SPINLab · DewWes · Sep 9, 2020
diff --git a/py1_LuchtmeetnetAPI/README.md b/py1_LuchtmeetnetAPI/README.md
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 # Practical Assignment 1 - py1 Air Quality - API 
 
 ## Introduction
-A recent study of SPINlab researcher [Dr. Bo Andrée](https://research.vu.nl/en/persons/bo-pieter-johannes-andree) showed a relationship between air pollution and confirmed COVID-19 cases. He concluded that a high level of Particulate Matter with a diameter of 2.5 μm or less per m3 is a highly significant predictor for the number of confirmed COVID-19 cases and related hospital admissions. Why this is the case is still unclear. The two most accepted theories are however that either people living in areas with high levels of air pollution already have lung damage due to the pollution, or that the virus latches on aerosols in the air which speeds up the spread of the virus. Either way are the results of this study important in controlling the spread of the virus. It shows that more and or different measures might have to be taken to protect people that live in areas with high levels of air pollution.
+A recent study of SPINlab researcher [Dr. Bo Andrée](https://research.vu.nl/en/persons/bo-pieter-johannes-andree) showed a relationship between air pollution and confirmed COVID-19 cases. He concluded that a high level of Particulate Matter (PM) with a diameter of 2.5 μm or less per m3 is a highly significant predictor for the number of confirmed COVID-19 cases and related hospital admissions. Why this is the case is still unclear. The two most accepted theories are however that either people living in areas with high levels of air pollution already have lung damage due to the pollution, or that the virus latches on aerosols in the air which speeds up the spread of the virus. Either way are the results of this study important in controlling the spread of the virus. It shows that more and or different measures might have to be taken to protect people that live in areas with high levels of air pollution.
 
 The results of this study even made it to the national [news](https://spinlab.vu.nl/relation-between-air-pollution-and-confirmed-covid-19-cases-researched/).
 
@@ -14,7 +14,7 @@ Both layers are the result of interpolated data points from measuring stations.
 In this practical assignment it is your task to collect data from air measuring stations and to see how these correspond with the modelled dataset used by Andrée. For this you are going to use the luchtmeetnet - Air measurement network. Luchtmeetnet is hosted by the Netherlands National Institute for Public Health and the Environment (or Rijksinstituut voor Volksgezondheid en Milieu (RIVM)) and contains information about air quality.
 
 ## About APIs 
-Before we start let us first tell you a bit more about the data. Data gets made available in different ways. Sometimes, for example, you simply go to a website and click a download link, sometimes you need to [scrape it off a website](https://realpython.com/python-web-scraping-practical-introduction/), and sometimes you can use an [API (application programming interface)](https://en.wikipedia.org/wiki/Application_programming_interface). An API is a set of definitions and methods for different computer software to communicate with each other. It is a general term, but in the context of data acquisition an API makes it easier to retrieve data in an automatic way. To make use of this automation we need a way to tell the computer how to use the API. This can be done using various tools and/or programming languages. We will use python to this end, since it is a very accessible and flexible programming language, with a lot of modules available able to provide a broad range of extra functionality. Python is also the programming language used by GIS packages such as ArcGIS (arcpy) and QGIS.
+Before we start let us first tell you a bit more about the data. Data is made available in different ways. Sometimes, for example, you simply go to a website and click a download link, sometimes you need to [scrape it off a website](https://realpython.com/python-web-scraping-practical-introduction/), and sometimes you can use an [API (application programming interface)](https://en.wikipedia.org/wiki/Application_programming_interface). An API is a set of definitions and methods for different kinds of computer software to communicate with each other. It is a general term, but in the context of data acquisition an API makes it easier to retrieve data in an automatic way. To make use of this automation we need a way to tell the computer how to use the API. This can be done using various tools and/or programming languages. We will use python to this end, since it is a very accessible and flexible programming language, with a lot of modules available able to provide a broad range of extra functionality. Python is also the programming language used by GIS packages such as ArcGIS (arcpy) and QGIS.
 
 As a data source for this practical assignment we are going to use the the Luchtmeetnet (air measurements network) API published by the RIVM. The luchtmeetnet contains measurements of different chemical compounds in the air and can be accessed here: 
 
@@ -84,20 +84,20 @@ print(response_dictionary['userId'])
 For this practical assignment we have prepared two exercises which will help you to understand which steps you will need to take to extract data from luchtmeetnet in order to reproduce the map for the average level of particulate matter per m3 in 2017. However, before you open them we first want you to go to the [Luchtmeetnet API documentation](https://documenter.getpostman.com/view/1562017/RVnbBxf9) and explore the API (what directories are there? What data do they return? Which options do they have?) Think of which url we can use to retrieve a list of measurement locations (stations) and how to retrieve information about a station.
 
