Friday, April 19, 2019

European Gerrymandering

How do you make regional funding in the European Union interesting? With an interactive map - that's how. As a map fan you might already be intrigued by The Pudding's headline Why EU Regions are Redrawing Their Borders. When I tell you that the article is illustrated with a sublime story map then I know you will be hooked.

Maarten Lambrechts' report into how some EU countries are gerrymandering their regional boundaries in order to qualify for European Union funding is explained with the use of an interactive map. The map is used to explain how the EU calculates regional aid based on economic development, which EU countries are currently more and less economically developed and how some countries are redrawing their regional boundaries in order to ensure that they qualify for EU funding.

As you scroll through the article the story map updates to illustrate these points. I particularly like how the map also updates when you hover over highlighted words in the text. Some words in the map sidebar are highlighted in color. These colors match the colors used on the choropleth map. If you mouse-over these words in the sidebar then the countries matching the selected data range are highlighted on the the map. Later in the story country names are highlighted in white in the text and when hovered over these named countries are also highlighted on the map.

The map itself is completely custom made using SVG. This allows the data on the map to be animated to further illustrate how some regions are being redrawn to ensure areas qualify for economic support from the EU. At one point the regions on the map animate into a chart showing the economic development of all the regions in each country (if you scroll back up the page you can watch these points on the chart animate back to form a map of Europe).

In Europe capital cities are often more economically developed than the surrounding region. Why EU Regions are Redrawing Their Borders shows how some countries have redrawn their regional borders so that the capital is placed in one region and the surrounding region then becomes another separate poorer region, which then qualifies for EU funding.

Where Germans Drive Fastest

The German autobahn system includes stretches where motorists are allowed to drive as fast as they want. These speed-unrestricted stretches do have an advisory speed limit of 130 kilometres per hour (81 mph) but going faster isn't illegal (however if a driver has an accident while driving at over 130 kph they may face increased liability).

Zeit has used data from TomTom to workout how fast Germans drive on average on each stretch of the autobahn. An interactive map on Where Germany is Racing shows the average speed of all drivers across the autobahn network and the average speed driven by the fastest 10% of drivers. Around 70% of the autobahn has no speed limit. The other 30% has different speed limits depending on certain factors (such as the level of urbanization, the road condition and road safety). Using TomTom's data Zeit discovered that the lower the speed limit the more drivers tend to ignore it.

On the sections which have an advisory speed limit (130 kph) then the average speed is 122 kph (75.8 mph). Only 30% of drivers drive faster than 130 kph on the speed unrestricted sections of the autobahn. Only 12% drive faster than 150 kph (93.2 mph).

Zeit's online map doesn't allow you to filter the sections of autobahn shown by the average speed of all drivers or the average speed of the fastest 10% of drivers. However towards the end of the article Zeit has published a static map which shows the sections of the autobahn network where the fastest 10% of drivers tend to drive on average at over 180 kmh (112 mph) or more.

Thursday, April 18, 2019

Mapping Urban Heat Islands

While the governments of the world refuse to take action against climate change you might want to start planning how you are going to cope with the extreme heat of your future summers. Particularly if you live in a large town, city or other large built environment.

It has long been known that certain areas of a town or city can be much warmer than other parts of the same town or city. This 'urban heat island' effect is often most pronounced in the summer and on days with extreme heat. If you know where those heat islands are in your town then you may be able to avoid them on the hottest days of the year.

Last August (2018) NOAA ran a citizen science project in Washington D.C. and in Baltimore in order to discover if these cities had urban heat islands, and if so, where those heat islands were. NOAA mounted thermal sensors on the cars of a number of volunteers. They then asked the volunteers to drive a set route while the monitors recorded temperatures, the time of the temperature recording and the location. NOAA then used this data to create detailed maps of the hottest and coldest places in each city.

