#30DayMap Challenge2023

I wanted to start this #30DayMapChallenge with something out of this world, so I used this dataset I found of Global UFO sightings. I had to do a bit of cleaning on the data, particularly aggregating some of the descriptions of UFO type into a manageable number to represent on the map. I then pulled the csv into Felt and styled it each point by the UFO type category.  

I think it's really interesting how this phenomenon seems to be largely restricted to the western world. Maybe we let our imaginations run wild a bit more?

Data Source 

To be honest I don’t really work with line data all that much, so I was a bit stumped thinking about what to do here. But I was thinking, what impressive thing is in straight lines? Roman Roads of course!  (NB - I nearly did ‘cancelled rail projects in the north of England’, but that was too depressing). 

I sourced a WFS layer from ArcGIS and pulled this into felt using the URL Upload. I also uploaded a shapefile which enabled me to show a base layer of the extent of the Roman Empire with its regions - I for one did not know that the areas in Italy were numbered and not named. 

It's a bit basic this map, but I quite like the regal colour scheme I went with, and also, THE ROMAN EMPIRE WAS BIG RIGHT!

Data sources: 

Roman Empire shapefile: cartographyvectors.com

Roman Roads: DARMC Scholarly Data Series Citation: McCormick, M. et al. 2013. "Roman Road Network (version 2008)," DARMC Scholarly Data Series, Data Contribution Series #2013-5. DARMC, Center for Geographic Analysis, Harvard University, Cambridge MA 02138.

I didn’t want all of my maps for this #30DayMapChallenge to be silly (many of them will be), and one thing I love about mapping is that it's really great at demonstrating inequalities. For example, I was interested in showing how government spending perpetuates inequality in the UK. I found a dataset that showed regional spending for the last five years and joined this csv to a layer of the UK countries and regions in QGIS. I created a field showing the percentage of total spend each region got and mapped this using a graduated symbology. 

I then used the Add to Felt plug-in in QGIS to bring the map into Felt and styled it appropriately, in a way that highlights the impact London spending has. 

I think if you lived in the North East or Northern Ireland, you might be a little miffed by this, although I have to admit, as someone who lives in the North West, I was quite surprised to see us out near the top. Plus I am sure it comes as no surprise to anyone that one city in the UK gets 15% of government spending, more than any other region. 

I also sourced the population figures for each region, and calculated a field for spend per head for the five years, which felt a bit more accurate, and definitely highlights the inequalities further (and perhaps why it doesn’t feel like the North West is really on top despite what the percentages show).

Data Source:

Country and regional analysis: 2022 - GOV.UK (www.gov.uk)

UK population 2021, by region | Statista

I live near Glossop, which is twinned with a town in Germany called Bad Vilbel, which gave me the inspiration for this map. Why is Bad Vilbel so bad? Well, did you know that ‘Bad’ in German means ‘Bath’ or ‘Spa’? So whilst these places might be ‘bad’, they’re probably actually quite nice. 

So, here is a map of 'bad' places to visit in Germany. 

To get this data I sourced place names and locations from GeoNames and linked these with the Wiki page for each town. I then plotted the locations in Felt, so now you can explore the bad places to visit in Germany! 

I’ve made that sound quite simple, but actually it was a lot of data cleaning, getting all the location data correct and pulling in urls from wiki! Once I’d done that, plotting them in Felt was really easy, I just uploaded the csv through the upload anything button and Felt did all of the plotting for me. 

Data Sources: GeoNames. Wikipedia 

I have loved maps for ages, and some of the very first maps that I remember were ones made at school or home as part of a treasure hunt or pirate themed day. I remember scrunching up the paper, using tea bags to stain it, tearing and burning the edges. 

So for my analog map I created a treasure hunt for my little one, seeking out chocolate coins hidden around the house. 

This definitely was a blast from the past, and hopefully will spark another life long interest in maps. 

Click on the picture above for a video!

Music credit:

The Wellerman by Alexander Nakarada (CreatorChords) | https://creatorchords.com

Music promoted by https://www.free-stock-music.com

Creative Commons / Attribution 4.0 International (CC BY 4.0)

https://creativecommons.org/licenses/by/4.0/ 

Mapping is a powerful way of telling stories, and some of those stories are uncomfortable. I, like many others, have felt very moved with what is happening in the Middle East right now, and so, when it came to creating a map based in Asia for Day 6 of the #30DayMapChallenge I couldn’t really ignore this story. 

