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mvam: the blog mobile technology for wfp's food security monitoring menu skip to content home about mvam methodology videos articles contacts online course podcast search search for: vam talks episode 16: the 72-hour emergency assessment approach july 24, 2018 / mvamblog / leave a comment in order to improve the speed and efficiency of wfp’s response to sudden-onset emergencies, a 72-hour emergency assessment approach has been developed. in this episode, we are exploring the origins of the 72-hour approach, how it functions, and how it has been used in nepal in response to various natural disasters. featuring: simon hollema , senior programme and policy advisor in wfp’s regional bureau in bangkok; yingci sun , regional programme policy officer in wfp’s regional bureau in bangkok; and pushpa shrestha , vam officer in the evidence, policy and innovation unit in wfp’s nepal country office. http://mvam.org/wp-content/uploads/2015/12/new-72-hour-podcast-finished-online-audio-converter.com_.mp3 tweet follow @mobilevam mapping our way towards zero hunger july 20, 2018 july 20, 2018 / mvamblog / 1 comment last year, we reported on our efforts to develop techniques and maps which allow us to have a picture of the food security situation at a very high resolution, so that we can pinpoint exactly where people are food insecure and allocate resources as efficiently as possible. the challenge, however, is that our food security estimates from survey data are typically only representative at a high administrative level such as a state or region. to get a precise estimation of food insecurity in one region, wfp needs to interview approximately 1,000 households in about 30 different villages. doing this data collection exercise at a smaller administrative level is most of the time simply not possible due to time, resource and security constraints. how, then, can we get a picture of the food security situation in communities where no surveys have been conducted? how can we map food security at the most granular level, in the most efficient way, and using tools that are accessible to a broad range of users? one approach we are trying out which could tick all of these boxes is what we call “humanitarian high resolution mapping”. the idea is to develop an app that automatically draws on two components: it uses various open data sources (which deliver data on features relevant to food insecurity such as nightlights intensity, vegetation, water, or infrastructure) and combines them with existing survey data to produce maps. next, artificial intelligence (ai) comes into play: we are using “ machine learning ” to train models to learn what the relationships between the survey indicators and the features extracted from the open data sources are. once these models are trained, they are able to make predictions for areas where no survey data was collected and produce maps based on these predictions. but let’s look at it step by step! our first important source of information is, of course, existing survey data: on the survey side of the equation, we have been training our model with survey data from wfp, the world bank and the demographic and health surveys program. geolocation is available for the data at household or cluster level, meaning that we know with reasonable accuracy where the surveys were conducted. it is very important to take into account the spatial relationships in the survey data: a food- insecure household is more likely to be located close to other food-insecure households than in the middle of a wealthy population. this is why we run a so-called “k-nearest neighbor algorithm”, which takes into account spatial correlation and makes predictions for a household’s food security based on its proximity to other food secure or less food secure households which have been surveyed. secondly, we extract remote sensing information from the following open data sources: 1) openstreetmap (osm) one factor that drives household vulnerability is proximity to infrastructure: osm is usually the most accurate source of data on infrastructure in developing countries. we therefore extract the location of schools and hospitals and calculate the distance from the area we’re interested in. a big thank you to all the openstreetmap contributors! 2) the national oceanic and atmospheric administration (noaa) of the us department of commerce as research has shown, nightlights are highly correlated with poverty and to some extent also with food security. that’s why we also include information on nightlights in our calculations and get information from the noaa on the luminosity of the area and time that we’re interested in. 3) sentinel 2 satellites, from the earth observation mission of the european space agency (esa) we are able to get multi-spectral images of the earth captured by sentinel 2 satellites every five days at a resolution of ten metres. using the google earth engine platform, we extract different indices from these images, such as a vegetation and a water index. these allow us to identify, for example, the density and health of vegetation during the growing season – key pieces of information needed to understand the food security of a given population. 4) transfer learning using google maps, sentinel 2 and noaa nightlights data satellite images from google maps and sentinel 2 are fed to an ai-powered programme that was previously trained to extract features that can be correlated to levels of poverty. the models used in this case are trained with nightlights from across the african continent and used as a proxy for poverty. it is quite a technical process and is inspired by the work of stanford’s sustainability and artificial intelligence lab . however, our results so far demonstrate the added value of using these features as integrating them improves the accuracy of our predictions. the whole features extraction process then looks like this: thirdly: predicting food security in areas where no surveys were conducted to make food security predictions in areas where no surveys have been conducted, we use the features extracted from the open data sources as covariates in a regularised linear model (“ridge regression”). at the same time, the models use the “k-nearest neighbor” information from the survey data to make a separate prediction about food security. these two predictions are then averaged in the final model. the output in the end is a gridded map of the chosen food security indicator with a score that reflects the validity of the model. we also aggregate the gridded surface into administrative areas to make results easier for our colleagues in the field to interpret. what have our results been so far? so far, we have demonstrated the validity of the method and its data sources for a variety of datasets, indicators and geographies. the automated pipeline also makes these tools easy to use. the results are promising, however, we still have some work to do to make sure that the app is reliable. in the future, we want to make it easy for other developers to help us improve the app so we want our code (or parts of it) to be easy to use for wfp and the wider community. that’s why we’ll be working on improving the method, restructuring and documenting the code and trying to make deployment easier. we’re also looking into finding a better proxy for poverty than nightlights, improving the underlying models and the pre-processing of the data sources, and refining the methodology as we process more datasets. stay tuned for more updates or meet us at the foss4g conference 2018 , where we will be presenting our findings so far. and get in touch if you want to contribute or test! and for more (techy) details on our humanitarian high resolution mapping project, please visit: [ link to our github page ] [ link to our github repo ] . tweet follow @mobilevam “the world is wider and more diverse than i ever imagined” june 14, 2018 / mvamblog data scientist lorenzo riches works in wfp

URL analysis for mvam.org

http://mvam.org/2018/07/24/vam-talks-episode-16-the-72-hour-emergency-assessment-approach/
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http://mvam.org/2016/04/20/introducing-mkengala/
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http://mvam.org/2016/10/17/going-mobile-in-afghanistan/
http://mvam.org/2018/07/20/mapping-our-way-towards-zero-hunger-2/
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http://mvam.org/online-course/
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http://mvam.org/author/mvamblog/
http://mvam.org/2018/02/27/hello-wfp-speaking/
http://mvam.org/2018/04/30/i-am-excited-about-numbers-and-the-difference-they-can-make/

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Domain Name: MVAM.ORG
Registry Domain ID: D177660014-LROR
Registrar WHOIS Server: http://api.fastdomain.com/cgi/whois
Registrar URL: http://www.fastdomain.com
Updated Date: 2017-10-16T14:47:21Z
Creation Date: 2015-10-16T14:47:04Z
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Domain Status: clientTransferProhibited https://icann.org/epp#clientTransferProhibited
Registrant Organization: The Endurance International Group, Inc.
Registrant State/Province: Massachusetts
Registrant Country: US
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Name Server: NS2.BLUEHOST.COM
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  REFERRER http://www.pir.org/

  REGISTRAR Public Interest Registry

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OWNER

  ORGANIZATION The Endurance International Group, Inc.

ADDRESS

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  REGISTERED yes

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