The OSM UK community is great, but it's hard to guarantee we can do the detective work to spot all 800,000 solar PV installations. There's a "long tail" of solar panels tucked away down side-streets. It's very much a needle in a haystack, and we would benefit from as many hints as possible.
(We're already working with (a) machine learning and (b) official data sources. They're good sources of hints too, but not the full picture.)
We could ask the general public for help with this. Almost everyone must pass a solar panel during their daily commute, their weekend stroll, or suchlike. But we can't expect the general public to use map editing tools, or in fact anything that requires technical commitment or expertise. Also nothing that requires login or user registration.
Can we make a tool that makes it so simple, that many thousands of people can send in just one or two sightings each?
We don't have the resources to make a fancy phone app. (And would people use it if we did?)
Option one: "drop a pin in a map" approach. Provide a simple webpage which lets people, with no login required, put a pin at a location where they think there's a solar panel. This is fairly easy to code (and could use the OSM Notes API, e.g. with a specific pre-agreed template for the Note text). However it's not ideal for people out-and-about with a smartphone, since it's all about the top-down birds-eye view.
Option two: people can take a photo of a solar panel they see, and post it to a service they already use. (Smartphones often record GPS location along with photos.) Posting to Twitter (with a particular hashtag) would be easy to set up and to scrape. However not everyone uses Twitter. Loads of people use WhatsApp. Can they report their solar spottings directly through a WhatsApp number? It would need someone to set up a number, fine - and then the coding required is to create a system that can slurp whatever photos were sent in, do a bit of sanity-checking and maybe some basic "bot" interactivity, and output a dataset of suggested-geolocations for panels. (These suggested-geolocations would not go into OSM directly, they're not appropriate for that. We can simply provide them as a dataset for mappers to refer to.)
Thanks to Max+Esther at 10:10 for suggesting the second idea. I approached them because 10:10 has previously been involved in something a little bit similar (a mobile app for spotting rooves that would be good places for new panels). They've clearly got the right idea for the kind of simple everyday interaction that's needed.
Where are all the solar panels in Britain? Are they in the south? The sunny east? The countryside, the city?
The UK's office "Ofgem" publishes open data about the solar PV installations that they know about. In the latest "feed-in tariff" (FiT) data, there are about 800,000 of them. The "installed capacity" adds up to about 4.9 gigawatts, about half of which comes from big industrial field-scale installations and half from domestic rooftop solar.
It would be handy to know where the solar panels are - for example, if you're searching for solar panels to map...
For privacy purposes, Ofgem don't publish exact locations, nor unique IDs, in their big spreadsheet. So the data aren't perfect for mapping, but they do give us the postcode district for 90% of these 800 thousand. So, using that postcode info, I've taken their data and simply plotted them on a choropleth. Let's take a look!
Before you look, please note that I'm plotting the raw numbers per postcode district, and NOT normalising the data to account for the size of the district. This partly explains why the plots look "dark" in the regions (such as London) which are chopped up into lots of small districts. Smaller districts should have fewer things in... but on the other hand, smaller districts are supposed to equate to higher density of households, so maybe the postcode district is a good unit of analysis after all.
Here are the plots - three plots showing, respectively, the raw number of installations per district, the total installed capacity in each district, and finally to get an idea of household density I also plot the number of households there are in each district according to census data:
And here's a CSV spreadsheet of the summary FiT numbers I used to plot these. Sorry for not showing (Northern) Ireland, it's not in the data I found.
(The CSV and the images are all derived from Ofgem's FiT data which are published under the Open Government Licence.)
Note that there are A LOT of caveats about this data. About 10% of the solar installations (80 thousand!) whose postcode district was listed as "unknown". Also some postcodes are allegedly not quite right (e.g. some of them are the postcode of the person who registered, not the location of the thing itself). Some of the installations they've listed might have been discontinued, and we don't really have much way of knowing. Oh, and... the postcode area data I'm using seems to have some omissions, hence the occasional white gap in Britain. But notwithstanding all that, this gives us some indication of the distribution.
One thing that pops out to me is that these three plots don't seem very correlated. I'd have expected them all to be highly correlated. For some reason it looks like a relatively high number of small-capacity installations across Yorkshire down into Essex. There's plenty of regional variation and clustering, which may be due to geographical/weather differences, or perhaps to local initiatives.
Jack had this great idea to find the locations of solar panels and add them to OpenStreetMap. (Why's that useful? He can explain: Solar PV is the single biggest source of uncertainty in the National Grid's forecasts.)
I think we can do this :) The OpenStreetMap community have done lots of similar things, such as the humanitarian mapping work we do, collaboratively adding buildings and roads for unmapped developing countries. Also, some people in France seem to have done a great job of mapping their power network (info here in French). But how easy or fast would it be for us to manually search the globe for solar panels?
