The 100 Most Boring People in America

Who is the most unnotable person in your town?

I asked ChatGPT to find me the most unnotable people in America's biggest towns and cities. The results can be seen on my Unnotable People map. On this map the placename labels of the 100 largest towns and cities in the USA have been replaced with each town's most insignificant person.

Using ChatGPT to find each town's most unnotable person means that the map may contain errors. Because the people who are featured on this map have achieved very little in life it was difficult to confirm their existence. It was even more difficult to find out what each of these people had achieved in life (mostly very little).

If you want to know your town's most notable person then you should also check out Topi Tjukanov's Notable People map, or The Pudding's A People Map of the USA.

Going Postal

The Topic & Sentiment Map of #royalmail Tweets interactive map analyzes Twitter messages to show where people in the UK are happy or disappointed in their postal services. By carrying out a sentiment analysis of Tweets containing the hashtag '#royalmail' the map is able to show where people are posting positive and negative messages about their mail delivery. 

Emoji markers are used on the map to show the type of sentiment expressed towards the Royal Mail in Twitter messages. You can click on the individual markers to read the Tweet yourself. Negative messages towards the postal service outweigh the positive messages on the map. This may well be a common problem with this type of sentiment analysis - as I think it is more likely that people will take to Twitter to vent their dissatisfaction with the postal service than they are to write a favorable Tweet about their local postie. 

However that doesn't mean that this type of sentiment analysis mapping of social media messages can't be extremely useful. It would appear to have great potential for identifying in near real-time emerging problems in a network, or at the very least identify locations where there is a large level of customer dissatisfaction. 

If you are interested in how machine learning was used to determine the sentiment of #royalmail Tweets then you should read An AI-Powered Analysis of our Postal Service Through Tweets, which does a great job of explaining how a machine learning model was trained to analyze the sentiments expressed in Twitter messages towards the Royal Mail. It also includes a link to the GitHub repo of the project.

You can read about previous examples of setiment mapping based on Twitter messages in the post Sentiment Mapping. This post links to three previous mapping projects which attempted to map sentiments expressed in Twitter messages (unfortunately all three projects are now dead links).

Can AI Models Make Maps?

I asked Google's artificial intelligence large language model (LLM) chatbot Bard: 

How can Artificial Intelligence be used to create interactive maps for the internet?

Bard replied that Artificial Intelligence (AI) can be used to:

• Automatically generate maps from satellite imagery. AI can be used to identify and classify objects in satellite imagery, such as buildings, roads, and trees. This information can then be used to automatically generate maps of a given area. 
• Predict the location of points of interest. AI can be used to predict the location of points of interest, such as restaurants, gas stations, and tourist attractions. This information can then be used to create interactive maps that show the location of these points of interest. 
• Detect and track changes in the environment. AI can be used to detect and track changes in the environment, such as deforestation, flooding, and erosion. This information can then be used to create interactive maps that show the changes that are taking place. 
• Provide real-time traffic updates. AI can be used to provide real-time traffic updates, such as the location of accidents and the speed of traffic. This information can then be used to create interactive maps that show the current traffic conditions. 
• Personalize maps for individual users. AI can be used to personalize maps for individual users by taking into account their interests and preferences. For example, AI can be used to show users only the points of interest that are relevant to them. 

Bard's response to my query provides an interesting overview of how image identification models are being trained to search for patterns in satellite imagery and to detect changes in satellite imagery over time. However I actually wanted to know how I might use ChatGPT or Bard to save me hours of work by making interactive maps for me (which shows how important it is to carefuuly phrase your interactions with LLMs).

Over the last few weeks I've been reading with interest on Twitter how developers have been using AI models (mainly ChatGPT) to create Overpass Turbo queries, to display requested OSM features on an interactive map, and how to make ChatGPT Generated Web Maps. My own personal attempts to get Google's Bard to create Overpass Turbo queries or valid GeoJSON have failed miserably. I think that at least fot the moment it that ChatGPT is a much better GIS practitioner than Bard.

If you don;t have access to ChatGPT or Bard don't worry. There are already two neat services which use AI models to help you easily create maps simply by typing a request or question:

Texttomap

Enter a question or some text into Texttomap and it will attempt to answer your question and show you the results on a map. For example the map at the top of this page was created by asking Texttomap "Which are the four longest rivers in the United States?" Other suggested prompts (by Texttomap) are "Which countries took part in WW2?" or "What should I see when visiting Paris?"