I sourced data on damage estimates in Gaza during the recent conflict and mapped these over the locations of key social infrastructure such as schools and hospitals. I also sourced a raster of population density in Gaza. 

I’m really aware that this is a fast moving story, and that some of this data is now out of date. I also couldn’t find data for the regions in the south west, and am not sure if this represents a lack of data, or real world concentration of the conflict on the ground. 

I used the heat map function in Felt to show the density of damage, if you zoom right in, you can see how whole streets or neighbourhoods have been damaged. The population raster also highlights how this damage is occurring in the most densely populated areas. 

Obviously there is a whole lot of context around this conflict, which I am not commenting on, but for me, maps are a way of visualising stories, and this is an important story. 

Data Sources: 

Population: State of Palestine - Population Density - Humanitarian Data Exchange (humdata.org)

Damage: UNOSAT Gaza Governorate Damage Assessment Update - Humanitarian Data Exchange (humdata.org)

UNOSAT Deir Al-Balah Comprehensive Damage Assessment - Gaza Strip - 21 October 2023 - Humanitarian Data Exchange (humdata.org)

Health Facilities: HOTOSM State of Palestine Health Facilities (OpenStreetMap Export) - Humanitarian Data Exchange (humdata.org)

Education Facilities: HOTOSM State of Palestine Education Facilities (OpenStreetMap Export) - Humanitarian Data Exchange (humdata.org) 

When I was thinking about Day 7 of #30DayMapChallenge it struck me that navigation isn't all about using a map. Or to be more specific, it's about being prepared in case you can't use a map.

When I first moved up to the Peak District I was taught how important it is to be able to navigate the hills without a map in hand. At any moment the fog can come in and you loose the ability to use visual clues to ascertain your position.

So for Day 7 I'm not presenting a map 🤔

Instead, here is a route card, produced with reference to a map, that takes you on a small trip in the Peaks. It has all the information you would need, so that with a compass in hand and a watch, but no map, you could find your way.

Bonus points to anyone who can work out the names of the three checkpoints.

Even more bonus points if you can explain why the letters in an OS grid reference are important, in particular with the map I would have used. 

Looking at other people’s #30DayMapChallenge maps I noticed a really cool data repository called Movebank, which is a place where you can explore and download tracking data on species from across the world. I thought this was super cool. I love seeing how far certain species travel, and how they take different migration routes. Africa is often an important node for a range of species, birds like the cuckoo, or Osprey for example migrate from Europe to Africa each year for example. 

So I was looking through the various data available for species in Africa, and came across this amazing tracking data for Egyptian Vultures (which by the looks of it don’t spend much time in Egypt). 

The researchers tracked 16 vultures, aiming to identify key areas for conservation purposes through identifying home ranges for each bird. 

To create this map I downloaded the data as a shapefile and loaded this into Felt. I played around with the colour and also the line size, to really help each line standout. 

What amazing birds! 

Data Source: 

Buechley ER, Şekercioğlu CH. 2019. Data from: Satellite tracking a wide‐ranging endangered vulture species to target conservation actions in the Middle East and East Africa. 

Movebank Data Repository. https://www.doi.org/10.5441/001/1.385gk270

As a trustee of the National Biodiversity Network, it would be remiss of me not to use data from the NBN Atlas at some point of this #30DayMapChallenge. 

My Masters degree was in Biological Recording, it was all about habitat and species data, and the foibles of both. Most notably, the issues that there are in species data bias. People tend to record interesting species, near where they live or near ‘honey-pot’ sites, and there has been an exponential increase in species data produced despite the fact that we are one of the most nature deleted countries in the world. 

To demonstrate this, I have downloaded records from the NBN Atlas since 2010 for a ubiquitous species, that is probably in every city, town or village in the UK. You will see it everywhere! But has it been recorded everywhere? 

You would think that given the NBN Atlas holds over 130 million bird records, that a humble species like the Feral Pigeon would be represented in this data fairly evenly.  Think again! 

So for Day 9 - here is a density map of species records for the Feral Pigeon, using 10km hexagons. 

I created a layer of 10km hexagons in QGIS covering the UK and then used the ‘count points in polygons’ function to create a new layer, showing the number of species records in each hexagon. I then exported that layer to Felt using the ‘Add to Felt’ plug-in, and used a continuous symbology to create a density map.  

