Anime Pilgrimage Maps

Apparently many enthusiastic fans of anime like to go on anime pilgrimages, in order to visit the real-world locations that have featured in their favorite shows. Fans of anime, manga, or other animated media often embark on these pilgrimages to connect with the stories they love and to experience the places that inspired their favorite characters and scenes.

A number of interactive mapping websites cater to anime fans who like to explore the real-world locations which feature in animated shows. Even if you can't visit these locations in person these animation pilgrimage maps are a fantastic resource for discovering the locations used in your favorite shows and also allow you to explore these locations virtually.

Animation Pilgrimage (anitabi)

The Animation Pilgrimage interactive map features thousands of locations which have been used in an ever growing number of animes. If you click on one of the larger 'character' markers on the map then this opens a map sidebar displaying screenshots of locations used in the selected show (shown in chronological order of when the location was featured in the series). You can click on these screenshots from the show to view their real-world locations on the map. Each screenshot marker on the map when clicked reveals a link to view the real-world location on Google Maps.

AnimeNavi

Another popular anime interactive map has been created by AnimeNavi. Currently the map features the locations featured in three anime series, Heya Camp, Kimi no Na wa and Yuru Camp.

If you ignore the map and navigate to the dedicated page for each anime you can view individual episode breakdowns for each series of a show. On these episode pages individual locations featured in each episode are listed with links to OpenStreetMap and Google Maps. Follow the Google Maps link and you can then compare the actual anime depiction of the location with the real location on Google Maps Street View.

Anime Pilgrimage

Anime Pilgrimage (the only one of the three sites which is in English) features individual location maps for lots of different anime series. If you search for your favorite anime you can view a dedicated interactive map featuring the real-world locations used as settings for the animation. 

Under each anime map you can view a number of stills from the anime shown side-by-side with the same location as seen on Google Maps Street View. Click on the 'View Map' link and you can explore the location (virtually) for yourself using the interactive Street View imagery on Google Maps.

245 Russian Military Targets at Risk

The Institute for the Study of War (ISW) and Critical Threats have identified hundreds of Russian military sites that are in range of Ukrainian ATACMS. Currently the United States will not allow Ukraine to use US supplied tactical ballistic missiles for long-range deep strikes into Russia. According to the ISW its new map shows "the extent to which US restrictions on Ukraine's use of ATACMS constrain Ukraine's ability to strike important military infrastructure."

The map Known Russian Military and Paramilitary Objects in Range of Ukrainian ATACMS plots the locations of 245 different military sites, including Russian military installations, and regimental, brigade & division headquarters. The map also visualizes the ranges of Ukraine's ATACMS and HIMARS missiles inside Russian territory.

According to CNN this week Ukraine will present the US government with a list of Russian military targets which they believe are important in trying to stop Russia's continuing invasion of Ukraine. They hope that this list of targets will help to persuade the US to lift its current restrictions on Ukraine using American supplied missiles for long-range attacks on Russia.

The ISW also provides daily campaign assessments of the ongoing war in Ukraine. The Ukraine Conflict Updates includes daily static control-of-terrain maps and a regularly updated interactive map, Russia's Invasion of Ukraine, showing the extent of territory currently controlled by Ukraine and Russia. 

The Money Mountains of Los Angeles

Nick Underwood has visualized the average median household income in each Los Angeles neighborhood using the analogy of elevation. On his The Topography of Wealth in L.A. map city each neighborhood is displaced 'vertically based on median annual household income'. 

The map uses census data to show the average household income in each Los Angeles neighborhood as a 3D tower. Sea level on the map is set at the median US income of $59,000 per year. The map reveals that a number of L.A. neighborhoods, particularly in Beverley Hills and East Pasadena, soar above the median US income. However many of these wealthy neighborhoods ' lie immediately adjacent to others with income well below the poverty line.'

According to Nick the map actually underestimates the huge disparities in income between many neighborhoods in Los Angeles. The U.S. census caps household income at $250,000+ so it is probable that some L.A. neighborhood towers should actually be a lot taller.

Using the analogy of elevation to visualize economic and demographic data can be an effective way to present a dataset geographically. For example The Pudding's Human Terrain interactive map shows the world's population density using 3D population towers. On this map the taller a block then the larger the population. 

As you might expect the Human Terrain map shows that some of the most densely populated neighborhoods in LA are in Central Los Angeles, such as Pico-Union and Angeling Heights. These areas seem to be among the 'valleys' of the Topography of Wealth map, suggesting that the most densely populated areas of Los Angeles are also among some of the poorest neighborhoods in the city.

Text Search for Street View

all text for nyc (brooklyn) is a very impressive search engine which allows you to search Google Maps Street View imagery for any word. In the words of its developer it is "a unique digital archive of Brooklyn's typography. Users can search and visualize every sign, notice, and street art captured in street images".