Once you have created a map with Textomap you can share a link (such as this map of 'What should I see in San Francisco?'). With a paid pro account you can also embed any created maps in your own website or blog.

mapsgpt

mapsgpt can also be used to create interactive web maps simply by entering a few text inputs. For example here is a map built from the input Show me restaurants near Spitalfield, London. mapsgpt will send you a link to any created map if you tell it your e-mail address.

The Mass Shooting Database

I tend not to review many crime maps on Maps Mania. Mainly because I have serious concerns about how accurate they are and the possibility that some crime maps paint a distorted picture of the real levels and locations of crimes.

There are many reasons why a crime map, purportedly showing the locations of recorded crimes in a city, may not be a true reflection of the true crime levels in that city. For example not all crimes are reported to the police. This can be for a variety of reasons, such as victims not feeling comfortable reporting the crime, not believing that the police will actually respond to a report of the crime, or believing that police will be unable to solve the crime. 

Even when crimes are reported, they may not be recorded accurately. For example, on this San Francisco bike theft map a major hot-spot of crime appears to be a local police station (possibly because the address of the police station was used when no theft site could be identified).

These concerns don't really apply to maps of mass killings. This is because mass killings are unlikely to go unreported to the police and their impact tends to ensure that they are largely recorded to the correct crime location. The impact and importance of mass killings also means that the data is likely to be more closely scrutinised and audited. For example the Mass Killing Database, a collaboration between Northeastern University and the Associated Press, tracks all multiple homicides in the United States from 2006 with four or more victims.

Mapping the mass killings in the Mass Killing Database reveals that multiple homicides occur in all kinds of towns and cities in the United States. However, according to USA Today, "Homicides with fewer than four victims are more common in larger cities, but mass killings with higher death tolls often take place in smaller towns or rural settings."

The Gun Violence Archive also collects data on gun-related violence in the USA (the Mass Killing Database isn't limited to mass killings involving only guns). The Gun Violence Archive reports that there have already been 130 mass shootings in the USA so far this year. To date 61 children have been killed by guns in 2023 and 132 children have been injured by guns.

Mother Jones has also been collecting data on mass shootings in the USA since 1982. According to their 'Guide to Mass Shootings' in over three quarters of mass shooting incidents in the USA the guns involved were acquired legally. 

The Mass Killing Database and the Mother Jones Guide to Mass Shootings use different criteria in defining what constitutes a mass killing. The Mass Killing Database isn't limited to gun crimes and Mother Jones only records indiscriminate killings in public places. There are therefore major differences in the number of mass killings between the two different databases. Despite these differences one thing is clear in both databases - the perpetrators of mass killings and mass shootings are overwhelmingly male.

The Real-Time Wind Power Map

Right now around a third of the UK's current electricity is being generated by offshore wind power. In fact more than 20% of global offshore wind capacity is generated in the UK.

You can explore for yourself the current operating status of UK wind farms on the UK Renewables Map. This map shows the live generating output of all the major wind farms in Great Britain, revealing the amount of power currently being generated by each farm and where that generation is happening.

The current MW output of each wind farm is represented on the map by scaled yellow circles. If you click on these circles you view the selected farm's name and graphs showing the historical generating output of the farm. The UK Renewables Map also includes a real-time animated wind layer which shows the current wind conditions around the UK.

The UK Renewables Map is a personal project of Robin Hawkes. The wind data for the map is from Elexon BSC and the animated wind layer is from WeatherLayers.

You can also view the estimated amount of electricity being generated by offshore wind sites across the UK on the Crown Estate's Offshore Wind Electricity Map. On this map each wind farm is represented by a scaled animated wind turbine marker. The size of each marker represents the scale of its current output. If you select one of these markers you can view the name of the wind farm and its current output in megawatts.

The map sidebar shows a dashboard reading of the share of the UK's electricity currently being generated by offshore wind. If you select a marker on the map the dashboard updates to show the name of the selected wind farm and its current output in MW.

The Electric Vehicle Driving Test

Electric vehicles are becoming increasingly popular, as people look for ways to reduce their environmental impact. But what are the pros and cons of electric vehicles compared to gas cars? And are all electric vehicles created equal?

