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OpenHyPE: OpenHygrisC Data Processing for Education


0. Abstract

The State Agency for Nature, Environment and Consumer Protection (LANUV) of the federal state of North Rhine-Westphalia (NRW) provides extensive quantitative and qualitative groundwater monitoring data. This contributes to the fulfillment of the European Water Framework Directive as well as the EU INSPIRE Directive for an open interoperable spatial data infrastructure.

NRW operates its own water-related data portal ELWAS-WEB, which also provides the groundwater database HygrisC. ELWAS and HygrisC are not easy to use and provide only limited exploratory data analysis capabilities to the public.

However, the state publishes much of its groundwater data as an open data archive called OpenHygrisC, which contains several data tables in csv format. In particular, the big data in the measurement table, which contains all time series with more than 3.6 million individual measurements (table rows), and the table with the spatial coordinates of the groundwater monitoring wells require the use of a spatially enabled object-relational database management system (Spatial ODBRMS) and extensive data engineering before insertion into the database.

The goal of the OpenHyPE project is to develop a first set of Open Educational Resources (OER) to train the setup, filling and use of a geospatial-temporal database with the OpenHygrisC data. Due to the graduated level of difficulty, the project addresses students from secondary schools as well as universities in the state of North Rhine-Westphalia and beyond.

All software products used are Free and Open Source Software (FOSS). The database, which we call OpenHyPE DB, is based on PostgreSQL / PostGIS and establishes the center of the system for environmental data analysis and presentation. The OER demonstates how the geographic information system QGIS as well as Python programs in the JupyterLab development environment interoperate with the OpenHyPE DB to select, analyze and display the data in the form of time dependent maps or time series. We use Python and Jupyter from the Anaconda distribution.

The start-up funding for the OpenHyPE project is used to raise awareness of the NRW's valuable open environmental data collection among young people as well as to contribute to interdisciplinary STEM promotion in general and education for sustainable development (ESD) in particular by linking environmental science and computer science.

1. Introduction

1.1 Problem Description

The state of North Rhine-Westphalia (NRW) operates comprehensive and professional measurement networks for the collection of environmental data through the LANUV. As part of Open.NRW and driven by the INSPIRE Directive of the European Union as well as other directives such as the EU Water Framework Directive (WFD), the state of NRW makes extensive data products openly accessible and freely usable on various platforms (Free and Open Data).

The state of NRW is a pioneer in Germany in providing open and (cost)free geodata. These data are a real treasure and form the basis for potentially massive knowledge gains in the field of environmental and nature conservation. Nevertheless, it seems that only a comparatively small group of people really uses this potential. Therefore, the OpenHyPE project has set itself the task of integrating this data stock into university teaching and developing corresponding freely accessible teaching material that can be used not only by students but also, to some extent, by pupils to learn the basics of environmental data processing. The start-up funding will be used to implement the first steps of developing such training material.

We follow the paradigm of “problem based learning”: the necessary knowledge and skills are identified and taught based on a concrete socially relevant problem. The solution of the problem identified as significant is the motivation for learning.

At the beginning we want to develop the material on the basis of the problem area “groundwater protection”. The Ministry for Environment, Agriculture, Nature and Consumer Protection NRW (MULNV) operates its own water-related data portal called ELWAS-WEB via the “Landesbetrieb Information und Technik Nordrhein-Westfalen” (IT.NRW). Data from the statewide groundwater database HygrisC are also held in this portal. ELWAS and HygrisC offer limited exploratory data analysis capabilities to outsiders. From the point of view of usability engineering, which deals with the user-friendliness of technical systems, improvements are desirable with regard to usability as well as data analysis possibilities, because exploratory data analysis and data mining in particular help to identify structures and relationships between data. ELWAS and HygrisC are therefore only suitable to a limited extent for teaching the basics of environmental data analysis, but they can be used in the classroom as supporting material.

On the portal OpenGeodata.NRW extensive data with spatial reference - also called geodata - are made available, which often have a time reference, such as land use changes or measurement data series on water quality. Excerpts of the HygrisC groundwater database of the state of NRW, published under the name OpenHygrisC, are also located there. These groundwater data can ideally serve as a basis for building one's own environmental database, which the learners can use to learn about concepts of data management and data analysis.