Enter any word (or combination of words) into 'all text for nyc' and it will return interactive Google Maps Street View images from Brooklyn which contain your entered text. The 'about' section of 'all text for nyc' does not go into any detail about how the search engine was built, beyond stating it was made 'Using optical character recognition on street level imagery'. I'm guessing a pipeline was established using the Street View Static API to download all of Brooklyn's Street View imagery and then use machine learning to scan and extract all instances of words in those images.

all text for nyc is such an innovative idea that all its possible uses have not been fully explored or realized yet. I am sure that its ability to read street signs and also store names would be a fantastic resource for human-centric landmark-oriented directions (eg turn right at 'Jerk Chicken', go past the 'liquor store' and turn left at 'Danny's Donuts'. I am sure that 'all text for nyc' also has lots of possible uses which I wouldn't even come close to thinking of.

Also See - Text on Maps

Last year the David Rumsey Map Collection unveiled its Text on Maps feature which allows users to search one of the world's largest collections of digitized maps by text. The David Rumsey Map Collection contains over 57,000 geo-referenced vintage maps. Using the Text on Maps feature you can search these 57,000 historic maps for any word or combination of words (eg gold mine).

WorldGuessr on Street View

My seemingly endless quest to find an alternative to GeoGuessr may be finally over. GeoGuessr is (rightly) by far the most popular Google Maps game on the internet. Unfortunately GeoGuessr's subscription charges mean I can't afford to play it very often. Which means I'm always on the look-out for free alternatives.

GeoGuessr is an online geography game that challenges players to identify locations around the world based on their Google Street View images. Using the visual clues (such as the language used on road signs or the architectural styles of buildings) players have to identify the location where they have been dropped in Google's panoramic Street View imagery.

This is also exactly the same game play used in WorldGuessr. I have played a lot of GeuGuessr type games and I think WorldGuessr comes closest to the purest form of the original GeoGuessr game. I am a little worried however that WorldGuessr might not remain free for long. The Google Maps API charges are not cheap - which is why GeoGuessr now requires paid membership. 

At the moment if you identify a Street View within a correct country on WorldGuessr then you extend your 'country streak'. Get a country wrong and WorldGuessr asks if you want to watch an advert to keep your 'country streak' going. Currently you don't have to watch the adverts if you don't want to extend your country streak. This makes me think that WorldGuessr might struggle to keep the game free based on this model.

Just in case WorldGuessr is also forced to introduce a subscription only model you might also want to check out the games in this Six Free Alternatives to Geoguessr post.

How Smooth is the Earth?

One of the most interesting facts that I learned from the BBC TV series QI was that the Earth is smoother than a billiard ball. Unfortunately, like nearly every other fact I've remembered from that show, it is a load of bull (or should that be balls).

Shri Khalpada has created a three.js powered 3D globe of the Earth which allows you to view the Earth's elevation profile to scale (and when exaggerated). If you compare the 'to scale' globe in How Smooth is the Earth? to the exaggerated profile you immediately get a sense of how smooth the world actually is.

However, despite its relative smoothness, the Earth is not a smooth as a billiard ball. Alongside his visualization of the Earth's elevation profile Shri Khalpada links to Dr David Alcitore's paper Is a Pool Ball Smoother Than the Earth? (PDF) which concludes that "the Earth would make a terrible pool ball ... (the) Earth ball would ... be terribly non round compared to high-quality pool balls."

If you don't want to read a scientific paper comparing the Earth's smoothness to a billiard ball then XKCD's Bowling Ball does a very good job at explaining why the Earth is also not as smooth as a bowling ball. 

Have You Earned Your Air Pollution Stripes?

Ed Hawkins' Climate Stripes visualization of global heating has quickly become a data visualization classic. Climate Stripes (sometimes known as Warming Stripes) are a visual representation of the long-term increase in global temperatures due to climate change. Ed's striking Climate Stripes visualizations consist of colored vertical stripes, with each stripe representing a single year and the colors indicating the temperature anomaly for each year relative to a baseline period.

Inspired by Climate Stripes Edinburgh University has created Air Quality Stripes visualizations for a number of global cities. Air Quality Stripes use colored stripes to indicate average yearly air pollution levels in cities since 1850. Like Ed Hawkins' Climate Stripes these Air Quality Stripes are effective in providing a simple and easily comprehensible visualization of historical trends in a given dataset.

Each city's Air Quality Stripes can instantly convey the history of air quality in a city. For example, as Edinburgh University notes, the stripes demonstrate "the air in many cities in Europe is much cleaner now than it was 100 years ago." In contrast (as you can see in the screenshots above) the air quality in many Indian cities has become dangerously worse in the 21st Century.

Edinburgh University actually provides four different types of yearly air pollution levels for each city. Despite the effectiveness of the air pollution stripes visualization of the data I actually prefer the more traditional bar graph visualizations of the same data. These bar chart visualizations of yearly PM2.5  concentrations include reference lines to show the WHO Guideline (a target of a concentration of 5 micrograms om PM2.5 per cubic meter) and Edinburgh University's own 'Very Poor Air Quality' level. Alongside the use of color the height of each year's bar provides an additional guide to the average levels of air pollution.