In Baltimore on the same day and at the same time some areas of the city experienced temperatures 17 degrees F higher than other areas. In Washington D.C. it was discovered that some areas of the city were 16 degrees warmer than other areas. The detailed maps which NOAA were able to create from the project not only provided proof of the urban heat island effect, it showed where those heat islands were and allowed NOAA to look for common features found in the hottest and coolest locations. In other words it allowed NOAA to explore what causes certain areas in a city to experience more extreme heat than other parts of the same city.

You can view the location of Baltimore's and D.C.'s urban heat islands on NOAA's Detailed maps of urban heat island effects in Washington, DC, and Baltimore. Both these two city maps overlay heat maps of the recorded temperatures in each city on top of an aerial imagery map. A swipe control then allows you to closely examine the common features underneath the hottest and coldest areas in each city.

You don't need to be an environmental scientist to see that the hottest areas in both cities are the areas with the densest built environment and the most roads. This is a result of un-shaded roads and buildings absorbing heat and then radiating it out to their surroundings. The coolest places in both cities are parks or other areas with tree cover. The dark surfaces of roads and built materials, such as bricks and concrete, absorb more heat than grass and vegetation. Which is why the densest built areas tend to be significantly warmer than areas with tree cover or parks.

In order to avoid creating areas that experience a heat island effect city planners can introduce measures which mitigate against the albedo effect of roads and buildings. Roofs can be painted white to reflect heat. Trees can be planted along roadways and parking lots can be replaced with parks (or at least made more green).

You can explore the locations of the urban heat islands in your town and city on Yale University's Global Surface UHI Explorer. This interactive map uses our knowledge of what causes the urban heat island effect to predict where urban heat islands will appear in towns and cities around the world.

You can read more about the algorithm which predicts the urban heat islands on the interactive map in the paper A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability, published in the International Journal of Applied Earth Observation and Geoinformation.

Wednesday, April 17, 2019

All the Lands & Kingdoms in the Whole World

Google Arts & Culture is an often overlooked resource for viewing vintage maps online. One example of an historically important map which you can view in detail on the site is Leonardo da Vinci's Plan of Imola. An even earlier map which you can explore on Google Arts & Culture is Hanns Rüst's Mappa Mundi (c1480).

These two maps were created possibly as little as 20 years apart but they belong to two entirely different worlds. Da Vinci's Plan of Imola, created with his own scientific instruments, belongs entirely to the Renaissance. The map is not only amazingly accurate it would not look entirely out of place in a modern atlas of Italy. On the other hand Hanns Rüst's Mappa Mundi is a product still rooted in the Middle Ages. It is a picture of the world which owes little to the new thinking of the Renaissance and nearly everything to a religious understanding of the world.

The Mappa Mundi, usually attributed to the German printer Hanns Rüst, is a much smaller map of the world than the better known Hereford Mappa Mundi. The Hereford Mappa Mundi stands over 5 feet tall and is over 4 feet wide. The Hanns Rüst map in comparison measures less than 11 x 16 inches. Despite this large difference in size both maps share a number of similarities.

Both the Hanns Rüst and Hereford Mappa Mundi are religious maps of the world as much as they are geographical maps of the world. The title of the Hanns Rüst map boasts (in German) "This is the mappa mundi of all the lands and kingdoms which there are in the whole world". However the knowledge which underpins this understanding of the world comes firstly from the Bible and the church. Ezekiel 5:5 says "Thus saith the Lord God; This is Jerusalem: I have set it in the midst of the nations and countries that are round about her." Something that the creator of the map has taken quite literally by placing Jerusalem at the very center of this map and therefore at the center of the known world.

Jerusalem appears above European countries on the map. The Mappa Mundi is orientated so that east is at the top of the map. Paradise is also at the top of the map. In Genesis the Garden of Eden is said to be the source of four rivers. This religious knowledge is used to frame the cartographer's understanding of the world. On the map four rivers are shown flowing from the Garden. Most sources claim that these rivers are labelled as the Ganges, Phison, Indus and Nile (however to me it appears that one river is labelled the Euphrates (Eufrates) and another the Tigris).

