Mixing Sensors and Crowds

If you want to know about a physical environment, use sensors.  Take pictures, take air quality readings, take water quality readings, detect radiation levels, use strain gauges,  listen to and analyse noise and the signals in it.  Smell, measure, touch (If it’s not dangerous to do so) what’s around you.  Find people who can teach you how to analyse what you see: imagery interpreters, naturalists and earth scientists for those pictures, acoustics experts, ornithologists and whale experts (as appropriate) for the noises.  You’ll learn a lot, and maybe question a lot too, like why some places have higher radiation readings than others, but at some point you’ll realize that the data you have available is limited by the number of sensors you can use or place by yourself.

Which is where crowds come in.  If you want to understand how the environment changes over time, or how the environment differs over a wide area, you’ll either have to dedicate a lot of time to placing sensors, or work out how to involve other people and organisations in your data gathering.

Journalists are used to crowdsourcing now.   In truth, they’ve been used to it for perhaps longer than even they know: my investigations into the roots of sensor journalism found Francis Galton crowdsourcing weather observations in 1870, and earlier than that, groups of amateurs collecting readings for the Central England Temperature series from 1659 to the present day.  Skip forward hundreds of years, and it’s not unusual for a data journalist to crowdsourced open data, or the Mechanical Turk crowdsourcing site to sift through documents and data.

But how do we ask a crowd of people to place sensors for us? How do we find them?  How do we equip and train them? What are our legal responsibilities when we’re asking them to do physical tasks in the outside environment?  How do we guarantee that the data we get back will be good – or at least, of a known and consistent quality? 

Writing an Ignite Talk

Oh grief, I’ve had posts stuck in my blogs list for a year now, but here’s one that I wrote last year, as part of a campaign to get more non-academics to speak at crisismapping conferences.

An ignite talk is 5 minutes’ talk with 20 slides that change automatically every 15 seconds.  Giving ignite talks is a skill, and one that can be learnt.  With the ICCM ignite deadline coming up soon, OpenCrisis thought that a) an ICCMxVirtual event for people who can’t travel to Nairobi would be good, and b) non-academic mappers could do with some help creating ignite talks. We’re talking about training sessions, but in the meantime, here’s what works for me.

  • Start with a story. Mappers have great stories, but they’re often too modest (“who, me?”) or too scared of presenting to tell them.  Here are some stories that could be told from a mapper’s perspective, to help other groups understand us: a deployment you enjoyed, and why; a technology you’d really like built (and why), good…
  • Sketch out the story. I do this literally, with a piece of paper and pen, or with a set of slide headings.
  • Start finding and making images that relate to the story.  Use a screen-grabber. Look at your maps, your data, your messages, your links, your notes (being careful not to disclose any sensitive data, of course).
  • Start writing paragraphs to match the images.  Some images will take more than 15 seconds – that’s okay, because you can either repeat the image, zoom into the relevant part of it, or …
  • At this point, you’ll realize that things need to be tweaked.  That’s okay. That’s supposed to happen.  If you try to write something perfect first time, you’ll fail (NB I edited this blogpost several times).  But if you get the basic framework together and start getting materials, your story will start to emerge. Encourage it!
  • Start talking… read your paragraphs out aloud… make a recording of it (you can do this from powerpoint, which can also advance your slides for you!), and listen to yourself speak (if you’re shy, video is a no-no: it’s terrible watching yourself, but not so bad to listen).  Edit again.
  • Keep talking… find someone to practice on, who’ll make notes as you talk.  Ask them what the 3 biggest messages they got from your talk were.  Ask them what the 3 biggest messages should have been.  Rewrite your paragraphs to emphasis these; it’s okay to use silence in an ignite talk, and using it around a big message can be a very powerful thing to do.
  • Now you have a talk.  Practice it… and as you practice, start highlighting the key points in your text (I use bold font for this).  If you’re nervous like me, having those key points in a paper in your hand can make all the difference between freezing up completely, and having a way to restart your talk so nobody notices.
  • Finally… the talk.  Get out there, tell that story, and most of all remember that this isn’t a judgement of your message or your style – it’s a conversation between you and the audience that you get 5 uninterrupted minutes to start.  Then take a deep breath and go continue that conversation – in person, online, wherever your audience takes you.

