Spatial data analysis for gender policy lobbying
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Formal Metadata
| Title |
| |
| Title of Series | ||
| Number of Parts | 295 | |
| Author | ||
| Contributors | ||
| License | CC Attribution 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/43347 (DOI) | |
| Publisher | ||
| Release Date | ||
| Language |
Content Metadata
| Subject Area | ||
| Genre | ||
| Abstract |
| |
| Keywords |
00:00
Time zoneDatabase normalizationRevision controlGenderProjective planeData analysisEstimatorQuicksortLevel (video gaming)Social engineering (security)Normal (geometry)Real numberContext awarenessSpacetimeForcing (mathematics)Observational studyWorkstation <Musikinstrument>Mathematical analysisMereologyInsertion lossMultiplication signCentralizer and normalizerOffice suiteComputer animation
02:49
Data analysisInformationDisintegrationSpacetimeSphereOpen setExpert systemFeedbackCivil engineeringComputer networkSystem on a chipExplosionMenu (computing)Price indexStatisticsAlgebraRange (statistics)Block (periodic table)Visual systemPoint (geometry)Lipschitz-StetigkeitResultantPrice indexProcess (computing)Information securityCountingNetwork topologyBlock (periodic table)Time zoneSpacetimeMoment (mathematics)TwitterHypermediaWaveProduct (business)Digital photographyHand fanRow (database)Intrusion detection systemGroup actionVolume (thermodynamics)Projective planeProfil (magazine)Element (mathematics)Electronic mailing listFocus (optics)Type theoryEccentricity (mathematics)Different (Kate Ryan album)Graph coloringWebsiteFlow separationMountain passArithmetic progressionOpen setStatisticsWorkstation <Musikinstrument>Closed setGradientQuicksortMereologyThermal conductivityField (computer science)GoogolVideo gameObject (grammar)Disk read-and-write headRule of inferenceLogicSocial classTable (information)Offenes KommunikationssystemGoodness of fitLevel (video gaming)Computer programmingSubject indexingChannel capacityRange (statistics)Web 2.0Google Street ViewVisualization (computer graphics)Term (mathematics)Traffic reportingMappingCausalityMathematical analysis
12:04
AngleTerm (mathematics)Channel capacityGroup actionMobile appMereologyGoodness of fitTerm (mathematics)Object (grammar)Level (video gaming)Physical systemReal numberINTEGRALProcess (computing)Group actionResultantSpacetimeGenderVideo gameAssociative propertyBus (computing)Web 2.0Procedural programmingWorkstation <Musikinstrument>Programming paradigmDefault (computer science)Flow separationBit rateSoftware developerLocal GroupPlanningLocal ringMobile WebMappingIntegrated development environmentTransportation theory (mathematics)BlogSoftwarePoint (geometry)WeightMultiplication signElement (mathematics)Archaeological field surveyTwitterMathematicsInformationChainDean numberPartial derivativeInformation securityWebsiteUsabilityAreaArithmetic progressionMedical imagingHeat transferMetropolitan area networkLecture/Conference
21:19
Lecture/Conference
Transcript: English(auto-generated)
00:09
Okay, I keep it short, it's not my part, it's about Selin and her analysis, spatial data analysis for gender policy lobbying, I think in Mexico if I'm correct, so the
00:25
floor is yours and yeah, let's see what is cool. Thank you. So hello, I'm Selin, I'm French but I live in Mexico, I studied there and I'm working there too, and this is a sort of tool between projects I developed as a
00:46
civic data activism about gender. I will give some context because it's a real concern in Mexico in this moment, and maybe in all the country's history, the insecurity that leaves women in Mexico City and in the country, and the
01:07
level of normalization people consider this issue. So actually the UN estimate that's nine women and girls are killed every day in the country, so that's
01:23
really huge, and four thousand and seventeen feminists from in the first semester of this year, and only in the Mexico City almost three thousand women are victims of sexual abuse and assault in the same semester. The
01:44
other problem is that we have very few data from the government to study the problem, so it's an incomplete data and an update and bad quality data because one of the first problem is that sexual violence is not well typified, so when women report that in the police offices, generally they
02:07
suffer violence there from the officials and crime is not well typified. So at the end we don't have data and we have to organize by society to create the data,
02:20
to analyze the data and to try to change the normalization there is about this topic in the country. So in the last month the city had a very large manifestation about that, after a violation by policemen to a young
02:40
lady, and that was a pretext to work more on the topic and to create a dialogue with the government of the city. So what I am doing in this topic, in general I have several projects where I try to promote civic data
03:00
collection. I work with several communities, so in OpenStreetMap community, in Geotica's community and all sorts of activism that have a relationship with the city, so pro-pedestrians, collectives, cyclists collectives, etc, etc. Where I try to teach them how to analyze data
03:27
and how to use this analysis ingestion with the government to achieve their causes. So through these projects I try to promote report culture
03:41
because a main part of the civic data can be collected through social media so that they exist if people report. So that's the main point. So why why urban data? One of the main space and time where women are totally
04:05
vulnerable is what we called in urban planning the first and the last miles. So it's a little space when people go out of their their house and walk maybe to take a transport to go to the transport station or just walk to
04:20
their activities or just take a taxi or just take their own car. So this little space can be a little space or a large space. It's a space where people are exposed to everything and security and specifically women are exposed there. So I also want to make the public