To the left of these four rivers is a mountain range with a single head appearing from behind one peak. The mountain is labelled, "Caspian Mountains gog and magog". The tribes of Gog and Magog are the descendants of Noah's son Japheth. For Christians Gog and Magog often seem to represent the uncivilized tribes of the world. The Book of Revelation says that in the Last Days Satan will rally "the nations in the four corners of the Earth, Gog and Magog, to a final battle with Christ and his saints".

Back in the center of the map, around Jerusalem are a number of locations which are probably included on the map as much for their religious significance as for their geographical importance. So, for example, we have Bethlehem, the Mount of Olives and Galilee.

Moving out from Jerusalem the map is divided into three continents. These are labelled in red on the map. Asia is shown above Jerusalem (remember the map is orientated with East at the top). Below Jerusalem Europe is shown in the bottom-left corner of the map and Africa is shown in the bottom-right corner. The three continents are also named for Noah's three sons Ham, Shem, and Japeth (see the red outlined labels on the map). At the time it was traditional to identify the three known continents as populated by the descendants of Ham, Shem, and Japeth, as can be seen on early T and O maps.

The Mappa Mundi itself contains a form of T and O map. In an inset at the bottom-right we have a T and O map which depicts the division of the world into town, country and sea. To the left of this is another inset which shows the world as consisting of the four elements, wind, fire, earth and water.

Back on the main map you can see that the world is encircled by an ocean which contains a number of islands (including in the bottom-left 'engenland'). To the right of engenland are the Columns of Hercules. The Columns of Hercules were believed in ancient times to be located at the Strait of Gibraltar and they marked the western extreme of the known inhabited world. As you move around the outer ocean the islands contain a number of weird and wonderful people and monsters. Outside of the encircling ocean four classical windheads are located at the four cardinal directions of the map.

If you were lost in Imola you could without a doubt use Leonardo da Vinci's map to find your way home. If you tried to navigate your way around the world using Hanns Rüst's Mappa Mundi you would soon be lost - unless you are a Christian and you need to find yourself philosophically within the myths and traditions of the Old Testament. In fact the only thing that the two maps have in common is the importance given to the different winds.

Tuesday, April 16, 2019

Plastic Polluted River & Oceans

Around 8 million tonnes of plastic is dumped into the world's oceans every single year. Plastic which is dangerous to marine life and, once it enters the food chain, dangerous to the health of people as well. This plastic comes from homes across the world. Mismanaged plastic waste and waste which is intentionally dumped enters the world's rivers and then flows into our seas. Studies are now beginning to map where this plastic ends up and where it originates from.

Litterbase is one organization attempting to collate the results of scientific studies researching the levels of plastic pollution found in the world's oceans. Currently Litterbase provides a summarized overview of the results from over 1,900 studies into the amount and composition of litter and its effect on marine environments. An example of one of these summaries is Distribution of Litter Types in Different Realms, which is an interactive map created from the results of 916 scientific publications on the amount, distribution and composition of litter in the world's oceans.

The map shows the results of hundreds of scientific studies carried out in specific locations around the world. It is not a heatmap of marine pollution around the world. It only shows the levels of pollution in the areas where studies were carried out. The markers on the map do show the levels of plastic and other types of pollution detected at different locations across the globe. However there are gaps in seas and oceans where little scientific research has taken place, for example around Africa and the Polar regions.

One way that we can fill in these gaps in our knowledge is by modeling the density of pollution in the oceans based on the results of scientific studies. Sailing Seas of Plastic is a dot density map which shows the estimated concentration of floating plastic in the oceans based on the results of 24 survey expeditions (2007-2013) and on wind and ocean drift models.

Each dot on the Sailing Seas of Plastic map represents 20 kg of floating plastic. According to the map there are 5,250 billion pieces of plastic adrift on the seas of the world. If you want you can also overlay the sailing tracks of the 24 survey expeditions on top of the dot map.

The Seas of Plastic is another visualization of the floating plastic debris that is polluting the world's oceans which is based on ocean drift models. The visualization includes an interactive globe showing the five large circulating gyres of plastic in the North Pacific, North Atlantic, Indian, South Atlantic and North Atlantic oceans.