1.2 Project goals

The following components are to be realized:

  • Development of OpenHyPE geodatabase based on PostgreSQL/PostGIS
    to manage spatial and temporal data on groundwater quality and quantity.
  • Problem-related free online course material (OER), tutorials, video tutorials, instructions, program code,
    using Free and Open Source Software (FOSS):
    • Introduction to the State Agency for Nature, Environmental and Consumer Protection (LANUV).
    • Introduction to groundwater protection
    • Introduction to the Geographic Information System QGIS
    • Introduction to the relational database PostgreSQL and the query language SQL
    • Introduction to the geodatabase extension PostGIS
    • Introduction to the processing of geodata with the programming language Python
    • Installation of the OpenHyPE database management system
    • Discussion of the data model and upload of the OpenHygrisC data of the LANUV
    • Automatic creation of diagrams on time series of water quality
    • Automatic generation of groundwater chemistry maps
    • Creating simple dashboards with interactive online graphs and maps
    • Introduction to data mining (descriptive statistics, searching for correlations)

2. Implementation

2.1 Data flow

Image 1- Data flow

2.2 PostgreSQL/PostGIS

PostgreSQL, also known as Postgres, is a free and open-source relational database management system, and according to the official website of pgadmin, “Postgres Database is the most advanced open-source database in the world.” In Postgres, we can store both time series and geometry data. In this project, we have used PGadmin, which is the most popular and powerful open-source administration platform for Postgres databases.

PostGIS: PostGIS is technically an extension of the PostgreSQL database, which helps add support for geographical objects. PostGIS is open-source and free to use.

The below image shows the PGadmin tool.

image 6- PGadmin

Watching the below videos to understand how we can create schemas and tables in the PostgreSQL database.

video1- Create Schema in PGadmin video2- Create tables in PGadmin

Create a database and schema based on the above video.

2.3 Data Engineering

2.3.1 Downloading the data

In the first step, The data must be downloaded from here. The first zip file should be downloaded and extracted which consists of four CSV files and one instruction. image 2 shows which zip file should be downloaded.

image 2- Download the data

After extracting the above zip file then we can consider the four CSV files and instruction file which is highly recommended to read first.

2.3.2 Python

Python is an object-oriented programming language that helps the programmer to write logic and clear code for small as well as large projects.

Python is used in several ways such as:

  • AI and machine learning
  • Data analytics
  • Data visualisation
  • Programming applications

In this project, we have used Python for data engineering, data pre-processing, and data analysis. Jupyter Notebook is used in this project to write Python codes. The Jupyter Notebook is an open-source web application that data scientists can simply write the code for and make it easier to document. Simply, we can combine Python codes, text, images, comments, and the result of the codes on the same page. The below image shows how code, text, and the result of the code can be seen on a single page.

image 3- combination of code and text in the jupyter notebook

2.3.3 Anaconda

Anaconda is an open-source distribution for python and R. It is used for data science, machine learning, deep learning, etc. With the availability of more than 300 libraries for data science, it becomes fairly optimal for any programmer to work on anaconda for data science Anaconda is used in this project. An environment on Anaconda has been created to install all the packages needed for this project.

VideoXXX- How to install Anaconda on Windows
VideoXXX- How to install Anaconda on Mac OS X

Environment in Anaconda: A conda environment is a directory that contains a specific collection of conda packages that are used in the project.

How to create Conda environment: The below video shows how to create a Conda environment, how to activate it, how to install different packages on the environment and how to deactivate the environment.

VideoXXX- How to create Conda environment

Python packages: a package is a collection of modules that have the same aim together. These modules are like functions and can help us to write code easily. The packages which are needed should be installed and imported before use.

To get more details about conda environment, I highly recommend visiting the below webpage.

openhype environment: In this project, “openhype” environment has been created in order to install all the necessary packages which are needed. openhype environment has been created based on the above video.

Several packages are related to data science but in this project, we have used the below packages. There are two ways to install the below packages:

install a package manually: In this way you need to install each package manually into openhype environment by the command prompt.

  • pandas: pandas is one of the most important and popular packages of python among Data scientists for data manipulation and analysis. We can do Data cleaning, Data pre-processing, fill the data, visualize the data, Data inspection, Loading and saving data and much more.

In this project, we have used pandas, to read the CSV files, clean the data and do data engineering. The below code should be written into the openhype environment on Anaconda prompt

conda install pandas
  • sqlalchemy: SQLAlchemy package is like a bridge between python programming language and database. we have used this package to connect to our database.
conda install sqlalchemy
  • psycopg2: With the help of this package, our python program can communicate with the PostgreSQL database.
conda install psycopg2
  • geopandas: With the help of this package we are able to work with geospatial data in Python. according to the geopandas website “ It

is extends the data types used by pandas to allow spatial operations on geometric types”.

conda install --channel conda-forge geopandas

Some packages need to specify the channel to install and that's why in the above code we have specified the channel.

conda install jupyter notebook

All the above packages must be installed into the openhype environment with Anaconda prompt.