You too are a teacher… yes, you!

I was watching aerial practice recently – it’s something I’ve always wanted to try, but… I’m clumsy, I’m scared of heights, I’m… but so are some of the beginners there too.  Clumsy isn’t the point, nor is getting your legs in the right place first time.  What counts here is that you keep trying, and if you need to, you make it a little easier until you get it (e.g. using a trapeze rather than the silks until the legs go the right way).

This doesn’t just apply to circuses. Some of the best volunteers, deployments and systems that I know started out awkward and clumsy, but kept trying, adapted to where their skills were, and kept trying ‘til they got good.  The circus school has incredible teachers who know when to ask for more, and when to walk over to the trapeze. We have great teachers in the crisis data space too – now we need to distil their knowledge, to best get skills and lessons to all the individuals, deployments and systems that we’re fostering.  That’s why we created OpenCrisis, and why it continues to support other efforts to teach.

I’m heartened that at ICCM 2014, some of our shyest but most knowledgable mappers stood up and talked about what they know, what they’re passionate about – people like Leesa who knows more about virtual PTSD than anyone else I know, and Hilary talking about how to manage gender issues in map creation. But more of you have amazing experience and know so much… please, please, get out there and teach others about it, give them the chance to learn from you too.

Learning to Code

I’m teaching a coding course this autumn, and looking for materials to help explain to non-coders that whilst programming can be magical, it really isn’t magic.

I’ve chosen Ruby on Rails for the course because I want people to win at getting something working and fast.   One of the great resources I’ve found is “the Bastards Book of Ruby” http://ruby.bastardsbook.com/about/, whose “about” section I’d encourage every aspiring non-programmer to read.  Especially this quote: “if you had spent that hour just copying-and-pasting, dragging, clicking, redoing the times that you didn’t properly drag-and-click, you’ve only gotten better at…just copying-and-pasting.

Coding isn’t magic. Most of data science isn’t magic. But they do both need practice and determination to become natural to you.  Not everyone needs their code or science to be magical; if you don’t, then an hour or a day’s training will set you on your way. But if you do, put in the time and do as much practice as you can: it’s worth it.

Sensor trip ideas

I’ve just put in a Seeed order for some more Grove sensors (because they’re very easy to set up and use). Here’s what I’m thinking of doing with them:

  • Collect the usual environmental data – air quality, dust, temp/humid, water, alcohol – and compare against readings from the same sensor set that’s in the Brck office.
  • Wildlife detection using motion sensor, mike, and camera unit.
  • Investigate fire/smoke effects using smoke and gas sensors; I’m more interested in effects of different types of cooking fuels in houses, but could adapt this for camping too (a group I belonged to lost a member to fumes in their tent, so this is of value to me).
  • Human monitoring using alcohol detector and galvanic skin response (e.g. sweat density) monitor.

I’m also thinking about what sort of UAV or balloon data would be useful from a short trip.

  • Even 10 minutes of data would be good – especially if it’s a) messy (e.g. oblique), and b) contains features like buildings
  • Although 10 minutes of nice clean stabilised downward-looking is also a good thing to have: garbage in, garbage out really does apply to aerial images.

Whither crisismapping?

[Cross-posted from OpenCrisis.org]

Crisismapping has never been just about Twitter feeds; it’s always been about data.  But what data, and how do we know what’s useful?  I’ve been looking back over 4 years of archived data to start answering that one. 

In truth, I’ve been having a bit of an identity crisis.  I see all the “big data” work on social media feeds, and although I can swing an AWS instance and the NLTK toolkit like a data nerd, for me personally, that’s not where the value of crisismapping has been.

It’s been about the useful, actionable data, and about connecting the people who have it with the people who need it. And whilst some of that data lies hidden in Twitter streams and Facebook requests, most of it is already on people’s servers and hard drives, often in formats that can’t be combined or understood easily.