The data for the Seas of Plastic visualization comes from a Lagrangian particle tracking model which simulated 30 years of input, transport and accumulation of floating plastic debris around the world. The model tracks the paths of plastic particles from land to sea and estimates the relative size of each of the five circulatory gyres.

The visualization also includes a Sankey Diagram that shows the amount of plastic debris which different countries contribute to each of these five circulating gyres. This diagram reveals that China is by far the biggest polluter of the world's oceans, followed closely by Europe. The notion that China is the source for a large proportion of ocean pollution is supported by the Ocean Cleanup campaign.

The Ocean Cleanup organisation believes that between 1.15 and 2.41 million metric tons of the plastic in the oceans originates from the world's river systems. Two thirds of it from the rivers of Asia. To help explain how and where plastic ends up in the world's oceans the Ocean Cleanup has released an interactive map, River Plastic Emissions to the World’s Oceans.

This map shows river systems around the globe. The predicted input from each river system is shown at the coast using scaled circular markers. These predicted inputs are based on a model which looks at population density, waste management, topography, hydrography, the locations of dams and the reported concentration of plastic in rivers around the world.

You can learn more about how the plastic you place in your trash ends up in the world's oceans in a National Geographic story map. In What Happens to the Plastic we Throw Out National Geographic explains how domestic plastic trash ends up polluting a remote island in the middle of the South Pacific. As you progress through National Geographic's story a background map of the South Pacific shows the levels of mismanaged municipal plastic waste produced by countries on the Pacific Ocean. Much of this plastic waste eventually ends up in the Pacific. Carry on scrolling and the map updates to show the levels of plastic waste entering the ocean from rivers in Asia and North, Central & South America.

National Geographic identifies the Yangtze River as the most polluted river in the world. Most of the pollution from the Yangtze eventually ends up in the Pacific by way of the East China Sea. The background map animates the modeled pathways of marine debris to show how the plastic from the world's rivers ends up creating the huge plastic gyres which are polluting our oceans.

Monday, April 15, 2019

Tilt Shift Maps

Tiltshift is an interesting experiment in creating an interactive map with a tilt-shift effect. Tilt-shift is a technique which is often used in photography to make subjects appear miniaturized. You can view some example photographs with a tilt shift effect applied, and even add a tilt-shift effect to your own photographs, at Tilt Shift Maker.

Many moons ago I created something similar for adding a tilt-shift effect to Google Maps Street View images. Unfortunately because of Google's new extortion racket API charges that little application no longer works. However, thanks to the post that I wrote for Maps Mania on this technique, I do still have a screenshot.

I also have the information which I posted about how I created this effect with Street View. To create the effect you need to overlay
"... one Street View layer on top of another Street View of the same scene. You can then use CSS filter transparency to create a masking effect. If you add this transparency filter to a horizontal band in the top Street View you can see the bottom Street View through this band. Then all you have to do is add a CSS blur filter to the top Street View. The effect is a Street View that is blurred (apart from one horizontal strip) creating a reasonable tilt shift effect".

If you look at the html code for the Tiltshift map you can see that something similar has been done to apply the tilt-shift effect to this interactive map. One Mapbox map layer has been overlaid on top of another map layer. The top map has a transparency style applied to it so that you can partly see the map beneath through it. A linear gradient has also been applied to the top map so that the transparency is greatest along the middle of the map. A blur effect has also been applied to this top map. The result is that the top and bottom of the map is slightly blurred while the middle band isn't blurred.

I think the effect is improved on the Tiltshift map if you offset the pitch of the map a little. You can do this by pressing down on your mouse's right button and dragging up or down.

Why Are We Dying Younger?

Life expectancy in the UK and the USA is falling, bucking a century long trend of people living longer lives. The CDC says that between 2016 and 2017, the average life expectancy in the US fell from 78.7 to 78.6 years. In the UK this year the Institute and Faculty of Actuaries (which calculates life expectancy for the UK pension industry) cut the average life expectancy of people in the UK by a whole six months. That follows the two month cut in life expectancy it announced last year.

