Haven, we see generally higher risk scores when resident population is modeled. The heavily residential neighborhoods surrounding downtown New Haven see greater risk scores with resident population, perhaps owing to the fact that many residents here commute to jobs in other neighborhoods or cities during the day. Alternatively many of these neighborhoods, including Fair Haven, Newhallville, and Edgewood, see sharply increased risk scores when resident population is considered. The effect of population is more difficult to gauge in downtown New Haven, which is dominated by Yale University and Yale-New Haven hospital, the city’s two largest employers. Despite a much larger daytime than resident population, assault risk scores decreased when accounting for daytime population. This could be due to the nature of assault crimes in relation to population density, the geography of assault incidents in our crime dataset, the role of uncounted university students in influencing assault patterns, or other issues. Our results demonstrate that while daytime population is an important element to consider in risk terrain modeling, crime risk analysis remains a complex and inexact science.

While some spatial analyses may not require the granularity of daytime population estimates, using commuter-adjusted population data has important implications when exploring time-sensitive phenomena like crime or transportation dynamics. The Census may not be able to account for population spikes associated with university students, tourism or shopping, CTPP data still gets closer to understanding where people spend their days outside of the home.

Summer of Maps: A Spatial Tale of Five Cities

This entry is part 5 of 6 in the series Summer of Maps 2014

Summer of Maps logo

Now in its third year, Azavea’s Summer of Maps Program has become an important resource for non-profits and student GIS analysts alike.  Non-profits receive pro bono spatial analysis work that can enhance their business decision-making processes and programmatic activities, while students benefit from Azavea mentors’ experience and expertise.   This year, three fellows worked on projects for six organizations that spanned a variety of topics and geographic regions.  This blog series documents some of their accomplishments and challenges during their fellowship.  Our 2014 sponsors, GoogleEsri and PennDesign helped make this program possible.  For more information about the program, please fill out the form on the Summer of Maps website.

 

One of my two projects this summer as a fellow here at Azavea was working with CBEI, the Consortium for Building Energy Innovation, located here in Philadelphia. CBEI had an interest in understanding a national view on building energy use and the potentials for reducing energy consumption in five cities: Philadelphia, Washington, D.C., New York City, Minneapolis, and San Francisco. Over the last few years, cities and states have passed benchmarking and disclosure laws that require the owners of buildings – commercial, municipal, private, public, and non-residential – to report their annual energy use. Due to this recent wave of published data, the potential now exists for a comprehensive analysis across many cities. This project is certainly one of the first of its kind, since the data were so recently published, and it has already garnered interest from various city governments who are interested in learning of the results and the processes utilized throughout the project.

Comparing five cities is not always an easy task, however. Each of the five cities that we investigated for this project are located in different parts of the U.S., have different sizes, and have a different amount of census tracts – a common subdivision of a county with a population size between 1,200 and 8,000 people, generally. Most importantly, the benchmarked building stock for each city was not the same. While we initially embarked on the project with the goal of mapping benchmarked commercial buildings, we were only able to find one published data set for this type, simply because they have not yet been made public. See the following table for a description of which type of building stock we analyzed for each city:

 

City Name Type of Buildings (number)
Philadelphia Commercial (1,171)
New York City Non-Residential (2,240)
Washington, D.C. Private (490)
Minneapolis Public (101)
San Francisco Municipal (431)

 

Although there existed some overlap in the types of buildings analyzed for each city, is important to take note that each city published a different set of data that otherwise might not have been compared, if more data had been available. Additionally, the data sets were self-reported by the buildings involved, which is an indicator that errors may – and do – exist. Nonetheless, we set forth to break down each city’s building stock by five key variables related to energy efficiency: greenhouse gas (GHG) emissions, weather normalized source energy use intensity (EUI), ENERGY STAR score, building size, and year built. We chose these five variables because we found them to be good representations of energy efficiency and they were largely available for each of the five cities – with the exception of year built for NYC. Mapping each city individually was simple and informative. We generated one map per variable, per city, such as the one below.

GHG_PHL

As part of our analysis we wanted to create composite maps of both the cities and the variables in order to more directly compare and contrast. When it came the time to place 5 cities, showing the same variable, on the map, we noticed one major flaw: that the legend values were totally different, because they were each representing their own city, and therefore the highest category of values for one city could fall in the middle of the range for a different city. In order to correct this problem, we created an excel spreadsheet with every city’s values for each variable, and used the quantile tool to correctly distribute the values, accounting for each city. Quantiles are a great way to represent a dataset, because rather than dividing the data by arbitrary intervals, it separates the data by 25%: 0-25%, 25-50%, 50-75%, 75-100%. I initially thought that this method would not be a good way to represent the data, as some of the highest values for particular cities are so much larger than the highest values of a smaller city, like Minneapolis, but because quantiles use percentages and not intervals, it turned out to be a great way to display the data accurately.

EUI_Composite

 

While it can be tricky to display cities of different sizes, showing slightly different information, on one map, it is worth the effort to normalize all of the data in order to accurately make comparisons. It can be truly fascinating to see how two cities, thousands of miles apart, relate in terms of energy efficiency – how they are similar and how they are different. I hope that this project will lead to similar initiatives in the future to improve energy efficiency and reduce costs, especially as more data sets become benchmarked and released to the public.