So, some first things that make a difference every time:

  • Rolodexes: knowing which response groups to follow, and who’s likely to bring what helps.  3Ws are part of this – but before the 3W (who’s doing what where) is the “who’s”.
  • GIS data: knowing where medical facilities, schools, roads, bridges are makes a difference.   Knowing what communications is available is important, so also knowing where cell towers are helps, but might be too coarse-grained: using signal maps to know which areas have cell coverage is often more useful.  For me, mapping cell towers is problematic for the same reason that mapping military bases is problematic: they’re both potential sources of help in a crisis, but they’re both critical infrastructure whose locations are potentially sensitive information.  But many maps include them (e.g. open signal map).
  • Demographics.  Very useful data, but finding even population counts at sub-country levels can be difficult.  They’re usually there (except perhaps in countries like DR Congo where surveying is difficult) but finding the “there” can be hard.   I’d add technology and social media use to demographics, because there’s no point sniffing Twitter if only 0.5% of the country (and mainly expats) use it – there used to be sites available that listed, e.g. Facebook, Twitter etc percentages in each country, but they all seem to be behind paywalls now.

After that, it’s the emerging data: the 3Ws, the situation reports (both official, via news sources and on social media), the field notes about what’s happening.

We also now have 4 years of historical crisis data collected and collated by volunteers, often in areas prone to repeated crises, on top of the data already available through organisations and groups that existed before crisismapping was a “thing”.  I’m not entirely sure what the value of that data is to the next crisis (like wars, every crisis is subtlely different), but it’s certainly worth working that out.

Tanzania day 5: Internet! (ish)

Woken at 4am by mosquitoes – I left the bathroom door open, and the insect screen only works so far.  None of the hotels here have mosquito nets, so I spend an hour listening and swatting before going back to sleep.  I breakfast on scrambled egg and bananas: my substitute for all the bread products I keep being offered.  The team is here: I watch them do a soil type analysis (using a flowchart: seeing if they can form muddy balls from the soil samples, seeing how long a ribbon of mud they can make (1cm? 2? more?) then finally rubbing in their hands to see if it’s “gritty” or not.  Most of the soil we collected is clay or sandy loam.  It rains.  Msofi and I swap British and Kiswahili words for different types of rain (we both have many of these).

I watch the team do rapid roadside assessments.  In rural areas (red dirt roads), these are done every 2km; on main roads every 20km, and in LOs (regions with a large concentration of surveys) every 4km.  A roadside assessment is just that: the team stands at the roadside, looks at the 50m by 50m area of land in front of them, assesses plant cover, plant types and use, and takes a panoramic photo of the area.  We stop and do 4 of these assessments on a dirt road, and mark the sheet for this with the from and to village names of the road.  I chat with Gervase, who is a seriously good systems thinker – he talks about how he persuaded rural people to adopt more efficient cooking stoves not because of environmental concerns: people living out here don’t understand the concept of save the forest, since the forest is all around them, but do understand the concept of “your eyes won’t get so red from cooking”.  In some areas, red eyes are seen as a sign of witchcraft, and people’s grandmothers have been killed for this.  I have serious respect for this guy and how he gets things done.

We go back to the hotel and go through the data entry and upload process, where data from the paper forms are entered into a tablet, then sent up to the head office servers.  The team are using Samsung tablets which have slots for simcards – available from Amazon, but I’m warned that the Dubai versions have a smaller simcard slot which means cutting down larger cards.  The team uses ODK Collect for its forms – these are available from the main server, but have changed many times already (mostly bug fixes, e.g. not being able to enter lat/longs ending in 0). We start with the soils form, but Joseph has the old version of the form on the tablet he’s holding.  He finds the right form on another tablet (internet here is still terrible), and walks me through the data entry.   There are many forms (and many versions of those forms): Eplot, soils, rapid roadside assessments, water rapid assessments, water lab reports, household surveys, agriculture surveys, farm field and crop surveys, the contents all of which we will have to make comprehensible (along with satellite data, open data etc) to decision makers.

We say our goodbyes and thank yous to the field teams (all Tanzanian, all experts who play games like “name that tree” with each other) who are starting their Easter break after 40 days in the field.  They’ve done roughly 50 of 350 Tanzania sites so far, and have much still to do.

