Workshop report: Building Linked Data heatmaps with Clojurescript &

Last week, during the second 3-day data visualization workshop with Clojure, Clojurescript and the libraries, we built a small web application using data from the UK’s Office for National Statistics (ONS) and the London Data Store.

Still from a previous London 3D data viz project done with & for The Open Data Institute in 2013, here showing knife crime incidents by borough.

The workshop was intended to introduce participants to important Clojure/script concepts (e.g. laziness, reducers, transducers, concurrency primitives), data structures (sets, maps, protocols, custom types), libraries (core.async, reagent, and workflows (figwheel), as well as teach Linked Data & Semantic Web concepts, technologies, standards and learn how these can be used to combine data from disparate sources, in order to simplify their analysis and later visualization. After much theory, sketching and many small, isolated exercises, we finally tied everything together by building an interactive heatmap and price charts about London property sales, prices & trends (data from 2013/14) in both Clojure and Clojurescript. The web UI also includes a query editor to experiment with new queries (and visualize their structure) directly in the browser…

Heatmap of average London property price per borough (2013/14) shows clear bias of high-price areas. Important note: We only used a sample of ~23k transactions to save time during the workshop. The full dataset published at contains >200k transactions.
Alternative heatmap based on number of sales per borough in 2013/14. There were ~2x as many sales in the south-east (orange) than in other areas (dark blue).
Charts of individual property sales per borough in 2013/14, sorted by date. Note the clearly visible upward trend for most boroughs. Charts are generated for all 33 boroughs, with the remainder omitted here for space reasons.
Screenshot of the query editor and auto-generated visualization of the shown query’s structure (courtesy and Graphviz on the server, editor uses CodeMirror)

Clone the project from Github and run locally (instructions further below).

The remainder of this article sheds some more light on implementation details, the role of the libraries in realising this project and lots of links to further reading…

Marcin Ignac (one of the participants and fellow computational designer) also just shared some of his own workshop experiences (and some great “homework” examples) over on his blog.

Graphs, RDF, SPARQL and the Linked Data model

Some background: I’ve been fascinated by graphs as general data structure for a long time and think it’s safe to say they’re becoming an ever increasingly popular choice, especially for greenfield projects. Graphs are more natural and friendlier to deal with than tables or document stores when dealing with complex data relationships and many large public datasets have been expressed as graphs way before neo4j appeared and had any commercial success and way before Facebook’s GraphQL, which recently/finally introduced the whole idea as big news to the big wide world of JS devs.

With all the current excitement around these two platforms (for example), I’m always somewhat taken aback by the fact that so many developers have either never heard of RDF (the W3C’s Resource Description Format) or (worse) never want to work with it again. I can somewhat share the top-level thinking behind the latter sentiment, since historically (RDF is in existence since 1999), the format had been closely associated with XML (originally the de-facto encoding) and its large/verbose Java tooling. However, RDF is an abstract data model encoding knowledge statements with clearly defined semantics. It’s not tied to XML or any other particular representation. All what really matters is a standard way to encode data / knowledge as triples of subject, predicate, object, and use, where possible, URIs to provide uniqueness and dereferencing capabilities to lookup unknown terms. The result of this extremely simple setup is that knowledge can be encoded and stored completely distributed, largely becomes self-describing, and equally important, becomes open for aggregation, regardless of where a piece of data has been retrieved from. This is what the term Linked Data (LD) stands for.

Placing our data in a graph system not supporting these principles, will still give us potentially more flexible query capabilities locally, but will not automatically solve the old questions of how to easily combine knowledge from multiple sources or how to provide our own data in a semantically, interoperable format to others.

The Freebase Parallax UI, a research project by David Huynh from 2008, still is one of my favourite examples, nicely showing this current limitation and contrasting it with the potential a Linked Data approach can offer (even though he’s only using one large dataset in the example [Freebase]). Now, 7 years later we still can’t get answers like this from the market leaders in search and this lack of interoperability in many (most?) public datasets is also holding back entire disciplines (UX, UI, data vis) at large:

Over the past 10 years the LD community has adopted a number of alternative, more lightweight formats, standardized embedding of metadata in HTML (RDFa), defined licensing options etc. RDF is also increasingly used and embraced by the biggest commercial players and governments worldwide. Institutions like the Open Knowledge Foundation and The ODI are actively furthering this course by tirelessly working with holders of datasets large & small.

The Linked Open Data cloud as of 2014, an overview of interlinked open data sets describing over 8 billion resources

In addition to the sheer amount of Linked Data available, there’re are as well hundreds of well-defined, freely available data vocabularies (ontologies) to define terms and express semantics of all complexities in a standard, interoperable and machine-readable way — something anyone seriously interested in working with data analysis / visualization should be embracing, or at least be welcoming… For many use cases, a handful of core vocabularies is sufficient to at least express the most common relationships in an interoperable manner. Data integration almost always is a continuous effort, but small, incremental changes can go a long way. It’s also important to recognize that these vocabularies themselves are expressed in RDF, so there’s no distinction between data and language. This should feel familiar to any Lisper/Clojurian ;)

Back to the workshop exercise…

The LD portal of the UK Statistics Office provides access to structured geographic data, which can be directly queried via their SPARQL endpoint. SPARQL is the RDF world’s SQL, a vendor independent standard, query language, protocol and service description for semantic data. Its query language part is heavily inspired by Prolog, Datalog and other pattern matching/unification approaches, which makes dealing with graph based data very easy: Simply state the patterns/relationships you wish to find, add some constraints (filters, unions, negations etc.) and modifiers (aggregation, sorting etc.) to manipulate the result set. There’re loads of more unique options, e.g. federated queries of multiple sources at once. See the primer for details.