(You might be thinking "automate it!" Yes, sure - I work with machine learning in my day job - but it's a difficult task even for machine learning to get to the high accuracy needed. 99% accurate is not accurate enough, because that equates to a massive number of errors at global scale, and no-one's even claiming 99% accuracy yet for tasks like this. For the time being we definitely need manual mapping or at least manual verification.)
(Oh, or you might be thinking "surely someone officially has this data already?" Well you'd be surprised - some of it is held privately in one database or other, but not with substantial coverage, and certainly almost none of it has good geolocation coordinates, which you need if you're going to predict which hours the sun shines on each panel. Even official planning application data can be out by kilometres, sometimes.)
Jerry (also known as "SK53" on OSM) has had a look into it in Nottingham - he mapped a few hundred (!) solar panels already. He's written a great blog article about it.
This weekend here in London a couple of us thought we'd have a little dabble at it ourselves. We assumed that the aerial imagery must be at least as good as in Nottingham (because that's what London people think about everything ;) so we had a quick skim to look. Now, the main imagery used in OSM is provided by Bing, and unfortunately our area doesn't look anywhere near as crisp as in Nottingham.
We also went out and about (not systematically) and noticed some solar panels here and there, so we've a bit of ground truth to put alongside the aerial imagery. Here I'm going to show a handful of examples, using the standard aerial imagery. The main purpose is to get an idea of the trickiness of the task, especially with the idea of mapping purely from aerial imagery.
It took quite a lot of searching in aerial imagery to find any hits. Within about 30 minutes we'd managed to find three. Often we were unsure, because the distinction between solar panels, rooftop windows or other rooftop infrastructure is hard to spot unless you've got crystal-clear imagery. We swapped back and forth with various imagery sources, but none of the ones we had available by default gave us much boost.
While walking around town we saw a couple more. In the following image (of this location), the building "A" had some stood-up solar panels we saw from the ground; it also looks like "B" had some roof-mounted panels too, but we didn't spot them from the ground, because they don't stick up much.
Finally this picture quite neatly puts 3 different examples right next to each other in one location. At first we saw a few solar panels mounted flush on someone's sloping roof ("A"), and you can see those on the aerial - though my certainty comes from having seen them in real life! Then next to it we saw some stood-up solar panels on a newbuild block at "B", though you can't actually see it in the imagery because the newbuild is too new for all the aerials we had access to. Then next to that at "C" there definitely looks to be some solar there in the aerials, though we couldn't see that from the ground.
Our tentative learnings so far:
- We will need to use a combination of aerial mapping and on-the-ground "solar spotting" from people.
- Whether on-the-ground or aerial, it's often hard to get a clear idea of the size of the installation. In lieu of that, maybe it's fine to map them as points rather than areas. People can come along later and tell us the actual power ratings, efficiencies etc.
- We will need to make the most of heuristics about where to find solar panels. For example Jerry notes that social housing is relatively likely to install solar, and I've noticed it on plenty of schools too; there may also be vendor/official lists (e.g. planning applications) out there - we'll need multiple sources to get a well-rounded coverage.
See Jerry's blog for more learnings.
There are plenty of virtuous feedback loops in here: the more we do as a community, the better we'll get (both humans and machines) at finding the solar panels and spotting the gaps in our data.
In mainland Britain, you are never more than 34 miles from a pub.
As a contributor to OpenStreetMap, one thing I've been wondering recently is what sort of map data should we collect for the UK, now that the coverage has already got good. Since OpenStreetMap generally has great coverage of the UK, when you're out and about with a printed-out map and a pen, it's very rare that you can find much significant that isn't mapped already - sometimes a new street or a missing church. You could pour your time into mapping increasingly obscure things, whatever you're interested in. But what would be the most useful things to map in the UK, over the coming year? Things that are not just interesting to map but could be practically useful to people? Some thoughts:
- Addresses. I kind of don't like mentioning this, because I find it boring to map addresses, and I'd much rather that the UK address data magically appeared from some big open-data source. But addresses are obviously really useful for so many things: routing, looking up shops, etc. Coincidentally, Simon Poole (chair of OSM Foundation) also says address collection is the thing we need, for OSM in general not just UK.
- Postcodes. In the UK postcodes are really important for satnav routing etc. For some reason I suspect that collecting postcodes could be less mind-numbing as collecting addresses, but just as useful. See Jerry's blog about UK postcodes in OSM for an analysis of where we are with postcodes... about 3% of them. As he says, we need to do better than this - so how best to collect them?
- Footpaths. Really important for planning walking routes, whether in the city or the countryside. We also need to mark when footpaths have steps or are otherwise no good for wheelchairs/prams. (It's also handy to know when footpaths are full-blown rights of way, or just "permissive" access.) In his speech at State Of The Map 2013, Peter Eastern mentioned that they estimated UK footpath data was still pretty incomplete. I often use OSM for planning walking routes - it has loads of footpaths that no other services have, but I do still often go walking somewhere and find new footpaths that aren't in there yet. I don't know how we could specifically push for more footpath mapping - all I will say is please help us and map walking routes :)
Some notes on other things which I'm not sure how vital they are:
- Buildings. I know when we've been doing London mapping meet-ups, Harry Wood has mentioned that OSM's buildings coverage for London is rather patchy. You can see it on the map - there are pockets full of buildings mapped, and large pockets with none. But... is this a bad thing? What would we want buildings mapped for? I know they're useful in fancy 3D map renderings, but for more practical purposes...? I'm guessing it's not that crucial, though it might relate a bit to the address mapping.