We set out for the bright lights of Iringa, the big town in this area and a 2 hour drive away.  We talk about water sampling methods and the issues of vandalized equipment and data not getting from the basin offices to the central ministry of water.   There are potatoes roadside now, and big boulders that look like glacial moraine, which confuses me – were there glaciers here?  We talk about the timber lorries we pass – there’s a huge need for timber across Africa; there’s much construction, and the people who own woods will become rich on this.  I think about the transitions between old African and new Western-style systems that I’m seeing, and think about the things that get lost in that transition.  Some of these are tragic, e.g. there are many blue babies (brain damaged from lack of oxygen at birth) born here now because the traditional practice of midwives sucking on babies’ noses to clear gunk has been lost in the new Western-style hospitals, but hasn’t been replaced by the Western-style use of suction bulbs to do the same thing.

We reach Iringa, and start hotel-hunting: Avery knows two places in town with wifi. We try the first one: a craft shop, café and guesthouse run by disabled people.  The rooms are beautiful, but their wifi is out.  We hear about a guesthouse by the university, and try that – no wifi, but there’s a strong signal from the nearby internet café.  The five-story glass building opposite seems surreal after a week of one-story houses.  Life now is all about getting wifi, and getting back to ‘normal’ work.  It’s a catholic guesthouse – we’re staying amongst nuns and looking at pictures of the pope, but there’s also a bar in the evening.  We pick up wifi vouchers from the café (5000 ksh for 5Gb) and head off to lunch.  We’re in the tourist zone, and the first café we try is full of earnest young Americans, English menus and high prices.  We go round the corner to a local place and eat lentils, beans, rice and salads off tables with tablecloths and matching cruets then head back to the hostel to get online.  Nicky goes off to get the car fixed – the long fast drives over rough roads have damaged a pipeline and bearing. Which is painful… the bandwidth is so slow that OpenStreetMap goes to the low-bandwidth version, and I can see the titles of my emails (eventually) but not the contents.  I manage a brief Skype conversation with Nairobi before giving up.  Avery goes off to buy her bus ticket (she’s going up-country from here for Easter), and I haggle for local fabrics (blue chickens!).  Then we switch to plan b: the other hotel with wifi has a restaurant, so we head up into the hills above Iringa to a place that even my clean jeans feel a bit underdressed for, and eat Indian food with our laptops in front of us.  I finally get an OpenStreetMap editor open on the area that we were lost in, and show the team how well the red dirt roads and waterways stand out against the vegetation.  When I have good internet again, tracing will happen, so the field team have a small-scale map to start from next time.

We head back to bed – the hostel rooms have mosquito nets, so I sleep (in the trying-not-to-stick-anything-outside-the-net position, waking once to the sound of a frustrated mosquito in the room.

Tanzania day 4: Field Trips

We saw a lot of schoolchildren in uniforms yesterday – in the afternoon, they were walking past carrying hoes.  We go shopping for a cable to charge my phone and  camera: I’ve lost one cable and broken the other, so it’s off to the local shops, each of which points us to another one: general store, electronics shop, phone shops, camera/video shops, some in buildings, others in plywood shacks. Finally have two colourful cables with smiley faces on the ends (the powerstrips in the shape of hearts are tempting too). Avery buys fruit from a lady with a huge basket on her head: it takes 2 of us to the lift the basket back up.  We check the cable: it’s the car adapter that’s broken, not the cable: the team lends me an adapter for the ride.  Add to field shopping list: car chargers, and lots of them.  The car is filthy: I just miss a shot of it parked next to the same colour and model: left car is red; right car is brown.

Gervase and I talked about sample areas last night. One set of sites is in a wildlife area, with twin dangers – from the wildlife, and from the poachers chasing the wildlife.   The team can look “official” in their khakis – Joseph tried to ask a local man for directions yesterday, but the man ran away – a common thing here.  I have huge bruises up my arms, splinters everywhere, torn jeans – but no bugbites. We saw trees, plants, flowers yesterday but almost no wildlife: some insects and butterflies (not many), birds (ditto): nothing larger, although we did find wild pig poo and some elephant damage on a tree.  We also found an animal trap on the path (Joseph got caught in it), but the jungle was eerily silent.

We arrive and walk to the first site (300 metres through open woodland – yay!), but the site’s start point is in a swamp.  This happens occasionally, and there’s a protocol for it: the team moves the corner to dry ground with similar vegetation to the swamp, and marks on the papers that they’ve done this.

