Using new technologies to build data systems

videoType

dynamic-media

videoDmUrl

keyFrameImage

timestamp

00:28 We are looking at remote sensing data,

00:31 satellite data.

00:33 We are using artificial intelligence to develop algorithms to understand

00:38 where things happen and when

00:40 to actually evaluate

00:42 poverty levels across space.

00:57 We are able to identify,

00:58 scrape the webs for tweets and identify where

01:03 accidents happen and what the characteristics of those accidents are.

01:07 We are gaining the collaborations of the private sector

01:10 like Uber and Waze weather data to be able

01:14 to understand weather conditions and traffic conditions,

01:18 accidents and other impediments to urban mobility.

01:36 The Smart Tras project has 3 main components.

01:40 The first one is

01:41 putting together and collecting the data necessary to look

01:45 at road safety and road traffic crashes in Nairobi.

01:49 The second is using the data

01:50 to analyze and identify.

01:53 The most prevalent areas,

01:54 the black spots where crashes are happening

01:56 and the characteristics leading to crashes,

01:58 and the third is working with policymakers

02:01 on testing policies in those specific black spots

02:04 that are meant to help in improving road safety.

02:20 The tools we're developing are meant to be ones that can be used

02:24 across other settings for collecting data.

02:26 The machine learning algorithms we've developed

02:28 can be adapted

02:30 to other city settings where there may be Twitter accounts that

02:33 are similarly talking about road traffic crashes or traffic more broadly

02:38 or also even using platforms like Facebook or

02:40 other crowdsourcing platforms where we can again adapt

02:44 the machine learning algorithm to be able to

02:46 identify crashes and their location from that.

showAllTimestamps

no

transcript

We are looking at remote sensing data, satellite data. We are using artificial intelligence to develop algorithms to understand where things happen and when to actually evaluate poverty levels across space. We are able to identify, scrape the webs for tweets and identify where accidents happen and what the characteristics of those accidents are. We are gaining the collaborations of the private sector like Uber and Waze weather data to be able to understand weather conditions and traffic conditions, accidents and other impediments to urban mobility. The Smart Tras project has 3 main components. The first one is putting together and collecting the data necessary to look at road safety and road traffic crashes in Nairobi. The second is using the data to analyze and identify. The most prevalent areas, the black spots where crashes are happening and the characteristics leading to crashes, and the third is working with policymakers on testing policies in those specific black spots that are meant to help in improving road safety. The tools we're developing are meant to be ones that can be used across other settings for collecting data. The machine learning algorithms we've developed can be adapted to other city settings where there may be Twitter accounts that are similarly talking about road traffic crashes or traffic more broadly or also even using platforms like Facebook or other crowdsourcing platforms where we can again adapt the machine learning algorithm to be able to identify crashes and their location from that.

showAllTranscripts

no

duration

PT2M53S

scene7File

worldbank/Using new technologies to build data systems 02

scene7Domain

scene7FileAvs

worldbank/Using new technologies to build data systems 02-AVS

title

Using new technologies to build data systems 02

description

Using new technologies to build data systems 02

showTimestampAndTranscript

yes