04:44
understand that when we are talking about women we are talking about every type, every profile of women, every age with different difficulties, different needs and in terms of public space. So we have elderly women, we have women
05:02
with this capacity, we have children, we have young ladies etc etc and the insecurity has to be understood in a very large way. So part of the exercise are about creating the data in the streets. So we make through an
05:22
exercise called purple streets, a walking focus group where we define a little tour in a specific zone of the city with users of its own. So it can be inhabitants, it can be students, employees of this zone and we try to
05:43
have a large range of profile in the group, profiles of women and we observe the space, we talk about our feeling in this space, our fears in this space and with what we say we make audio mapping and a photo mapping with
06:00
mapillary. We create the data, we have the data on the map and this map help us to have an assessment material to after that work with the local government through the collective or association. So I just give the methodological support for that and I empower, it's the objective at the list,
06:24
I empower the collective to have them doing this suggestion with government. And the audio mapping or at least the facts to talk a lot about what we feel helps a woman to explain, to express what they feel and helps a woman to
06:40
understand what they feel because it's very common that we are not very conscious of what we feel, of our fears, of our conduct in the space. Small micro conducts for example changing the way we talk, we walk in the streets etc depending of any element of our surrounding and it allows to
07:07
classify and to hierarchize elements that impact on our experience. So with these indicators, at the end they are indicators where we are able to work
07:22
more quantitatively. So this is another exercise with people with this capacity and disabilities where also we walk, we observe how we walk, all the difficulties due to the physical space and we take, we document these
07:41
problems with photo mapping, with mapillary. And we try to have people doing that reporting in social media in their daily life. So it's not just in the moment of the exercise, we try to teach a behavior,
08:05
a reporting behavior. So this is a live visualization of the products, it's very pixelized but this is audio mapping, we did that with Osment and we
08:21
are in process to upload that on a web map and this is a map on mapillary. As we use the same user account in mapillary, if we look for, if we filter in mapillary with the account of Calles Violetas, it's in Mexico, we have all the points documented by this exercise with women. So after that with
08:47
the benefits of the indicators we were able to create thanks to these exercises. The point is not to develop the exercise in the whole city, in the
09:01
with several climates, several types of cities, big cities, small cities, we can have indicators and with these indicators about all types of urban elements, the idea is to create an audit instrument. So this is part of a project I work with the NGO where I was working, WRI, World Resource Institute
09:27
with the World Bank, where we developed a whole audit instrument. So all the indicators we create with Calles Violetas can be put in a table and we
09:41
can weight every indicator we observe, we just observed in the public space, walking again or using Street View photos, so it can be with Google but preferably with mapillary, we qualified any photo and we are able to make zones using a grid in the city. So I'm quite finishing, so with
10:11
with that we can use this data, we can also use data from OpenStreetMap, so observing the type of elements detected through the others exercise and
10:23
these elements existing in OpenStreetMap and also with other sources, for example in Mexico we have a good National Institute of Geography and Statistics that describe all the urban zones, so we know how is every block in terms of mobility, we know where are some trees, some different elements of the
10:49
streets and I created a special index to try to qualify every
11:01
part of the grid in Mexico City. Ideally it can be developed actually at the national level because all the data exists, if we use OpenStreetMap and the National Institute data, so it is different weighted for pedestrian, for women pedestrian and for women pedestrian by night, considering that the
11:25
woman pedestrian by night is the most vulnerable person, so if the space is qualified as secure or insecure for her by night, we can consider is secure or insecure for everyone. So this is part of the result, it's still in
11:42
progress, so it's not totally adjusted, I have to integrate more elements, this is by day, this is for women by day, so the yellow color is bad and green color is good, so we can see that for women we have less medium values
12:01
and more and stronger bad values and this is by night, so the good values lose weight and the bad value gain. So at the end it's a work in progress, we are still developing it, the objective with that is as I said
12:24
working with local governments, I have yet worked with local governments with in Mexico we have an institution called the Women Institute that's worked on gender violence, health and several topic and they tried now to develop
12:46
this methodology in several cities with groups of women, it's a way also to integrate local groups of women and to have to gain consciousness in the space and to integrate. The second objective is to have citizen
13:04
involvement in a long-term effort because I try to have to have people tweet information, use mapillary to also share in Twitter some specific elements of the space they consider in their daily life is insecure etc.
13:23
and it's about to teach also to public institution what is a way to have real citizen participation and that's a demagogique one where we just make some survey to legitimize some action and this more slow and permanent
13:41
effort is about a real citizen participation and at the end it also help governments to start to manage civil data because one of the typical idea in Mexico at least is that only the public official data have a good
14:02
value but it's very bad built so it really don't have a good value and it's time to change the paradigm and help them to change their paradigm to integrate more sources, big data and civic data from social networks.
14:24
That's it. Thank you. Thank you very much Céline. Are there any questions?
14:43
Thank you for the talk. I actually was wondering have you documented your procedures somewhere and are they available because at least in Brussels I know of associations who would be very interested by this kind of approach as well.
15:01
In part I mean this is part of an activism so it's in my free time so it's hard but yeah in part I try to have the more optimists way to document and so I have a blog I have a blog I update the blog with any new exercise any new conclusion and it's a more quick is a
15:24
quickest way to do that but it's not enough I know so I would have to do more I can you could follow GioChica's maybe network I think I would put the news if I do that yeah. When you collect information from
15:55
women do you use any app or any other environment to collect information
16:02
directly from women? Yeah we work in the street with Mapillary. It could be OpenStreetCam too but I always use Mapillary so it's easier. It's also easier for people to upload the data. It's more friendly I think and
16:22
Osmand for the audio mapping. Osmand is not very friendly actually but it's part of an exercise I organize so I use the app. I make the audio and I put it in a web map so this is in process. Yeah for audio map I don't know if you know
16:41
some app more friendly please tell me. Thank you. Thank you. Other questions? Oh just a question on have you had any success with the government in terms of in local government with actually implementing
17:03
any changes like just adding a new streetlight or for example? One of the experiences we made with Women Institute in Mexico City was about integrating the results in redesigning local plans or in the delegation we call delegation in
17:25
a little part of the city. So the government of this delegation wanted to integrate to the results to define which space is exactly which which elements change to change first. Now it was a question of
17:41
characterization. It's a point it's also a point not exactly with this exercise but more related to cyclists mobility so with gender but not totally. Another city a medium city called Morelia defined cyclists plan at city level
18:07
to help people to move say more safer yeah sorry for my English. Yeah do you know what your rate of accuracy is in terms of safety at the end of the day
18:23
you're just walking around and looking at things and thinking this feels safe this doesn't feel safe so is there any way you know what you've done is good and second question do you have any success stories following what you did so far success stories as in okay so first part it is still under
18:50
development so I am still adjusting the criteria actually I think I have to put more data on land use and I did it on the city I know and it's my way
19:03
to evaluate if it's accurate it can't be totally accurate because I choose partial data opens with map is on perfect but so and also the public official data is very general so it has defects default but for now it's a
19:27
way I have to evaluate so the idea is also to make some group working with results and evaluating where they live it's a question of perception a lot I want I want people to be able to express perception and not only facts
19:42
so that's the point of working with audios to to have totally in-depth information just not facts and I don't have a success story because it's a process it's a beginning of a process so when you talk about the first and the
20:03
last night it made me think about the fact that in some cities now you can request when you get the night bus you can request a stop between two stops so you can have you work less to your destination because of safety so I was wondering if you tried to link your data with maybe public
20:24
transportation data so you could improve or at least find suggestions to improve the night buses or something like that in Mexico 80% of the transport system is totally informal so there is no stop station yeah we have the metro we
20:43
have the metro bus and that's all of them the rest of the transport system is actually no yeah it's it's a problem for urban planning good thank you
21:04
questions then maybe we can hear from you next year and the year after when when you have more data and good luck thanks yeah