References:

Nick J. B. Isaac, Michael J. O. Pocock, Bias and information in biological records, Biological Journal of the Linnean Society, Volume 115, Issue 3, July 2015, Pages 522–531, https://doi.org/10.1111/bij.12532

State of Nature 2023 - National Biodiversity Network (nbn.org.uk)

Data Source:  NBN Atlas - UK’s largest collection of biodiversity information 

These maps are getting increasingly random as I search for interesting datasets.  

Here is a dataset of every dam in North America. I loaded the csv straight into Felt using the upload anything tool, and then used the style by size option to show the dams with the largest water capacity. A little bit of playing around with the best colours, and sizes of the circles, and done. 

What's great about this map is how fast it was to make. Once I found the data source, it really did take me about 5 minutes to load the data into felt and style it. Felt does all the hard work for you. 

What a dam good map!

Data Source: AQUASTAT - FAO's Global Information System on Water and Agriculture

I recreated the map in QGIS, first by georeferencing the map, and then creating line and polygon layers to represent what is shown on the map. I tried my best to replicate the symbology etc used in the original. The basemap is modern day, highlighting the subsequent changes in the city. 

I think this map really shows one of the issues with the operation, in that drop zones were too far from the objective and crucially on the wrong side of the river, as reinforcements from 30 Corps were approaching from the South. This led to large parts of the units to be cut off after the failure of 30 Corps to reach Arnhem. 

Fun fact - the bridge in Arnhem is now named after Lt Col John Frost, who led the only British unit to successfully reach the bridge. 

I found the original map on this website, which has a lot more historical detail. 

Ever been caught short and needed to find the nearest toilet? 

Well now you know what area of the UK you are most likely to be nearer to a loo if you need it. 

For today’s map, I scraped the data from the Toilet Map website and joined this in QGIS to a shapefile of the district authorities in the UK. I used the field calculator to calculate the area in square km for each local authority and then another calculation to work out how many toilets there were every square km. I then pulled this into Felt and gave it the most appropriate symbology I could find! 

Data Source: Toilet Map: Loo Statistics

Open Geography Portal (statistics.gov.uk)

One of the key tenets of rewilding is the return of large predators. The theory being that this creates a trophic cascade whereby whole ecosystems are altered back to more natural states. This argument is based on the current situation where prey animals such as deer have no reason to move on and their populations are not naturally controlled, and so through their grazing, they do enormous damage to ecosystems. When predators return, species such as deer are put under more stress. They have to move around more, and are naturally culled, meaning they do less damage to the ecosystem, resulting in a boom of biodiversity and abundance. This has been seen all of the place, such as the return of wolves to Yellowstone National Park, which actually moved the course of a river! 

Across continental Europe, large predators are returning. Here I map the known populations of Bear, Lynx, Wolverine and Wolf. 

One thing that is noticeable, is the absence of these species from the UK. Obviously as an Island nation, natural recolonisation is not possible, and therefore arguments persist over if, how and when such species should be returned to the UK. 

Data Source: 

Kaczensky, Petra et al. (2021). Distribution of large carnivores in Europe 2012 - 2016: Distribution maps for Brown bear, Eurasian lynx, Grey wolf, and Wolverine [Dataset]. Dryad. https://doi.org/10.5061/dryad.pc866t1p3

https://datadryad.org/stash/dataset/doi:10.5061/dryad.pc866t1p3 

This was a new experience for me, as I've never extracted vector data from OSM before. To be honest I thought this map of BOATs (not that kind of boats) in England and Wales would look better.

BOATs are a bit of a historic anomaly in terms of access rights, I don't think people originally imagined large 4x4s and motorbikes zooming around the countryside, and therefore many BOATs are now 'restricted byways' which essentially makes them a bridleway.

I guess this is the reason why this map didn't look how I imagined it would when I went to extract the data, as I thought it would look like more of a network, but in actual fact, its very disjointed, with lots of small routes that don't really go anywhere. 

I created this map by using the QuickOSM plug-in in QGIS to extract BOATS using the highways/ways/track key and tags as well as the designation/byway_open_to_all_traffic key and tag. I then exported this to Felt using the Add to Felt plug-in and edited the symbology. 

Data Source: © OpenStreetMap contributors 

At this stage of the #30DayMapChallenge the content of todays map is rather apt as to how I feel. Posting a new map every day is actually quite tiring! 😴

So for today, here is a map of sucken wrecks in Oceania.

