Saturday, February 23, 2019

Mapping Real-Time Road Traffic

This interactive map of Palo Alto shows the live status of traffic signals. The map also uses live data to show how many vehicles are waiting at different intersections and how many pedestrians are waiting to cross at each intersection. The live traffic data on the map comes from TrafficWare's streaming TidalWave API.

The colored traffic approach lanes on the Traffic map show the color of the traffic signal when approaching from that direction. If you click on a colored traffic approach lane you can actually view a countdown timer to when the traffic signal controlling that lane will change. Opaque approach lanes mean that at least one vehicle is currently being detected in that lane. Translucent approach lanes mean no vehicles are currently detected.

The purple intersection markers mean a pedestrian is currently waiting to cross at that intersection. Animated circular blue ripples emanate from intersection markers when new vehicles are detected. Purple circular ripples appear when new pedestrians arrive at an intersection. The dashboard counters (down the right-hand side of the map) show the numbers of flowing vehicles and the number of vehicles waiting at intersections. The purple counter shows the number of pedestrians in total waiting at intersections.

Dissecting Planet Earth

Earth Elevation provides an interesting view of the elevation of the Earth along circles of latitude. That sounds more complicated than it actually is. You can see Earth Elevation in action in the screenshot above, which shows the elevation & ocean depth around the world at 30° north of the Equator. This cross-section shows the relative height of the Rockies in America and the Himalayas in Asia. You can also see the depths of the Pacific and Atlantic Ocean.

Earth Elevation uses elevation and bathymetry data to provide cross-section views of the Earth. Using the right-hand slide control you can adjust the line of latitude shown in the visualization. The figures on the left-hand side of the visualization show the elevation height of the land around the world at the selected latitude and the depths of the oceans.

Friday, February 22, 2019

Searching for Slums with Machine Learning

Machine learning techniques are being increasingly used to detect features in satellite and aerial imagery. Artificial intelligence and machine learning can be used to train algorithms to search for familiar patterns in aerial imagery of the Earth. The reasons for searching satellite imagery can be varied and can be for commercial, environmental or social purposes.

One example of machine learning being used to identify common features in aerial imagery is OneSoil, which uses AI to detect where different types of crops are being grown around the world. Another example is Земляна проказа, which was created using machine learning to identify Ukraine's illegal amber mines. Another example, recently covered on Maps Mania, is Curio Canopy, which has used machine learning based techniques to identify tree canopy cover in European cities.

Another example is Dymaxion Labs Maps of Potential Slums and Informal Settlements. Dymaxion Labs used machine learning to search the satellite imagery of a number of South American cities in order to identify and find slums and informal settlements. The resulting maps are being used to help urban planners and local councils identify where vital utilities need to be directed.

To help identify the informal settlements Dymaxion Labs used the Random Forest machine learning technique. You can read more about the process on the Mapbox blog.

The Map of Science Fiction

In a parallel universe '100 Years of Sci Fi' is called '100 Years of Non-Fiction'. In a completely different parallel universe it is known as '100 Years of SYFY'. We don't live in either of those parallel universes so we shall call it by its given name.

100 Years of Sci Fi is an interactive map of science fiction novels listed on Good Reads. All the sci-fi novels on the map are organized by similarity. In other words novels which share common sci-fi themes are grouped closer together. Using the map you can search for your favorite novels and authors and find other works of science fiction which share common themes.

Novels on the map are linked when they share common keywords on Good Reads. The novels are placed in clusters of works which share similar keyword signatures. If you click on the 'Legend' button in the map sidebar you can filter the map to show all the works that share a common keyword theme. The map menu also allows you to filter the maps shown by theme, concepts and date of publication.

If you click on an individual novel on the map you can read a brief synopsis of its plot, view its list of keywords and its date of publication. 100 Years of Sci Fi was created using openmappr a visualization tool for mapping networks.

Exploring Britain's History from Above

You can now explore over 80,000 historical aerial photographs of Britain dating from 1945-2009. Cambridge University’s Collection of Aerial Photographs contains thousands of aerial photographs captured by the University’s Air Photography Unit since the end of World War II. There are nearly half a million aerial photos in the collection. 80,000 of them have now been digitized and can be found and viewed on the Cambridge Air Photos website.

You can search and browse the aerial photos of Britain by location and by category. If you want to search the collection by location then you can use the Cambridge Air Photos interactive map. When zoomed out the map only shows a selection of the available aerial photographs. You need to zoom in on the map before all the available photos will appear on the map.

Some of the best images in the collection have been organized into featured collections, such as Ancient Britain, Castles and Coasts. Around 1,500 of the aerial photos are also available as high resolution zooming images. These images are also being made available using International Image Interoperability Framework (IIIF) technology, so you can explore them in detail on any IIIF viewer.

Thursday, February 21, 2019

Satellite Remote Sensing Analytics

The Earth Observing System's LandViewer application is an online GIS-assistant which provides a set of advanced tools to perform real-time remote sensing data analytics. The site allows you to access and use a constantly updated satellite imagery catalog.

One of the coolest features of LandViewer is the ability to create your own time-lapse animations using satellite imagery captured over time. With this function you can easily create videos or animated GIF's which show changes to locations over time. For example, in the image above you can see the construction of the Bhadla Solar Park in India between 2016 and 2019 using Sentinel-2 satellite imagery. The time-lapse tools in LandViewer allow you to customize the quantity of frames per second, resize the video, create a GIF, show dates and download the results.

The imagery in LandViewer provides access to imagery from the world’s top providers of high-resolution satellite imagery, including Airbus Defense & Space, SI Imaging Services, and SpaceWill. It also allows you to browse, preview and purchase products from Pléiades 1a/1b, SPOT 5, SPOT 6 and SPOT 7, as well as KOMPSAT-2, 3, 3A and SuperView, Gaofen 1, 2, and Ziyuan-3.