Load all the packages into the environment by a yml file: In this way, you need to create an environment (which in our project is Openhype) and load a yml file which consists of the all packages that are needed for this project. The below image shows the content of our openhype.yml file.

image- openhype.yml

With the below code, we are able to create an environment based on the yml file. Please be aware that this yml file should be in the same directory as our anaconda directory, otherwise you need to write the full path of the file. openhype.yml can be found from here

conda env create -f openhype.yml

How to create a yml file for the environment: With the below code you can export the yml file from the existing environment.

conda env export > openhype.yml

Since we have downloaded the four CSV files in the previous chapter, now is the time to read our CSV files and start to clean them in order to make them ready to import into our database.

please refer to our Notebook section of the code on Github (the below image).

image 5- Github

There below four notebooks should be run separately, in order to import data into the database.

  • import_gemeinde.ipynb:

In this notebook, we will import the data of all geminde into the database.

  • import_katalog_stoff.ipynb:

In this notebook, we will import the data of all the catalogue substances into the database.

  • import_messstelle.ipynb:

In this notebook, we will import the data of all stations into the database.

  • import_messwert.ipynb:

In this notebook, we will import the data of all values into the database.

2.4 Observation Data in the Database

In the previous section, we downloaded the data, cleaned and imported them to the database successfully and now it's time to see the data in the database. as we know, the SQL command is valid in the PostgresSQL database, as a result of that, we will run some basic SQL commands to see the data.

First, we want to see our tables, with the below code, we are selecting all the columns (* means all the columns) from our schema which is consist of our table (in this case is sina) and with the name of the table. and then because the size of the table is huge and we want only to see the first 100 rows then we just limit it to 100.

select * from sina.messwert limit 100;

The below image (image 7) shows the result of the above command in PGadmin.

image 7- messwert table in databse

Now with the above SQL command, we are able to see the others three tables that we have (The below codes).

select * from sina.messstelle limit 100;
select * from sina.katalog_stoff limit 100;
select * from sina.katalog_ge limit 100;

Now we want to see more details for our tables and we will run the below codes.

Count the rows of each table: with the below code, we can see how many rows we have in each table.

select count (*) from sina.messwert;
select count (*) from sina.messstelle;

Filter the data based on Nitrate only: In this case first we need to find out what the substance number of the Nitrate is. With below code, we can find

select * from sina.katalog_stoff where name like 'Ni%';
Image 8- Find out the substance number (stoff_nr) of Nitrate

As we can see in image 8, the substance number (stoff_nr) of Nitrate is 1244

Filter the data based on Nitrate only:

select * from sina.messwert where stoff_nr = '1244';

Now we can filter the messwert table based on Nitrate. In this step we need to somehow save the above table. We can save this new table as a new “view”.

What is views: A view is a database object that is of a stored query. A view can be accessed as a virtual table in PostgreSQL. It means that we can do whatever we are able to do with views same as tables. The below code create views for us:

create view sina.nitrat as (select * from sina.messwert where stoff_nr = '1244');

Now we have “nitrat” view which simply can call same as tables with the below code. This view is filter of our messwert table based on “1244” which is “Nitrate”

select * from sina.nitrat ;

Group by the two tables: In this section we want to group by our two tables (messwert and messstelle) only in Nitrate. These two tables have a column messstelle_id which simply means station id.

select messstelle_id,  count(*) from sina.messwert where stoff_nr = '1244' group by messstelle_id;
Image 9- Group by the two tables (messwert and messstelle)

Image 9 shows that each station id has how many single measurements for the Nitrate only. I highly recommend opening the below website to get more deep into how “group by” works and how we can use it.

Video 3- How group by works

Maximum date in nitrat table:

select * from sina.nitrat where datum_pn = (select max(datum_pn) from sina.nitrat);
Image 10- Maximum of the date in the nitrat table

As we can see in Image 10, the maximum date is 2021-08-17

Minimum date in nitrat table:

select * from sina.nitrat where datum_pn = (select min(datum_pn) from sina.nitrat);
Image 11- Minimum of the date in the nitrat table

As we can see in Image 11, the minimum date is 1951-04-30

Create geometry column in messstelle table: In this section, we want to create a geometry column from e32 and n32 columns from the messstelle table. with the below code, we are able to create a new column and we set the name as geom

ALTER TABLE sina.messstelle ADD COLUMN geom geometry(Point, 25832);
UPDATE sina.messstelle SET geom = ST_SetSRID(ST_MakePoint(e32, n32), 25832);
Image 12- Geom column

Now the messstelle table has one more column (geom) which consists of the geometry information of the location of each station.

Merge two tables:

In this section, we want to merge the two tables (messwert and messstelle) based on the same column which is “messstelle_id”. We need to select columns that we need from each tables and then merge them based on the “messstelle_id”

select t1."messstelle_id", t1."name", t1.geom, t2."stoff_nr", t2."messergebnis_c", t2."masseinheit", 
t2."datum_pn", t2."messergebnis_cm" from  sina.messstelle t1 , sina.nitrat t2 
where t1."messstelle_id" = t2."messstelle_id";
Image 13- Merge tables

Now we need to create a view and save this SQL command as a new view. the name of this new view is “nitrat_geom”.

create view sina.nitrat_geom as (select t1."messstelle_id", t1."name", t1.geom, t2."stoff_nr", t2."messergebnis_c", t2."masseinheit", 
 t2."datum_pn", t2."messergebnis_cm" from  sina.messstelle t1 , sina.nitrat t2 
where t1."messstelle_id" = t2."messstelle_id")

We need this new view for the next section in QGIS.