 ## Excercise 1: Basic Info
-Once you explored the API open [1_basic_info.py](https://github.com/SPINLab/GI_Minor_2020/blob/master/py1_LuchtmeetnetAPI/1_basic_info.py) in spyder and go trough the different steps. This script will allow you to retrieve basic information from the API. The file contains pointers and hints to help you. But remember, coding can be very frustrating. Most of the time you are trying different approaches to find out that you were on the wrong path. In the end it will be very rewarding to finally get what you we looking for. 
+Once you explored the API open [1_basic_info.py](https://github.com/SPINLab/GI_Minor_2020/blob/master/py1_LuchtmeetnetAPI/1_basic_info.py) in spyder and go trough the different steps. This script will allow you to retrieve basic information from the API. The file contains pointers and hints to help you. But remember, coding can be very frustrating. Most of the time you are trying different approaches to find out that you were on the wrong path. In the end it will be very rewarding to finally get what you were looking for. 
 
 ## Excercise 2: Timeseries data
-Now that you understand how to get basic information from the Luchtmeetnet API we are going to obtain data from a specific time period. Have another look at the [Luchtmeetnet API documentation](https://documenter.getpostman.com/view/1562017/RVnbBxf9) and try to determine which url we could use to request measurement data of a single station? Which parameters would we have to set? Now choose a location (for example the one in Amsterdam of the previous exercise) and retrieve the location number. Next, open [2_timeseries.py](https://github.com/SPINLab/GI_Minor_2020/blob/master/py1_LuchtmeetnetAPI/2_timeseries.py) and create a python script which retrieves measurement data of a station and plot the data.
+Now that you understand how to get basic information from the Luchtmeetnet API we are going to obtain data from a specific time period. Have another look at the [Luchtmeetnet API documentation](https://documenter.getpostman.com/view/1562017/RVnbBxf9) and try to determine which url we could use to request measurement data of a single station. Which parameters would we have to set? Now choose a location (for example the one in Amsterdam of the previous exercise) and retrieve the location number. Next, open [2_timeseries.py](https://github.com/SPINLab/GI_Minor_2020/blob/master/py1_LuchtmeetnetAPI/2_timeseries.py) and create a python script which retrieves measurement data of a station and plot the data.
 
 ## Practical Assignment 
-With the two exercises completed you are now ready for the practical assignment. to extract the data from the luchtmeetnet API which can be used as input to reproduce the map for the average level of particulate matter of 2.5 μm and 10 μm or less per m3 in 2017. Note, that we do not ask you to interpolate the data. We just want you to extract the different point datasets. 
+With the two exercises completed you are now ready for the practical assignment. To extract the data from the luchtmeetnet API which can be used as input to reproduce the map for the average level of particulate matter of 2.5 μm and 10 μm or less per m3 in 2017. Note, that we do not ask you to interpolate the data. We just want you to extract the different point datasets. 
 
 Again have a look at the [Luchtmeetnet API documentation](https://documenter.getpostman.com/view/1562017/RVnbBxf9) and determine which data we need to request to make a map of all stations colored by the measurement of a particulate matter of 2.5 μm and 10 μm or less per m3. Now open Practical_assignment_1.py and create a python script which retrieves the average measurement data of PM 2.5 and PM 10 for 2017 for all of the Netherlands.
 
 The script you have to produce must result in a shapefile `measurements.shp`containing the average PM 2.5 and PM10 measurements for 2017.
 
 Once you finished the script go to https://www.atlasleefomgeving.nl/kaarten and download the PM 10 and PM 2.5 map data used by Andrée. Add the shape files you created and the downloaded data in a GIS (ArcGIS pro or QGIS) and systematically compare the data you retrieved from the luchtmeetnet API. Answer the following: 
-   1. Which funtionalities in GIS have you used to compare the data point with the modelled raster layer?
+   1. Which funtionalities in GIS have you used to compare the data point layer with the modelled raster layer?
    2. Do the measurements from the Luchtmeetnet correspond with the datalayer PM 10 and PM 2.5 from the atlas van de leefomgeving?
    3. Do you think that the measurements from the Luchtmeetnet are sufficient as input for to generate the maps layers? 
    4. Image that Andrée would have used the data from the Luchtmeetnet directly in the way you did. How do you think that would have changed his analysis?