So why is life expectancy suddenly falling for the first time in over a hundred years?

Creating choropleth maps of average life expectancy can show us where people are living shorter lives on average. For example, the UK's Office for National Statistics has released information on the Health State Life Expectancies 2014 to 2016, which examines life expectancy in each area of the UK. This ONS report includes two interactive maps; one visualizing life expectancy in each local area in the UK and the other showing the gain in life expectancy in each local area since 2001-2003.

The map reveals that the best places to live if you want a long healthy life in the UK is Richmond upon Thames if you are male (69.9 years) and the Orkney Islands if you are female (73.0 years). The worst places to live are Dundee City for males (54.3 years) and Manchester for females (54.6 years). So why are men in Richmond upon Thames expected to live longer than men in Dundee and why are females in the Orkney Isles expected to live longer than women in Manchester?

The answer isn't just geography or climate. Time and again maps of life expectancy show us that life expectancy can vary by huge amounts even between neighborhoods which are very close together geographically. Quartz's Life Expectancy Map reveals the average life expectancy in nearly every US neighborhood. According to the map people who live in New York's Chinatown have a life expectancy of 93.6 years, while people who live in nearby Roosevelt Island have a life expectancy of just 59 years. Obviously it is not the climate in Chinatown that is helping its residents live longer than people on Roosevelt Island.

You can view the life expectancy at birth for each county in the USA on the University of Washington's US Health Map. A choropleth layer shows the life expectancy for every county in the country. FiveThirtyEight has used the same data (life expectancy at birth 1980-2014) to compare the life expectancy in each state to the U.S. average. Life Expectancy in Each State vs. U.S. Average reveals which U.S. states have higher or lower life expectancy than the country average. It also shows which states are improving or falling behind over this time period compared to the country average.

Again these maps show that there can be huge differences in life expectancy even between neighboring counties. To find out why people in some areas are now expected to live significantly shorter lives than people living in other areas we need to look at the underlying demographics of these areas. We need to explore some of the economic and demographic data which might begin to explain why some people are living longer than other people who live close by.

In the UK the University of East Anglia argues that health inequality is a major cause for the higher rates of premature mortality in some areas of the UK. The UEA's Year's Lost interactive map shows how many years of life are lost in each local authority area in the UK. The UEA's research shows that the rates of premature mortality are twice as high in the most deprived areas of England when compared to the rates in the most affluent areas.  In other words wealth (or lack of wealth) is a major factor in how long you can expect to live.

NBC News' map of How Long People in Your City Are Expected to Live reveals some of the underlying demographic and economic data which might begin to explain some of the huge differences in life expectancy between close neighbors. NBC's map colors each census tract by the average life expectancy. However if you click on a census tract on the map you can also view information on the median annual income, the percentage of the population without health insurance, the percentage without a high school diploma, the percentage of the population unemployed and the racial composition of the local population.

This extra economic and demographic data allows you to explore for yourself some of the possible causes of lower life expectancy. In fact NBC News have themselves provided a number of examples where census tracts in close proximity have vast differences in life expectancy. In Los Angeles they show how one tract in Palo Verdes has a life expectancy of 86.4 years, while a nearby tract in Long Beach has a life expectancy of 73.3 years. In the Palo Verdes census tract the average median income is $175.536, while in the Long Beach neighborhood the average income is $54,167.

Time and again the map reveals that areas with a high life expectancy are likely to have a high average income and areas with a lower life expectancy usually have a lower average income. In fact Associated Press' analysis of the National Center for Health Statistics' data on life expectancy found that on average life expectancy increased by six months with a $10,000 increase in median income. They also found a strong correlation between life expectancy and race. On average life expectancy decreased by eight months with a 10% increase in the black population.