Joseph explains the sampling protocol in detail and I take many many photos of soil, buckets and poles.  First tree-counting: they use the ranging pole (long pole with tape markers every 50cm) to find all the trees within 1.5m of the plot start point (“corner”), then they measure every tree over 5cm diameter and 50cm height (knee height).  They do this so the analysis team can estimate carbon sequestration in this area.  Each tree’s diameter is measured with a tape; the small tree heights (5m or less: the ranging pole is 2.5m long) are measured with the ranging pole; the larger tree heights with a clinometer; the tree’s canopy width is measured with the ranging pole (in dense woodland, one person shakes the tree so the others can see where the canopy is).  Once the team starts on a plot, the same team member does the same measurement jobs at every “subplot” point: because people’s estimates vary, this gives some consistency of measurement across the plot.   The team takes soil samples by putting down the metal plate and pushing a corer (also marked up with tape) through the hole in it; putting soil into buckets marked 0-20cm (topsoil), 20-50 (subsoil), 50-80 and 80-100cm (the 80-100 sample is only taken in the first corner): these depths are based on the Afsis (African Soil Information System) sampling protocol.  The team also throws down a quadrant (50cm metal square) and counts the species in it, how much of each it contains, how much bare earth, dung etc; and uses a densiometer (mirror marked into squares) to estimate the canopy cover north, east, south and west of the start point. They also hang a scale bag in a tree, put 500-900g of each soil in a marked plastic bag, weigh the bags, and also weigh marked metal pots containing samples of each layer of soil (the pots are added to a plastic bag attached to the scale).

We hear a shotgun – the team thinks it’s just a car backfiring or tyre blowing out – there are very few guns in this region.  We move on to the subplots.  What we’re doing is called the “E” plot – we’re sampling a 100m by 100m square area, with “subplot” points every 20m in that grid (e.g. there are 36 subplots to a plot).  The shortest path with the smallest errors through that grid is in the shape of a large “E” (eeeeessssswwwwwneeeenwwwwneeeenwwwwn), hence the name.   At each subplot, the team counts and measures trees (trunk diameter, height, canopy width, species) and estimates the biomass only in the quadrant (from the average plant cover and heights).  The team used to do the full works (everything done at the first corner) at each subplot, but the process was long and has now been streamlined.

There are lots of trees here – I learn Kiswahili for “please shake the tree”.  It rains a little, and we have 33 subplots to go.  There’s more noise here than in the jungle: birds are singing, insects are buzzing.  I ask about wildlife protocols – there aren’t any animal, insect or bird protocols yet.  The team were going to set camera traps for animals, but they were too expensive; I wonder if there’s something small and cheap we can hack together with a camera board, motion detector component and microprocessor.

We move over the road to a Pinus Patula (misheard as “Spatula Pine”, which causes much amusement) plantation – well-spaced small trees with soft spines and wild mint growing underneath.  We talk about Apopo’s work on mine-sniffing and TB-sniffing rats, and how this works in Africa but not so much in SE Asia.  At every corner of 100m grid, we take soil samples and do detailed quadrant analysis; we also do this at the centre of the plot, where we take a panoramic photo (the camera has built-in GPS).  I’m still picking thorns out of my head; at the centre, I sit on a fallen log, and Avery rushes to brush the ants off me (I now have ants in my pants).  It rains heavily. The team gets wet, and the car goes red again.