- Shops. It's great to have shops, restaurants, pubs and other local businesses in OSM. Once you start mapping these, though, you notice there's quite a rapid turnover - your high street probably gains/loses a shop every 3 months or so, at a wild guess. So this data is useful, but it's less permanent than all the other stuff I've mentioned so far. I'd suggest there's no point having a big push to map every shop in every high street, we just need to let the momentum build to a point where that happens under its own steam.
- Postboxes. Again Jerry has a detailed breakdown, and says we need to map them more. Plus Robert Whittaker has some data mining tools about postbox completeness. On the other hand, is it really that urgent to map postboxes? It doesn't feel anywhere near as critical as mapping addresses, walking routes, etc. The only use case I can think of is "where's the nearest postbox?" which is rarely a critical matter.
- GPX traces. After MapBox published their beautiful rainbow GPS map tiles which provide a lovely way to see the GPS traces contributed by the community, I noticed at least two villages where there were basically zero traces uploaded. Are GPS traces important to UK mapping? The coverage of the aerial imagery is good, and generally quite well GPS-aligned, so... do we need more GPS traces around the UK? I genuinely don't know, and would be interested to find out either way.
- Grit bins. Something I noticed a couple of winters ago - it would be really handy to have every grit bin mapped: one day, when it's freezing cold outside, all the grit bins are hidden under a foot of snow, and you need to clear a driveway, it could be really handy. That's just one little thing that I don't think anyone has particularly focussed on, so a little call out - please map amenity=grit_bin when you see them!
I'd be grateful for any feedback on the thoughts above, including other things that could be priorities. Just one UK mapper's perspective.
On OpenStreetMap, I find the /browse/ pages really useful for getting a quick summary of an "object" in the map. It shows when it was edited, shows all the tags, etc.
However, I have two issues with it:
- The use of space isn't ideal. There's plenty of unused space which I don't think is entirely deliberate (of course whitespace is good sometimes) - and the interesting information often gets pushed down below the fold as a result.
- The browse pages have enough information that they should be generally useful, not just as a diagnostic tool for mappers, but maybe for people who want to share the details of the pub they're going to, or whatever. The main impediment to this is that the initial impact of the page is fairly unfriendly and technical.
I believe the layout can be rearranged in a way which doesn't remove any of the information that mappers need, but which makes the browse pages more accessible and friendly and hopefully generally useful. This would encourage more casual users to see the tags we have, and... fix them :)
So the main objectives are:
- Make the main heading a bit more approachable, making the "name" (where available) a bit more primary than it currently is.
- Make the "Tags" section a little bit more visually primary (more approachable to newcomers than changeset).
- Make the "last edited" info more compact - it doesn't need to be a four-row tabulation, but can be as a sentence "Last edited [date] by [user] (version [v] in changeset [c])". It makes sense to put the "View history" link at the end of this too. Also, it's more approachable to have the last-edited-date converted to something like "2 months ago", and for full info it'd be good to have the full date tooltippy.
- Try not to do anything that prevents experienced mappers from getting a visual overview of the more technical info, such as history, XML link, edit links etc.
Work so far is in my github branch called "browsepage". Here are some screenshots, in each case with "before" on the left and my version on the right:
I really think the "Last edited N decades ago by Thor" is much more approachable than the current table of metadata. The other stuff I've done is less dramatic, but I like the way it gives a bit more priority to the tags and makes room for plenty of them in a screenful.
Update: someone asked if I could post how the pages look on small screens (i.e. phones) - here are screenshots, taken by resizing my Firefox window small enough that the small stylesheet kicks in:
The big annual meetup of OpenStreetMap folks was last week and it was full of interesting talks. The diversity of people seemed pretty good relative to a lot of the meetups I end up at (open-source software, experimental music, computer science, you know, that kind of thing), but still, the …
Another iteration of my visualisation of OpenStreetMap edits - here's an animation showing, for each year 2005-2012, the density of edits according to their geographic location:
The upper plot is the raw edit density. The lower one (which I think is more illuminating) is the edit density per unit population, as …
I watched the fancy OpenStreetMap Year of Edits 2012 video, which shows a data-driven animation of all the map edits happening around the world from thousands of contributors. It certainly makes the project look busy!
BUT it's not the kind of data-viz that particularly wants you to understand the data …
OK, if you want to know where in the country has good pubs, how do you do it? Well, here's what I do: download a data extract of all the pubs in the UK/Eire from OpenStreetMap, and use density estimation to look at the distribution of pub attributes such …