What appears to be clear is that in both the USA and the UK average life expectancy is beginning to fall. None of these maps provide evidence that this falling life expectancy is a result of the middle-class squeeze in the USA and the UK. However it is apparent that those areas in both countries with the lowest life expectancy are on average the most deprived areas. In other words there is a high level of health inequality in both the USA and the UK.

Saturday, April 13, 2019

Leonardo da Vinci's Map of Imola

In the early Sixteenth Century Niccolò Machiavelli and Leonardo da Vinci accompanied Cesare Borgia on his rampages through the Romagna region of northern Italy. Borgia, acting in the name of his father, Pope Alexander VI, was busy capturing city after city in order to create his own northern state in Italy. Machiavelli had been sent by Florence to accompany Borgia as an emissary-cum-spy. Leonardo was there because he had been employed by Borgia as a military architect and engineer.

While working in the role of military engineer Leonardo da Vinci created the 1502 Map of Imola. Borgia had captured the city of Imola at the end of the 15th century, which he then used as a base to attack Bologna, the main city in the region. Leonardo da Vinci was given the task of mapping Imola, possibly as a plan to strengthen the city's defenses. The map that he created is now renowned as one of the first scientifically accurate maps ever created.

If you want to explore the map in detail you can view an interactive zoomable version of Leonardo's Plan of Imola at the Royal Collection Trust. You can also view an interactive version of the map at Google Arts & Culture.

Leonardo's Plan of Imola is reported to be the oldest extant ichnographic or zenith map. An icnographic map shows every location depicted as it would be seen when looking directly down from above. The accuracy of the Leonardo map is a testament to da Vinci's scientific knowledge and probably to his own inventions of accurate scientific measuring tools, including the odometer and magnetic compass.

While surveying Imola Leonardo da Vinci may well have walked the city with his own design of odometer (measuring wheel). You can view one of da Vinci's odometer designs on Google Arts and Culture. This odometer consists of a wheel, used to measure the distance on the ground, and a tray of balls. The odometer holds a tray with a number of sections, each of which contains a ball or a stone. This tray is pushed along as the odometer moves. When each box passes over a hole the ball or stone it holds falls into a drawer. The number of balls dropped into the drawer gives the distance traveled.

As well as accurately measuring the lengths of roads, walls and buildings Leornardo da Vinci would also have had to measure the angles of the streets, building walls and city fortresses. To do this he could use his own design of magnetic compass. Henry Gillete in 'Leonardo da Vinci, Pathfinder of Science' describes this compass as "a board with an arc on it and a compass needle, and was probably the first magnetic needle on a horizontal axis."

The map itself contains a form of compass rose which frames the whole map. We are accustomed to seeing a compass rose placed in one of the corners of a map. The Plan of Imola doesn't have a small compass showing the cardinal directions. Instead the circular plan is marked itself with eight lines emanating from the center of the circle. The lines marking the cardinal directions are marked in da Vinci's own hand at the outer rim with the names of the main winds.

While in the city Machiavelli and da Vinci cooked up a plan (ultimately unsuccessful) to support the ambitions of Florence by stopping the Arno river from reaching Pisa, thus depriving the city of water. After leaving Imola, Machiavelli went on to write The Prince, of which the eponymous character appears, at least partly to be based on Cesare Borgia. Borgia himself wasn't able to enjoy his conquest of northern Italy for very long. After the death of his father, the Pope, in 1503, his fortunes quickly turned and he was killed in 1507.

Leonardo da Vinci went on to accomplish much more before his death in 1519. Not least among these achievements was his painting of the Mona Lisa. The mountains in the background of this painting are the Apennine mountains which da Vinci rode through while working as military engineer for Cesare Borgia.

If you want to see how revolutionary Leonardo da Vinci's map was then you can compare it with Hanns Rüst's Mappa Mundi. This map of the world was created around 22 years before the Plan of Imola. Despite being created within a short period of time the maps belong to completely separate eras. Hanns Rüst's map, inspired by a religious understanding of the world, is a product of the Middle-Ages while Leonrando da Vinci's Plan of Imola, created with scientific instruments, heralds the new Renaissance.