Dataset 1: London geodata

We used this query to extract a subgraph of all London boroughs and their lat/lon boundary polygons:

Dataset 2: London property sales

The second dataset we used comes from the London Data store and constitutes ~650MB of all property sales between 1995–2014. This data comes in “glorious” CSV flavor, with several files using different column layouts and so, of course, requiring special, manual treatment. Oh the joy!

I prepared a little Clojure utility namespace and demonstrated how we can convert the CSV to a RDF graph model by using terms from the general purpose vocabulary (and supplement a few ad-hoc ones of our own). From a user perspective of this code, this boils down to just this:

With both the geo data already in graph form and the house sale transactions converted, we were ready to place them into one common container and acquainted ourselves with the library. is a still young, modular framework for Clojure/Clojurescript, providing a general purpose compute graph model as the foundation to build more context specific applications on (from spreadsheets, navigation, inferencing to knowledge graphs). In the compute graph, nodes store values and can send & receive signals to/from any connected neighbor. At first, this sounds similar to the well-known Actor model, however in the Signal/Collect approach this library is loosely based on, each graph edge is represented as a function, which can transform or inhibit outgoing signals and hence perform computation on the original node values (similar to the mapping phase of Map-Reduce). Furthermore, the collection of received signals also happens via user defined functions, enabling further transformations only possible when combining multiple signals (reduction). A choice of customizable schedulers (incl. parallel & async options) allows for different approaches to control the overall computation. Many graph algorithms can be expressed (more) succinctly using this setup, but for the workshop we focused on the two library modules allowing this architecture to be used as a fairly well featured and ready-to-go Linked Data development server. To my best knowledge, it’s also currently the only pure Clojure solution there is thus far. &

These two library modules together realise:

What do I mean with “SPARQL-like” query engine? Well, since I’ve been using Clojure for most of my data centric work in recent years, I wanted to have the ability to apply these kind of queries to any data without having to restrict myself to the requirements of pure-RDF tools. Furthermore, since the boundary between code and data can be easily blurred in Clojure, it made sense to define everything in an as Clojuresque as possible way, e.g. by using Clojure data structures (maps, symbols, s-expressions) to define the queries and gain programmatic manipulation/construction as a result. In some way this is similar to Allegrograph’s SPARQL S-expressions, though these were not a motive and is quite natural to do in any Lisp. The other reason for “SPARQL-like” is that fabric still being a young project, not all aspects (e.g. federated queries, construct queries, named graphs) are yet implemented, but it’s a work-in-progress.

As an example, the query to compute the complete aggregate heatmap data and lat/lon polygons for all boroughs, can be expressed with this Clojure EDN map. Note: The :aggregate expressions are not function calls, but will be compiled into functions during query execution:

Ready, set, go…

Use the commands below to clone & launch the workshop project locally on your machine. You’ll need Leiningen and Java 1.7+ installed. The project is also configured to ask for up to 1.28GB of RAM for the server part. Figwheel will temporarily also require quite a bit of memory, so will your browser.

The readme for the LD module contains several examples how to interact with the server via HTTP.

Important: Do not view the web app via localhost:3449 (figwheel’s port), since none of the queries will work (originally due to CORS, but I also switched to relative query paths). Use http://localhost:8000/ only, figwheel will still apply in any code changes via its WebSocket connection.

The code in the repo is fully commented and also acts as an illustrated use case and combined example of various libraries in the collection (among others). A quick breakdown of the various parts follows:

Clojure server

This project uses a custom fabric.ld server setup and loads the two datasets discussed above during startup (see ws-ldn-2.core namespace). Zach Tellman’s Aleph is used as the default underlying HTTP server for the fabric.ld module, since it provides a nice mechanism for deferred response handling, which can be important when handling long, complex queries. We also injected a server route to handle the structural visualization of a user’s query sent from the browser-based query editor. This handler shells out to Graphviz, which needs to be installed and on the system path. To install on OSX:

brew install graphviz

Clojurescript frontend

Our main focus of development was on the CLJS frontend parts of the app, which (like 99% of all Clojurescript web apps) is based on React.js. Of the available wrappers, I’ve always found Reagent the most lightweight and least painful, so proposed to use this here too. Together with a completely datadriven 100LOC router, which I’ve been using for many of my own projects and a sprinkling of Bootstrap, we had a basic SPA skeleton app running in no time.