The San Franicisco Chronicle has created an interesting route planning tool which can help drivers compare the likely travel time and cost of taking any car journey in the USA. If you click on your starting point and destination on the interactive map in How Teslas and other major EVs perform you can discover how long the journey woild take in both a gas powered car and an electric vehicle. You can also find out how much the journey would cost in terms of gas or electric charging costs. 

The Chronicle talks about the map being used to plan California road trips but the map actually works for any road trip in the USA or Canada. If you do use the map outside of California just be aware that the gas and electricity costs of a journey are calculated based on the average gas & electric charging prices in California on March 1 2023.

The Chronicle map can be used to compare the performance of four different models of electric vehicles (the Tesla Model Y, the Polestar 2, the Ford F-150 Lightning truck and the Chevy Bolt). Select one of these electric vehicles (using the buttons at the top of the map) and you can discover the estimated journey time for that vehicle, the number of potential charge stops and the estimated cost of the journey.

Toponym Homonyms

In January a New Yorker called Kingsley Burnett landed in Sidney, Montana. As Kingsley emerged from the plane he was upset to discover that the temperature was 22°F and not in the 80s. You see when Kingley Burnett booked his plane ticket he thought he was embarking on a dream holiday to Sydney, Australia, not to the sleepy Montanan city.

Kingley is not the first person to book a holiday to the wrong Sydney. Back in 2017 a Dutch student, Milan Schipper, arrived in Sydney, Canada. Unfortunately when Milan bought his plane ticket he also thought he was heading to a sunny vacation in Sydney, Australia.

It is very easy to book travel tickets to the wrong location. Especially when so many locations share the exact same name. For example in the United States there are 34 different towns and cities called Springfield, 31 cities called Franklin and 29 called Clinton. 

If a friend asks you to meet them in Springfield on Sunday which Springfield will you travel to? Obviously you should ask them which Springfield they are talking about. However if for some reason you can't confirm the correct Springfield then you could try using The Pudding's Most Likely Town map instead.

The Pudding's Map of Places in the US with the Same Name calculates "what place someone is most likely referring to, depending on where they are". For example if you enter 'Sprinfield' into the map it shows you the location of all the towns called Springfield in the states and colors the map to show you which Springfield would be most likely the town being referred to depending on your location in the United States. 

This is determined by giving a score for each town "based on a combination of proximity, population, and Wikipedia article length, then normalized by share". For example if you are in Bent County, Colorado then the Springfield being discussed is most likely to be Springfield, Colorado.

You can enter any town name into The Pudding's map. For example if you enter 'New York' into the map you will discover that there is a New York in Texas. Therefore if you live in Anderson County in Texas and someone says lets go to 'New York' they are probably referring to the 'Little Apple' (at least that's what I hope the citizens of New York, Texas call their tiny town).

The Pudding's map also seems to cope well with international city homonyms. For example if you enter 'Paris' or 'London' into the map it reveals in most counties in the United States 'Paris' or 'London' usually refers to the French and UK capitals respectively. Only if you live in a county near the handful of US places called Paris or London are you likely being referred to a US location.

Is Your Town a Parking Lot?

The Parking Reform Network has ranked U.S. towns based on how much land is dedicated to parking cars. Their Parking Lot Map allows you to see at a glance the percentage of the land surface area of a town or city that is taken up by parking lots and discover each city's PRN Parking Score.

PRN analyzed the amount of land taken up by parking lots in over 50 cities. It then gave each city a score based on how it compares to other cities of a similar size. A city that has a high Parking Score has a high percentage of city land devoted to parking cars. A low Parking Score means that less land is taken up by parked cars.

Arlington, Texas tops the rankings with a Parking Score of 100. 42% of the central area of Arlington is taken up by parking lots. At the other end of the scale is San Francisco, with a Parking Score of 4. Only 4% of central San Francisco is dedicated to parking automobiles. 

If you don't live in the U.S., or your map isn't one of the 50 cities analyzed by PRN, you can see how much of your own neighborhood is dedicated to parking cars using this Overpass Turbo query. Just center the map on the location you wish to analyze and press the 'run' button. Overpass Turbo will then highlight in red all the parking lots at that location.

You can also use the Parkulator map to see how much of your town is dedicated to cars. Parkulator is an interactive mapping tool which allows you to discover how much of your town is dedicated to parking lots, golf courses, brownfield sites, solar generators or parks. 