2.5 QGIS

QGIS is an open-source and free application that can support viewing, editing and analysis of geospatial data.

You can download QGIS for free from the below link.

The below video shows how to download and install QGIS for windows which are highly recommended to watch before installing it.

Video 4- How to download and install QGIS

Now is the time to get to know about QGIS and the below video can help so much.

Video 5- Get to know QGIS

Create a time series video: In this section, we want to create a time series video to see how the nitrate concentration has been changed over time in North Rhine-Westphalia which is the most crowded state in Germany. First, we need to download the shapefile of the North Rhine-Westphalia state and load it into QGIS.

We will download three shapefiles,

  • Whole state shapefile (dvg1bld_nw.shp)
  • kreis shapefile (dvg1krs_nw.shp)
  • Gemeinde shapefile (dvg1gem_nw.shp)

All the three shapefiles can be downloaded from here. After downloading, we need to load them to the QGIS to see them. The below video shows how to load the shapefile in QGIS.

Video 6- How to import shapefile in QGIS

Now we can see the map of NRW, kreis and Gemeinde. There are two options to create a video for time series.

Locally with shapefile: In here, we need to have a shapefile that consists of the nitrate concentration over time. download the notebook from here and run the python codes to create two shapefiles. then we should load these two shapefile to the QGIS. The first shapefile is consist of all stations in NRW and the second one is consist of the nitrate concentration.

The below video shows how we can load shapefiles to QGIS.

Video 7- How to load shapefile in QGIS

Connect to Database: The below video shows how we can connect our QGIS to Database and load the file.

Video 8- How to connect database to QGIS and load the files

3 Dashboard

This section will discuss how we can create an interactive dashboard for our data. An interactive dashboard is a tool that users can interrelate with data by analyzing, visualizing as well as monitoring the data.

Two approaches to creating a dashboard will be discussed in this section. The aim of this dashboard is a simple interactive dashboard in which users with no knowledge of programming can easily consider the data as well as a map. This kind of dashboard will help users to understand data better. Nowadays dashboards are widely used in several ways to help managers and decision-makers to make decision easier. one good example of such a kind of dashboard is the Covid-19 dashboard which each country also here in Germany people are widely used. The Covid-19 Dashboard aids people in noticing how many new cases and how many new deaths have been recorded in different periods of time.

In our case, we want to create a simple dashboard which shows the map of NRW as well as Nitrate and Sulfate concentration rates at different times.

3.1 Plotly Dash:

Plotly: Plotly is a computing company located in Montreal, Canada. They develop online data analytics and visualization tools. Plotly offers online graphing, analytics, and statistics tools for their users, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, and REST. Plotly offers several open-source and enterprise products such as Dash which have been used for creating simple and interactive dashboards in this project.

Dash: Dash is a framework to build data apps rapidly not only in Python but also in R, Julia, and F#. According to Plotly official website, Dash is downloaded 800,000 times per month which shows that nowadays Dash getting more popular. Dash is a great framework for anyone who uses data with a customised user interface. Through a couple of simple patterns, Dash eliminated all of the technologies as well as protocols that are needed to make a full-stack web app with interactive data considerations. Another good feature is that Dash is running on web browsers so it means that no other application needs to run it.

If you would like to know more about Dashboard with Plotly Dash, click the link below. In the below link, you will find full tutorials about how to create a simple dashboard with Plotly Dash.

Dash is also offering some dashboards examples which could be really nice and helpful to get ideas. | Click here for Dash gallery

All the source codes of the dash gallery are available in | here

3.2 Panel:

4. Result

Nitrate concentration 2000-2010

The below video has shown the concentration of nitrate in NRW from 2000 to 2010. The video is created with QGIS 3.16

Nitrate concentration 2010-2020

The below video has shown the concentration of nitrate in NRW from 2010 to 2020. The video is created with QGIS 3.16

Sulfat concentration 2000-2010

The below video has shown the concentration of sulfate in NRW from 2000 to 2010. The video is created with QGIS 3.16

Sulfat concentration 2010-2020

The below video has shown the concentration of sulfate in NRW from 2010 to 2020. The video is created with QGIS 3.16

5. Project codes

All the codes are available in the below link.

Click here for project codes

Weitere Infos

eolab/openhype/start.txt · Last modified: 2023/01/21 19:34 by rolf.becker