The team has spilt into two, to get two sites done for the day.  We drive over to the other site; it’s raining heavily so Avery and I sit in the car and work on our laptops.  She gets a good data signal for the first time in days (the signal at the hotel is non-existent) and I manage to post an “I’m safe” home.  The flowers here are beautiful, making the site look like an English cottage garden: huge purple mallows and something that looks like clematis overlaying delicate yellow flowers – I wonder how many common British garden plants have come from here.  It stops raining for a while (this rain is monsoon-grade), so I walk out to the site: across a swampy valley, through woods, across grassland to a firebreak between woods.  I find the team’s start point (an umbrella over the sample pots), and track them to the edge of the woods – which look impenetrable: someone has chainsawed the plantation but, puzzlingly, not taken away the fallen wood.  Trees have grown up through the fallen wood, and the whole effect is one of a giant woven basket.  I hear the team’s voices, find a not-so-bad patch, and push through, under, over, across trees to reach them.  They seem surprised: they’ve been pangaing through these woods for hours now, and weren’t expecting anyone to just go through them.   They’re still on the outside legs of the “E” plot – with 22 subplots to go.  These are very different woods than the morning, so I tag along to see how they sample in dense woodland.  There’s a lot of scrambling but not so much wait-a-minute here: today is branch scratches rather than thorns.  Moses the biologist tells me that there are two species of wait-a-minute, and that the one by my head is related to the orange tree – I crush a leaf, and yes, it smells of oranges.  In the firebreaks, I see animal tracks going into the wood – later, whilst crouched under a fallen tree, I hear what sounds like a boar grunting annoyance.  It gets late: 5:30pm in a place where the 6:30pm sunset is a sudden from light to dark.  The team has 10 plots left to do, and push on, quickly measuring trees and assessing the ground cover.  Just before sunset, we finish and rush back to the cars before dark.  I’m wet, cold, muddy, sunburnt (regretting not bagging that aloe) and happy that I understand a lot more about what it takes to collect this data, what it means and what we could do to help.

Tanzania day 3: Welcome to the Jungle

Today we go into the field.  Woken by laptop charger fizzling – electricity is available but a bit variable here.  Breakfast with sweet milky tea – the tea in it is grown and picked here in Mafinga.  We drive past tree plantations – pines, for their wood. I ask about the rice; Tanzania is a major rice grower, and much of it comes from Morogoro.  I meet the rest of the team, and explain Ushahidi and my own skills to them, as asked, armed with a notebook, pen and much arm-gesturing.   We drive off to the site; on red mud roads, fast.  The team truck has a snorkel and I worry they might be using it.  Finding routes to the sites is an issue, and the gps units fill up if the team trys to track roads: we talk about roadmapping using their GPS-connected tablets and Funf, and about OpenStreetMap traces from Bing’s satellite images.

This area (Mafindi)’s economy is based on trees, tea and maize; I see eucalyptus (grown for its wood) and other trees grown for paper.  I see some cows – we talk about the perceived difference between pastoralists (many cows, with perhaps a small piece of land for vegetables) and farmers (mostly crops, with perhaps few cows), the conflicts between them, and how farmers often don’t count the cows as part of their farming.  We see the first tea plantations of many – I’m surprised that the tops of the plantations are flat rather than tea bushes.

We drive fast for 2 hours on dirt roads – we’re remote but trade is visible; motorbike repair shops and women wearing cloth made in Nigeria.  We see cabbage fields – European missionaries settled here because the climate was familiar to them; there are some churches but also many schools in this area.  It rains again – the roads are getting muddy now.

We stop on the road.  We’re 7km from the site, so we go back and try another road.  The logging trucks are out now (11am) – Mafindi Paper company, and little trucks with offcut bark.  If only we had a roadmap.  We talk about how to improve tea production efficiency, and wonder about mechanical harvesters – just before we see mechanical tea harvesters: giant lawnmowers pulled over the tops of the tea plants.  We reach a dead end: 6km away now.  Really really need better roadmaps for this project; I can feel an OpenStreetMap session coming on.   And here’s how to help:  there’s Internet here in the sticks, but it’s slow – too slow to sensibly edit maps from here.  But internet is good in New York, London etc: so if you help OpenStreetMap make better maps of this region (southern Tanzania), local people can get on with the things that they need to do, like plant monitoring, instead of getting lost.  I’m told it takes 4-5 hours to process each plot, and that each plot will be revisited every 3 years.  If you add getting lost onto that, it becomes a very very slow process.