If you draw an area on the Parkulator map you can choose to find out how much of that area's real-estate is claimed by parking lots. Parkulator will also tell you how much housing or how many parks could be built instead on that area of land. In addition the Parkulator map can show you how many railway stations, light-rail stops, subway stops of tram stops are in that same area.

In the United States it is also possible to explore the amount of land dedicated to parking lots on the USA Parking Lots interactive map. USA Parking Lots is an interactive map of the United States which blacks out every single parking lot in the country. 

Because this is America if you zoom in on a major city the map usually displays a lot of black polygons. Using USA Parking Lots you can quickly see which areas of your town or city have a high concentration of parking lots. You can then observe this same area on the Parkulator map to find out how many homes you could build in this area or how much parkland you could have if your city had better public transportation and dedicated bike lanes.

Locals vs Tourists 2023

Erica Fisher's Locals & Tourists interactive map, released in 2013, is rightly considered a classic of digital data visualization. His interactive map identifies areas of cities that are popular with tourists and those areas which only seem to be known to locals. The map was made using the locations of Tweets sent by locals (those who post in one city for one consecutive month) and tourists (whose tweets are centered in another city). 

The Locals & Tourists map of Twitter users was inspired by Erica's own 2010 Locals and Tourist maps posted on Flickr. These maps show where (in a number of different global cities) photographs have been submitted to Flickr by tourists and where they have been submitted by tourists, based on how far the user's profile location was from a photo's location.

Now Logan Williams has released a new interactive map called "Locals" and "Tourists" on iNaturalist. This 2023 riff on Erica's methodology uses data from iNaturalist to map out where nature lovers are posting nature photos as tourists and where iNaturalist observations have been posted by locals. On this interactive map the locations of iNaturalist postings are shown as either blue or orange dots. The blues dots show observations made by locals (defined as <= 90 days of local activity). The orange dots show observations made by tourists (> 90 days of local activity).

Like Erica's original maps Logan's map provides a fascinating insight into the areas of our towns and cities which are frequented by tourists and those areas which seem to remain only visited by locals. In the screenshot at the top of this post you can see how in San Francisco, in the Embarcadero and Fisherman's Wharf iNaturalist receives lots of entries from tourists. On the otherhand the trails around Mt. Tamalpais feature observations mainly submitted by locals.

In London we can see that a lot of tourists have submitted photos to iNaturalist from around Tower Bridge and the Tower of London. However not many tourists travel further east and visit Shadwell Basin or Swedenborg Gardens, where all the observations (bar one) appear to have been made by locals. 

You can read more about Logan's Locals and Tourists map on his blog post iNaturalist observations: "locals" and "tourists". This post explains a little more about the inspiration behind the map, some of the limitations of its methodology and how the data was downloaded from iNaturalist.

Spying on a Chinese Spy Balloon

Thanks to some impressive detective work the New York Times has worked out the provenance of the notorious Chinese spy balloon which enraged many Americans back in February. In Tracking the Chinese Spy Balloon From Space you can follow the path the balloon took on an interactive map, as it journeyed from China, across the Pacific Ocean and across the United States during January and February of this year. 

In order to discover the path taken by the Chinese balloon the NYT used satellite imagery of the balloon discovered by the imagery analysis platform RAIC. RAIC used image recognition software to find pictures of the balloon in millions of individual satellite images taken by Planet. The NYT were then able to create an interactive map which shows the path taken by the balloon over time. According to the NYT the balloon was launched from (or near) Hainan Island on January 15th.

Beneath the NYT's map of the balloon's track from China to the Atlantic is a very interesting explanation of how RAIC were able to discover the balloon's path from an analysis of satellite imagery. It also explains how the balloon's varying altitude was also able to be detected from the same satellite imagery. 

Fans of dirigible espionage can launch their own virtual spy balloon using the Spy Balloon Simulator interactive map. The Spy Balloon Simulator allows you to launch an imaginary spy balloon anywhere in the world and view its possible flight path over a 20 day period. The balloon's simulated flight path is calculated using atmospheric data from ERA5, produced by the Copernicus Climate Change Service. At the bottom of the map are some time controls which allow you to see the simulated balloon's position along its calculated flight path for any hour during the 20 day period.