Integrating 3rd party JS libraries into CLJS projects used to be somewhat painful until not so long ago. However, since the advent of the cljsjs project, which is re-packaging JS libs for CLJS, this is thankfully a thing of the past and in some ways almost easier to handle than with npm. As an integration example, we imported CodeMirror and used Reagent’s create-class mechanism to build a “reactified” editor instance with Clojure syntax highlighting, learned about the reaction macro mechanism to minimize component render updates and experimented with submitting queries to the server and visualizing their structure.

Always be visualizing…

In a somewhat longer next step, we then introduced the SVG and visualization modules, which also form the backbone of our heatmap visualization. After learning about the general approach, output format independence and looking through various examples (incl. the SVG 3D renderer with software facet shaders), we transformed and projected (mercator) our query results into valid SVG polygons and then started working on the actual heatmap of London property sales.

Dynamically generated 3D meshes rendered in SVG with different (composable) software shaders
Blender’s Suzanne imported as STL and rendered in SVG with Phong sofware shader

In order to translate a value range to colors, some form of gradient lookup table (or function, or both) is required. The library provides a namespace to define complex color gradients using just 12 numbers (4x RGB cosine wave params). The original idea for these gradients comes from the master, IQ himself, and the library provides a few useful presets for our purposes (The library also provides some of Cynthia Brewer’s categorical pallettes which are often better suited for visualization purposes).

Example gradient presets

Adding some of these presets to a dropdown menu, then allows the user to see London in “different colors” (not all of them good or useful):

Heatmap based on average sale price per borough
Heatmap based on number of sales per borough, same color preset as above. Dark green lowest, cyan highest numbers.

Using core.async & SVG chart pre-rendering

Generating the 33 SVG charts (one per borough) with some of them consisting of >1200 data points is taxing for the browser, so I decided to pre-generate the SVG only once during initial page load and query processing. Caching these SVG elements is easy in Clojurescript (and Reagent), since the geom library generates them as pure Clojure data structures (nested vectors) in hiccup format. In the React component, which later creates the full DOM for each chart, we then simply need to insert the cached vectors representing the SVG DOM fragments and have nothing else to do.

At application start we execute two queries: 1) Retrieve the set of polygons and aggregate values for each borough, 2) Obtain individual property sale transaction details, about 23,000… Both of these queries utilize fabric’s registered query feature, which means these queries are stored as part of the compute graph and their results are always immediately available (without incurring new work for each connected client) and will update (on the server side) automatically, should the underlying set of facts change. Since the second query returns approx. 1.4MB of EDN data which needs to be parsed, processed and transformed into SVG charts, the entire application startup process is handled asynchronously using the fabulous clojure.core.async library. Replacing the original callbacks with async channel operations to coordinate the different processing steps, allowed us to keep a linear structure in our functions and avoid blocking the DOM during pre-processing of the charts.’s visualization engine is completely declarative and essentially transforms a specification map definining all axes, datasets, layout methods and styling configs into a nested DOM-like data structure. Because both the initial visualization spec and the result is pure data, it’s easily possible for either to be defined or manipulated programmatically/interactively. E.g. Changing axis behavior or layout method is very easy to do: just update a key in the input spec map or add an event listener in the result tree, post-generation. Together with the other CLJS workflow ingredients (e.g. figwheel live code updates), this allows for a quick iterative design exploration…

Here we explore the impact of different axis scales and rendering methods:

Using a linear scale y-axis is a bad choice for this data due to extreme price fluctuations in some boroughs (e.g. outliers like Kensington’s 27.9 million or Lambeth’s 7 million property sales cause havok)
The same data for the same boroughs mapped using a logarithmic scale
And once more using line chart with gradient

Previous workshop

For completeness sake, this workshop was intended for an intermediate Clojure audience and we went through a lot of topics over the 3 days. The week before I ran a similar workshop more aimed at beginners and we produced a visualization of 47,000 airports using CSV data from

47k airports (magenta = IATA, cyan = non-IATA)

The Github repository for that workshop has more information.

Future workshops

In order to support the open source development of the tool chain and to provide more learning resources, I’m intending to run more workshops like this (also more regularly), over the foreseeable future. These will not only be focused on Clojure — I absolutely believe in combined skillsets! If you’re interested, please do check for more details of upcoming workshops and/or sign up to the no-spam announcement newsletter.

The next workshop will be about one of my other passions:

Embedded devices, ARM C programming and DIY polyphonic synthesizer

London, 5–6 December 2015

The ARM Cortex-M processor family is used in many embedded devices, from IoT, wearables, phones and more demanding use cases and is rapidly gaining traction. This workshop will give you an overview and hands-on experience how to program the STM32F4 (a Cortex-M4 CPU) and work with various peripherals (GPIO, gyro, USB I/O, audio). Over the 2 days we will be building a fully customizable, CD quality, polyphonic MIDI synth and cover some generative music approaches to round off.

Finally… Clojure community FTW!

This workshop would not have been so easily possible without the amazing & innovative work done by others in the Clojure community. If you want to learn more, the following Clojure/script libraries were used (and essential) for this project & workshop: