The GreenUr project: creating an application in QGIS to manage the impacts of urban green spaces on human health
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| Number of Parts | 351 | |
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| License | CC Attribution 3.0 Unported: 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. | |
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Transcript: English(auto-generated)
00:01
Thank you very much Vero for the introduction. Good morning everyone. It's early yet but still we start. I would like to present to you the Green UR project which is a project which has been requested by the WHO European Centre for Environment and Health in Bonn and is funded
00:21
by the German Ministry of Environment and Climate and the Clean Air Coalition. What we do here we are investigating the impact of land use on health and especially focus on green spaces. You know urban green spaces like parks are known to be beneficial
00:46
to human health but is sometimes quite interesting to better understand in which detail level of detail at city level how to deal with that. So what we have been doing we have been developing a QGIS plugin which is still under development but
01:04
these are early results we wanted to present at the occasion of this conference here and well it's QGIS as already mentioned but underneath there's also the use of GRASS GIS so in this particular case we are using both the APIs of QGIS, Python APIs of QGIS and GRASS
01:25
in order to do the computation. I will walk you through different aspects and yeah we will start with that. So the question is in the first place where does it all come from our knowledge. We are relying on scientific literature here. This project is basically the
01:45
idea to convert scientific literature into code and so we have been looking at different articles this has been done by the scientific team at WHO especially and our co-authors and we were
02:00
looking into the connections between green space especially urban green space and human health and you see here some citations like an inverse association between the proximity to green space and all course mortality means the more close you are the less mortality you find statistically with a consistent negative association between urban green spaces
02:26
exposure and mortality heart rate and violence and a positive association with attention mood and physical activity. I'm citing this because there are citations so I cannot modify that and eventually it's important that green space can benefit health because there are three
02:45
general functions reducing harm restoring capacities and building capacities. So we are looking particularly at cardiovascular diseases that is for example hypertension, ischemia, heart disease. We're looking at mental diseases like anxiety, stress, depression and eventually
03:05
at metabolic diseases another family that is diabetes or obesity or respiratory diseases like allergic greenitis. So there's a lot of work still to be done but there's a growing corpus of scientific literature in this regard which is looking at the causal relationships between
03:26
nature and health and well there's a meanwhile agreement between biopsychosocial petapathways linking green space to health that is related to these three functions mentioned below.
03:44
Okay so this is in a nutshell the foundation of everything. Since I told you we are developing a QGIS plugin I will now show the different aspects of this plugin what you can do. You are initially presented with so let's say overall it's a wizard yeah you are walked through
04:08
the different aspects and you can go back and forth you have some settings in the beginning where your data are and then which what do you want to compute and here you can see a choice of different things like effect on air pollution that is particulate matter for example effects
04:25
of noise, effects of temperature, effects on pollen and a tree module as well and this is under the section ecosystem services. There are others in this with that which I will show partially later. We're looking at health impact of mortality under development this is still
04:44
really under development morbidity on mental health, dementia, reduced depression and stress and stroke. This is of course pretty complex to analyze and we are using environmental data and then try to turn them into something related to these aspects and eventually there's active
05:02
transport that means cycling and so on which is also a topic here. So concerning the the morbidity on mental health this is let's say a start on for the discussion. Now the question is how
05:21
much green space is available one of those questions for the population of a specific city. So I want to give some example here this is a Maastricht city example data have been provided by Bram Oosterbroek from Maastricht university and in the first place as always in a GIS you
05:40
define your study area and feed it with input data. So in this particular case you have to we use a vector map with the different districts of Maastricht and you upload this data through the or let's say import the data or register them through this wizard you pick the
06:00
data set and then in the lower part you have also a population map which is needed and here you can either bring your own or we are also to some extent providing sample data because it's not obvious for everybody to to get all these kinds of data which we see here and later. You know there are different sources existing but things also have to be
06:23
combined properly. So the first question is where is currently green space and how to get that there are different options one option is to use satellite data or aerial data with infrared channels so you can compute the normalized differences vegetation index
06:42
that NDVI that is kind of hello world of remote sensing and then you can define a threshold because it also depends a bit on where you are with this threshold we say okay everything above this threshold is green and everything below is not because the range of NDVI is from
07:00
theoretically minus one to plus one with minus one not really existing but say small negative values are like water for example and then you have streets and covered soil and so on and then at some point the green vegetation starts with a maximum of plus one. So you use your threshold you can also naturally compare it since it's in QGIS to other
07:26
services like WMS you can put into the background and so on another option is to take data from open street map parks and gardens in case they are already digitized and like this you can compute the green space area of this particular city and here in the case of Maastricht
07:48
we get this amount of these results here so it's not that complex of course and we move to the next now we look at the population importantly it depends on where
08:02
people live population map are usually of course a resolution so we need to generate the association to the different districts here and this is nothing else in a technical sense like zonal statistics and like that you also get a respective map so why is population important
08:24
because we want to have the reachability to urban green space where are most of the people living for example where is the next green space what are the distances and then we come into this context here so reachability and which amount of population has an has access to green
08:42
space so another topic is to compute how many deaths could be prevented due to the proximity to green spaces here you can say okay wow this is already a quite quite a wild assumption but as mentioned before we are looking at the scientifically described correlation between green
09:04
spaces on mortality there's again some citations here which i will not read to you but later on at the end of the presentation you have all the links to the different articles if you're interested importantly the first paper here is a systematic review and a meta-analysis
09:27
of longitudinal studies so that is not a single study here but it is looking at the entire corpus of scientific literature in this regard and then derived different results here all right so with
09:42
that we move on all course mortality so we use again our input data already taken and the mean ndvi so with ndvi you must imagine if you're in an area with seasonality like here in on the northern hemisphere central europe wherever we are you have winter sum and so on and ndvi
10:02
is naturally not constant so you can use for example different ndvi data sets over time and then average that out and then on this on top of this we compute the all-course mortality so the first step you can see here and then we can look at the population data again and buffer the
10:27
green space so the question is what is the reachability and up there you see a number maybe i have a mouse here yes um 300 meters so people tend to be lazy at some point they will not
10:40
walk easily one and a half kilometer to go to the next park and we try to say here possibly 300 meters are doable it depends on where you are what temperatures are and so forth but that's why you can modify this value and then go and simulate another another situation
11:02
okay and going on with that um what are prevented premature mortality cases this is then computed here there are different statistics computed as well there's a help um also integrated so what you do you have seen we have this without style and here you can
11:24
can click on run analysis and look at the results you need to interpret them of course you can also go back modify the parameters and go forth again to to define what you have and mortality data you're natural of course also need in this case we have 900 cases per
11:44
thousand and this value is then inserted here so the idea is have to have it all modifiable because this is a tool which aims at global usage naturally you need to have the data but the data are statistically existing and then you look up for this particular place
12:03
in the world what the respective data are so temperature for some of us it's pretty warm in florence and this is as you know giving with heat waves and so forth giving quite an impact this can also be studied here and trees and green spaces are known to have cooling effects
12:25
on the respective surrounding areas where they are located and we put this also into code in order to see the effect so here we need a land use map we are still in the
12:41
Maastricht case and we wanted to compute from that the urban heat island effect so with soil being sealed you can see it pretty much also in this particular city where we are in Florence there's a lot of stone and so on it's heating up in daytime and in night time this effect continues because it's a slow change of temperature this may differ from where you are
13:05
but we use a land use map to then turn this into something which is related to effects on temperature so the land use maps here are a road map paved surfaces so impervious surfaces and buildings and if you have those data you can activate them and use
13:25
them and then we naturally need some weather data they can be defined here as a csv table for example and you define which columns contain for example wind speed the daily rural temperature outside global radiation and so on those data do exist and we
13:44
of course help finding them and then it depends on which resolution you have how that goes so weather data look like this you have the table pretty simple you have the date you have the different parameters which are here and then
14:02
you simply tell okay this table this column goes here this column goes there and then you can go on with the computation you can compute here the monthly mean temperature we have also a radius for soil sealing and so on different parameters and with this then you get
14:22
the statistical effects okay and then eventually once we have prepared all our data the next point is to compute again the prevented premature mortality cases and you walk through this wizard and get the respective results and here you can also
14:46
include the expected temperature change so do two degree of celsius for example and then go and simulate what it means so if you want to compute something how would the city look like or what are the effects in 20 years from now with the current configuration but two degrees of
15:03
increase of temperature or you say okay 1.5 goal point degree of celsius degree we will make you put 1.5 there and can simulate so we have plenty of more functions like that there's not the time to go through that as i mentioned before as a summary we have this
15:28
developed this or we are developing this qgs plugin it's a better version we plan to publish it very soon it's already in a hidden space but let's say we need to have the
15:40
authorization by who to make it really public it will come but it's a big organization and this takes a bit of time and of course since this is then while computing quite important parameters like health related data we also want to be sure that the outcome is sufficiently correct
16:01
so correct in a sense that it is scientifically based we need to do more use cases in order to be sure that it the computation we have properly in different parts of the world and um well through this we offer a standalone tool in a well-known open source gis
16:23
with methods for investigation on the relationship between green spaces and health impact it is fairly easy then to uh well to use it in different places we are already creating packages for different cities or capitals like katmandu akra in gana and so on this will be open data
16:43
of course and so we want to simplify things and if you then work in a particular city but have one data set which is of better resolution or quality then you can naturally as you have seen before replaces replace it and use it so we want to release this plugin officially in
17:03
next year maybe we will have better testers if you are interested please let us know we can discuss everything we will implement more methods and we have already tested it in some workshops internal workshops workshops of who the response was quite well and we
17:23
tend we will be completing things here again the funding acknowledgement um and eventually just to tell you these are the data references the data we have been using for this mass trick case so you see also global data are used here from european commission
17:42
human global human settlement layers some are local so you can combine things as needed and here are the references of the papers which have been used in this study i thank you for your attention the slides will be available once i get access to the keyboard again i guess
18:03
later today on this website and i thank you for your attention
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