200 Years of Irish Maps

The first ever large-scale survey of an entire country was started nearly 200 years ago. From 1825 to 1846 the Irish Ordnance Survey undertook a highly detailed survey of the whole of Ireland in order to create maps primarily at the 6 inch scale.

To celebrate 200 years of Irish mapping the University of Limerick and Queen’s University Belfast has created OS200. The OS200 website is a digital archive of Ireland's Ordnance Survey which allows anyone to browse and explore the Ordnance Survey's First Edition Six-Inch Maps, the OS Memoirs, Letters and Name Books.

The maps themselves are exquisitely detailed and beautifully drawn. Thanks to the digitization work by the OS200 project you can now explore these original Ordnance Survey maps of Ireland in the closest detail as interactive maps. If you are Irish, or have ever visited Ireland, you can have hours of fun exploring places you know on the OS maps, as they looked 200 years ago. You can have just as much fun browsing the Ordnance survey Name Books.

As well as spending years scientifically surveying Ireland the Ordnance Survey sent out agents of the Topographical Department to collect and compile lists of the historical forms of place-names to determine the correct place-name labels to be used on the maps. These Name Books list place-names (with English translations and alternate spellings) but also provide details on the people who live at each place, the people's religions, who owns the land, and who leases the land. It also lists information on the types of crops grown and the condition of the soil. These non-etymological details hint at one of the original purposes of the map -to help the British government levy local taxes.

The broader governmental aims for creating a national map of Ireland are also apparent in the Memoirs. As well as the geographical surveys and place-name collections the Ordnance Survey staff were required to gather additional information "on social and economic conditions, ... the landscape, topography, nature, geology, historical monuments and antiquities, estates, mills, infrastructure, people and culture ..., communications and (provide) 'suggestions for improvement'". These memoirs provide a fascinating glimpse into local life in Ireland in the early 19th Century.

Population Flags

World Population Flags is a Dorling cartogram in which country flags are sized by population. The cartogram is used to visualize where people live around the world and the relative size of each country's population.

A Dorling cartogram is a thematic map that is used to visualize quantitative data. On a Dorling cartogram areas are represented by circles rather than their actual geographic boundaries. The size of each circle is proportional to the variable being represented. In the case of World Population Flags the variable being represented by the circles is country populations.

Dorling cartograms are particularly useful for making it easy to compare values across regions, especially when the actual geographic area sizes are misleading or irrelevant to the data being displayed. For example in World Population Flags the world's biggest country in land area, Russia, appears to be roughly the same size as Bangladesh (which is much smaller in terms of geographic area) because both countries share a similar population size.

The Dorling cartogram in Word Population Flags is used in a scrollytelling presentation to take a closer look at population trends in the world's populated continents. As you scroll through World Population Flags the map zooms and pans automatically to illustrate the relative size of each continent's population. At the end of the presentation you can explore the cartogram for yourself, and hover over individual circles to view each country's population.

You can explore two other methods of visualizing population data on maps in the Maps Mania post If the Romans did Data Visualization.

One Million Screenshots. One Map!

Over the years a number of people have used the popular Leaflet.js mapping library to map image datasets. For example Nathan Rooy's Visual Book Recommender uses Leaflet to map the images of 51,847 book covers. The Pudding has also mapped images of 5,000 book covers on its 11 Years of Top-Selling Book Covers, Arranged by Visual Similarity. 

Mapping libraries have also been used in the past to visually map the internet. For example the Internet Map (which appears to be now dead) used the Google Maps API to visualize the 350,000 largest websites in the world. On this map different sized circles were used to represent individual websites on the Internet. The sizes of the circles were determined by the amount of traffic to each website - the larger the amount of traffic, the bigger the circle. The location of websites on the map was determined by the active hyperlinks between the sites.

Now One Million Websites has created a map which visualizes screenshots of the top one million websites in the world. One Million Screenshots is an interactive Leaflet map which allows you to pan and zoom around the screenshots of the top 1 million websites. To make the map Urlbox took screenshots of the top 1,048,576 ranked websites by Common Crawl Web Graph. 

Usually on maps of large image datasets some attempt is made to map the images so that related images appear near to each other. Whatever category One Million Screenshots has used to map their images doesn't appear to have worked very well. For example the screenshot of the New York Times on the map is flanked on one side by the website of the Municipality of Pictou County and on the other by Websunday (a Japanese manga magazine). I struggle to understand how these three website screenshots come to be mapped so closely together.

It isn't even as if Urlbox hasn't spent time trying to categorize similar sites. If you click on a screenshot on the map and go to a website's dedicated page you can view a number of different categories of 'similar sites'. If you select the 'similar description' option here you can find a lot of websites which do appear to be closely related. The 'similar description' metric looks to me to be the one that Urlbox should have used to determine the position of each website screenshot on the One Million Screenshots map.

Via: Data Vis Dispatch