Friday, April 12, 2019

A Street View of Thrones

A Viz of Thrones is a map and data visualization about the characters and episodes of the HBO series Game of Thrones. The map allows you to view the setting in Westros or Essos where every single scene in all seven screened seasons of A Game of Thrones takes place.

At the heart of a Viz of Thrones is a mapped timeline which allows you to drill down into any scene, in any episode of all seven screened seasons of the television series. If you select an episode from the season timeline then the map sidebar will show all the individual scenes in that episode. Select any scene in the chosen episode and character markers will be added to the interactive map. The map therefore shows you both the location of the chosen scene and the characters who appear in it. If you click on a character's marker on the map you can even find out the length of their screen time in the chosen scene.

If you enjoy exploring these fictional locations on the interactive map then you might like to explore the real locations where these scenes were shot using Google Maps Street View. In Game of Thrones: The Old Views and the NewGoogle has curated a collection of Game of Thrones shooting locations which can be explored using Google Maps panoramic imagery. The collection includes the real shooting locations for King's Landing and Winterfell. Google's collection of magical Street Views is split into three main Houses, the Starks, Lannisters and the Mother of Dragons.

If you are impressed by A Viz of Thrones then why not create your own interactive Game of Thrones map. Thanks to Carto you don't even need to create your own basemap of Essos and Westeros. Carto's Game of Thrones Basemap of the Seven Kingdoms provides a great canvas on which you can add all of your Games of Thrones geo-data.

If you want to know how to add all your GoT geo-data to the Carto basemap then you need Patrick Triest's tutorial Building Aa Interactive Game Of Thrones Map (Part I) - Node.js, PostGIS, and Redis. The first installment of this two part tutorial explains how you can build a searchable backend to serve your data to the map. Part II of the tutorial shows you how to build a responsive interactive map for this data using Leaflet.js.

If you were wondering what a Node.js, PostGIS, and Redis powered Game of Thrones map looks like then you can find out on Patrick's Atlas of Thrones. The map includes lots of categorized points of information that you can view on the map. Including castles, towns, regions and landmarks. You can navigate to these via the categorized menu or by using the built-in search function.

Thursday, April 11, 2019

Is Your Neighborhood Improving or Declining?

Around 36.5 Americans live in areas which have seen economic decline and larger numbers of residents living on low incomes. According to a new report from the University of Minnesota most metropolitan areas in America have witnessed economic decline since 2000. While a limited number of cities such as Washington D.C. and Los Angeles have experienced gentrification most metropolitan areas in America have seen an increase in the percentage of the low income population.

If you want to know if your neighborhood is declining or becoming more gentrified then you can refer to the University's new interactive map which visualizes which census tract areas are becoming poorer and which are becoming richer overall. The map shows the level of low income displacement or low-income contraction in each census tract in the USA. In other words it reveals which neighborhoods have seen a decrease in the number of people on low incomes (2000-2016) and which areas have seen an increase in the low income population. Low income is defined as "those below 200 percent of the federal poverty line".

The Low Income Displacement and Concentration in U.S. Census Tracts, 2000 to 2016 interactive map colors census tract areas by the change in the number of the low income population. If you click on a census tract you can view details on the percentage of the population who were low income in 2000 and the percentage in 2016. You can also view other details about the local population, such as the changes in the number of local residents with a 'Middle-High Income', the number on a 'Low Income', the number 'Below Poverty' and the number living in 'Extreme Poverty'. The demographic information provided also shows changes to the numbers of Asian, black, Hispanic & white residents, the local number of college graduates, and changes to the number of rental, vacant & owner properties.

The full report, American Neighborhood Change in the 21st Century: Gentrification and Decline outlines some of the main patterns revealed by the map. For example, the data reveals that nonwhite Americans are far more likely to live in economically declining areas than white Americans. The data also shows that white flight is strongly linked with neighborhood change. In economically expanding areas between 2000-2016 the white population grew by 44%. In economically declining areas the white population fell by 22% over the same period of time.