I struggled a bit with today's theme. Initially I thought about plotting the migration/colonisation of the indigenous cultures, but in the end couldn't really find any definitive information on dates etc. I then thought about mapping volcanoes, which nearly worked, but I was having all sorts of problems manipulating the data, probably because of projection issues.

In the end I found a really cool dataset from The Admiralty, of global ship wrecks, which I was able to plot in Felt for the area.

So it's a bit of a simple map, but at this stage I'm just relieved to have something to post!

Have a look at the layer, you can get a description of the circumstances of each sunken ship, which is pretty cool, as well as info on if its fully submerged/ dangerous etc.  

Data Source: https://datahub.admiralty.co.uk/portal/apps/sites/#/marine-data-portal/items?tags=GlobalWrecks

Open Government License

If you’re a fell runner (not like me - I stick to lovely trails thank you very much), part of the ‘fun’ is finding the best line to sprint/tramp your way over the moorland. It’s all about balancing speed, technicality and the possibility of falling on your face. 

Back in September, several members of my running club completed a fell run as part of our champs called the Tintwistle Three Trigs. They had to get to three trig points around Tintwistle, as quickly as they could. There was lots of debate about the best lines to take across the moors. So what was the best line to take? 

I pulled all of their GPX routes from Strava and merged these. In QGIS, I then carried out a line intersection, creating a point every time one of their routes intersected with another. Pulling this into Felt, I then created a 3m buffer around these points, to smooth them out a little and make them look less ‘point-like’, and then heat mapped these, showing the most effective and well used routes in the race. 

So now, my clubmates can stop arguing (that’s not going to happen).

During COP26 in Glasgow there was a lot of debate around the target of limited global warming to 1.5°c or 2°c. It can often feel a bit difficult to visualise what the difference between the two would be like.

So today, I used data from the Met Office climate change projections, to illustrate the annual average temperatures for both scenarios. It's quite telling that the highest temperature range for the 1.5° scenario, isn't even the bottom range for the 2° scenario. 

Today's challenge was to make a map in only 5 minutes. So I went with something I am really familiar with, mapping out sites designated for nature.

All of the data is available on the data.gov website, on an Open Government License, and they all had an API for a GeoJSON that I could use to pull in the data really quickly, so no need to download anything etc.

For me this map really has demonstrated the power of Felt. I used the upload by URL function for each of the layers, and whilst I waited for them to load (which is pretty quick), I could do things like set the background and title. Then once the layers had loaded, all I had to do was change the colours a little bit and the map was ready to share.

So I went from no map, to fully sharable map in under 5 minutes, which is pretty fantastic I think. 

Even in QGIS if I loaded the data as WFS layers, I'd still have to go to the print composer to download a jpeg to share, or in Arc Online, I'd have to spend time setting the sharing settings etc. Whereas in Felt, I've created and shared an engaging map within 5 minutes.

Data Source: Find open data - data.gov.uk 

I was traveling today, so wasn't able to spend much time on this. However for tomorrow I've been playing around with 3D viewer in QGIS, so for todays map, here is a completely random, utterly useless flat map!  

Data Source: © OpenStreetMap contributors 

NDVI or Normalised Difference Vegetation Index, measures the greenness of vegetation. Again, this can be really helpful for farmers to detect issues in their crops. However, once again, this is where there is a difference between intensive farming and conservation, where many of these fields have been 'improved' and are therefore the bright green (ecological barren) fields we see around the countryside. Conservation NGO's would probably like to see a reduction in 'greenness' which would indicate more floristic diversity. 

For me, it's quite interesting that the wetter bits, are probably also the more floristically diverse bits, and you can see that in both rasters. 

Contains modified Copernicus Sentinel data 2023 processed by Sentinel Hub 

One of the great things about GIS, is that if you don't know how to do something, there's plenty of YouTube videos that you can follow along with. 

That was the case today, having never done a 3D map, I have watched a couple of YouTube videos and replicated what they did. This is a great way of learning new skills. 

I have used the 3D viewer in QGIS to create a flyby of Edinburgh, where I am today, having travelled up for the National Biodiversity Network conference. 

I'm not 100% happy with the map, it would be better if the buildings were relative to their actual size and if the surrounding hills were accurate as well. But, its a good start for a first 3D map!  

Data Source: © OpenStreetMap contributors 

As I spoke about on day 9, species data is not always black and white. 