We stop at a dead end 5km away from the sample site. It’s getting late, so Joseph, the field team lead, asks me if I’m okay with walking 5km (3 miles) – he explains that 5km straight is probably going to be a lot further on the trails, and that it might be a bit up and down.  I look at the tea plantations around us, and think “hey, this is just like walking in Dorset”.   We set out… across the team plantation and down into woodland – walk downwards for a while, then retrace our route because two of the team are yelling that there’s a river in our way.  We set out again… down through woodland, across a small brook (which I’m hoping is the river), up past a small shack with a fire going, and small garden with mint and vegetables (Joseph explains that sometimes the farmer stays with his fields), and up through a maize field.  The field is closely planted – I follow the voices ahead of me.  The field is underplanted with courgettes, which I take care to step around; and then we’re out into another field – some type of wheat?  at the top of a hill.  We go down through woodland again – a slippery muddy path that looks well-used. Someone mentions that we’re going down to the bridge the plantation workers told us about.   I think “ah – a roadway; great’. They don’t mention that the ‘bridge’ is a pair of tree branches across the river.  The guys walk across one of the trees then jump onto the far bank; Avery and one of the guys wade waist-deep across instead.  I take the tree – the guys build me steps down out of their field plates.   This is the first time I hear them say MamaSita; I hear that a lot soon.  We walk along a small trail going up through the trees – and then the trail stops.  It’s panga time: Joseph starts hacking a trail through the jungle, and the walk becomes a long repeat of ducking under vines, picking “wait a moment” (bramble-like plants) off our arms and heads and waiting for enough of a trail to be cleared.  There are holes in the jungle; I step over most of them, but it’s muddy, and sometimes I slide thigh-deep into them.  The guys talk all the time – laughing, teasing each other, talking about politics.  We stop every so often, and call out the distance to the sites.  First stop, it’s still 5km.  Then 4.8 km; we have a
picnic” of samosas (or in my case samosa innards: it’s tough being gluten-free in the jungle).  After 3 hours ducking through the wait-a-moment (jungle: I thought snakes and big cats would be an issue: turns out it’s falling into holes and picking big thorns out of your head), it’s 3:30pm, we’re 3.6km away from the sites, and have 3 hours of light left for the day.  We turn back, planning to return the next day.  The route back takes 1.5 hours: when we return, Nicky has collected local pears for us all.  We drive back past towns with repair shops and chickens, past recently-logged areas and more logging trucks.  I’m exhausted and covered in scratches and bruises – I crawl off to sleep for a while.

Tanzania day 2: the Safari Commute

Today I meet the team: we breakfast, talk about the plan for the week (travel, measure, measure, rest, travel) and set off on the road to Iringa.  I’ve now been in 2 of the “big 5” wildlife countries and so far have seen: 1 dog.  I’m hoping we might see something else in the parks.  The road is very quiet – most of the traffic has stopped because of the traffic jams around the flooded bridge, which is great in terms of having the road to ourselves, but no so great in terms of being the only car around for the traffic police to stop.  They stop us and show the radar gun (the most common sensor that I see around here) – speeding.  We stop next to one of the communities making woven baskets – I’m tempted to go shopping but know that would just increase our chance of a fine.  I see another dog.  We pass the waterfall where our driver once took a hippy who tried to teach him about transcendental meditation.  All the police want to talk to us, to see the car’s papers – today they must be bored.

And then we enter the Mikumi National Park.  Right away there are baboons – mothers with frisking children, big proud males with bright bulbous bottoms.   Then giraffes, posing tall under shady trees. Impala peeking through the bushes.  Elephants ranging in the distance. Wildebeast and zebra sharing a watering hole.  And more giraffes.   Someone jokes that this is the “safari commute”.  We eat good African food just outside the park (last night the hotel staff made me a late meal: of chicken and chips, then eggs and frankfurters for breakfast), then continue through the plains and along a river.  As promised, there are Masai herding cows whilst on their mobile phones (“they carry two at least”), and small boys with sticks and goats.  I look at an NGO crew in their big white 4×4 and wonder how many of them actually know what it’s like to be poor – not poor as in student, but poor as in having to make the difficult choices you make to survive.  We see onion stands near the river – you can map your location here by what’s on the vegetable stands: onions, mango, tomato, peppers, and finally, in the high plains past Ikaya, potatoes.   We climb, past a crashed lorry, up a mountain road that Nicky tells us once had a phone at each end because it was too narrow for vehicles to pass).   We’re in high meadowlands now, and there are many sunflower fields.

We stop in Mafinga and choose a hotel – the cheaper one that just opened today.  I sign in as their first guest, and put “Webb” in the “tribe” column.   We eat local chicken, plantains, rice and big sweet avocados and talk about maps.