LandViewer includes tools which allow you to perform time series analysis of satellite imagery. For example you can perform analysis of vegetation growth, crop identification, and land use change. Landviewer is therefore an important tool in a number of different fields. In agriculture monitoring it can be used to identify high/low crop yield and identify different types of vegetation areas for fertilizing, sowing/resting, and watering. In coastal monitoring LandViewer can be used to analyze coastal zones and to analyze water temperature, salinity, phytoplankton, and potential threats to shores. In forestry LandViewer can be used to identify identification of specific zones by vegetation density, vegetation type and monitor deforestation.

The World Map of Shipping Traffic

This map of global shipping density reveals the world's major shipping lanes and also the areas of the world that the major shipping companies avoid. The reasons why some areas of the world's seas and oceans don't see as much traffic as others can vary from geo-political reasons to the dangers of piracy and local sailing conditions.

The live ship tracking map MarineTraffic includes an option to view a density map of the world's shipping traffic. If you select the 'Density Maps' overlay on MarineTraffic you can view an overlay which shows the accumulated recorded data of all vessels on MarineTraffic over recent years.

If you zoom in on the coastline of North Korea on MarineTraffic you can see that there don't seem to be many ships breaking the international trading sanctions. The coastline of Somalia is another area which seems to have less dense marine traffic than other countries' coastlines. The reason that ships avoid Somalia however is presumably more to do with the dangers of piracy.

The Gulf of Sirte off the coast of Libya is another area with a low density of ship traffic. According to Wikipedia the dangers to boats in the gulf have been known for centuries, "Ancient writers frequently mention the sandbanks (in the gulf) and their vicinity as dangerous for shipping".  Elsewhere marine traffic might avoid coastlines because of Emission Control Areas. The EU, the US and Canada all have controls which force ships to use cleaner and more expensive fuel near coastlines.

The different types of routes and journeys taken by different types of marine vessel around the UK can be seen in a series of maps by Alasdair Rae. In Watching the Ships Go By Alasdair has created a number of static maps showing the vessel tracks of different types of vessel in the coastal waters around the UK. These include maps showing the different routes taken by cargo ships, passenger ships, fishing boats, high speed craft, military vessels, tankers and recreational craft.

You can also explore the different shipping routes of different types of vessel using is an outstanding animated interactive map visualizing the movements of the global merchant shipping fleet over the course of one year. The map uses AIS shipping data from exactEarth to visualize the movements of different types of cargo ships over the course of 2012. allows you to filter the ships shown on the map by type of cargo vessel. The narrated tour provided with this map also explains some of the interesting patterns that emerge from mapping the worldwide merchant shipping trade.

The History of Settlements in Hungary

Partly inspired by the New York Times' popular Map of Every Building in America the Atlo data visualization team decided to undertake a detailed examination of the street patterns of urban settlements in Hungary. In Roads, Buildings, Networks Alto looks at the different types of settlement morphology and the history of urban growth and planning in the country.

Roads, Buildings, Networks includes an interactive map which shows only the outlines of building footprints and roads. The data for this map comes from OpenStreetMap. Unfortunately the building footprint data for Hungary on OSM can be a little patchy, however the Atlo map does provide an interesting view of the street patterns of Hungary's urban settlements.

Accompanying the interactive map is a very detailed explanation of how terrain, hydrography, history and other factors have helped shape Hungary's different urban environments. Through this exploration of settlement morphology Atlo identifies distinct forms. These include the spherical street patterns of the historic mining towns and the regular chessboard-like street patterns which resulted from the urban planning which began in the 17th century,

Wednesday, February 20, 2019

Navigating the Green Book

In the 1930's Victor H. Green started publishing an African-American travel guide (first published as 'The Negro Motorist Green Book' and later as 'The Negro Travelers' Green Book'). In the guides Green reviewed hotels and restaurants which welcomed Black customers during the time of Jim Crow laws and racial segregation.

Back in 2013 the University of South Carolina created an interactive Green Book Map which visualized over 1,500 listings from the Spring 1956 Green Book. Unfortunately the University of South Carolina's interactive Green Book Map has suffered the Google Maps API kiss of death and now all the map tiles are stamped with unsightly 'For development purposes only' warnings. However there is no need to worry as you can peruse the new NYPL Navigating the Green Book route planner instead.

Navigating the Green Book allows you to plan a route anywhere in the USA and find hotels, restaurants and bars which were welcoming to African Americans during the times of segregation. The map attempts to show a restaurant every 250 miles and a lodging every 750 miles. You can read more about the map, the Green Book and the New York Public Libraries attempts to map the entries in the Green Book at the NYPL Digital Collections.

Mapping the Carbon Costs of Flying

Flying is one of the worst things that you can you do for the environment. Aircraft are just about the worst forms of transport in terms of the CO2 cost per passenger kilometer. The UK's Department for Transport claim that flying is responsible for at least 6.3% of the UK's CO2 emissions. Many environmentalists say that this is an underestimation.

The Flight Emissions Map allows you to calculate the carbon cost of every single air flight that you have taken. Using the map you can enter flights that you have taken or plan to take simply by clicking on cities on the map. Once you have entered each flight the map shows you the amount of CO2 emission of that flight in kilograms. You can enter more than one flight on the map and receive a carbon cost for each flight and an overall total.

The map calculates the total emissions of a flight based on an estimation of the CO2 emitted for each kilometer of travel. The Flight Emissions Map uses an equidistant azimuthal projection. The red lines on the map follow a great circle and each red line shows the equal distances from your selected point of departure.