I’ve gone back to the National Biodiversity Network Atlas for today’s map, to demonstrate how bias and noise in species data can mask even the most extreme cases. In this case, the Willow Tit, the fastest declining bird in the UK. 

The irony of being the fastest declining bird in the UK, is that it’s probably also one of the most recorded relative to its population size, with concerted efforts to monitor populations, and therefore significant amounts of data. 

I downloaded the species records from the 1800’s from the NBN Atlas, and in excel calculated the year and decade the species was recorded in. In QGIS I then created a layer from these records, and split it into several individual layers based on the date range I wanted. I then exported these to Felt, styled the map and exported images. For a bit of fun, I then created a video out of the images showing the transition in time. 

Click on the picture above for a video - 📣sound on

Data Source:  NBN Atlas. 

Sound file: Simon Elliott, XC794939. Accessible at www.xeno-canto.org/794939. 

I have used a Digital Elevation Model I found on ESRI's Living Atlas, and a contour layer from the British Antarctic Survey to create this detailed topography map of Antarctica. It's pretty amazing that for such a remote place, you can get such great data, although projections were an issue!! 

Data Sources:  

DEM - Esri, PGC, UMN, NSF, NGA, DigitalGlobe 

Contours - SCAR Antarctic Digital Database, 2022, https://doi.org/jsbv  

For todays map I was exploring what layers are available as standard on Felt. These are easily added to a map, and add really useful insight and design features straight away, such as topography, buildings, cities, summits etc.

I have used the Graticules layer, showing the lat-long lines across the globe, and a Bathymetry layer showing the depth of the oceans. I think the output is pretty cool, especially as it involved a minimal amount of work on my part thanks to Felt! 

Today's map used education data from the ONS Census 2021 for England & Wales. It shows the percentage of residents in an area with a degree and above. Quite interesting to see the north south divide here, although it doesn't necessarily show where someone came from, but more where graduates are more likely to live (and therefore where graduate level jobs are more likely to be).

In QGIS I created a centroid for each local authority area and joined that with the ONS education data. I then pulled the layer into Felt and used the style by size function to create the dots.

If you're interested in exploring the data, which has a bunch of other info for different education levels check out the interactive map by clicking on the picture above. 

Data Source: ONS Census 2021

I love a good dashboard, and had been dying to try out the map functionality in Looker Studio for ages, so this was just the chance. I found a random dataset showing the UK Government’s consumption of wine from its cellar between 2017-2918, which broke this down into regions/countries. 

I added this data into a Google Sheet, did a few VLookup’s to get the data how I needed it and then linked it into Looker Studio.  First off I created a couple of basic charts, a scorecard of total consumption, and a bar chart showing totals from each region. I then used the map function to heat map the countries that provided the most wine. 

One tricksy thing about Looker Studio, is that it’s not great on design features, so I could either show the map at a continent, subcontinent or world level, so this meant that there was a lot of the world with no data showing up. At a small scale this looked really bad, as you couldn’t really see all the countries in Europe that well at the same time as seeing Australia and New Zealand. Instead I chose to use the map as a background and overlay my two charts in front of the bits of the world that had no data. 

Click on the picture above to open the dashboard. 

Data Source:  Wine and spirits consumption and sales report, 2017 to 2018 - GOV.UK (publishing.service.gov.uk) 

When I was working on my map for day 8 (such a long time ago!!), I was looking at the different data available on Movebank's tracking databank. I cam across this cool dataset where the researchers used GPS tracking on the adults in a group or Baboons in the DeHoop Nature Reserve in South Africa.

I was really struck by the patterns and movement of the group, so decided to present that data for today. 14 individuals of the group of Baboons were tracked for 74 days across the area, leaving this really cool pattern, with each line representing an individuals travels. 

Data Source: Bonnell TR, Dostie M, Clarke PM, Henzi SP, Barrett L. 2017. Data from: Direction matching for sparse movement datasets: determining interaction rules in social groups. Movebank Data Repository. https://www.doi.org/10.5441/001/1.t2212r18

Is it weird to have a favourite polygon? It probably is, but I still have one. It's the LSOA or Lower Layer Super Output Area, which is basically a grouping of postcodes, and it's used for a whole heap of statistical information in the UK, from NHS, Census, spending etc etc. Which is why I love it as a polygon, because there's so much data available to fit in it.

My map uses the Indices of Multiple Deprivation, which are mapped to LSOA areas and show the most deprived and least deprived areas in the country.