Differences

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--- eolab:openhype:start [2023/05/21 11:28] – [2.5 QGIS] sina001
+++ eolab:openhype:start [2024/03/15 17:21] (current) – [2.1 Data Flow] rolf.becker
@@ Line 111: / Line 111: @@
-|  {{ :eolab:openhype:data_flow.png?600 |}}  |
+|  {{ :eolab:openhype:data_flow.png?600&direct |}}  |
 |  // Image 1- Data flow//               |
@@ Line 145: / Line 145: @@
 ===== 2.3 Data Engineering =====
-===== 2.3.1 Downloading the data =====
+===== 2.3.1 Downloading the Data =====
 In the first step, The data must be downloaded from [[ https://www.opengeodata.nrw.de/produkte/umwelt_klima/wasser/grundwasser/hygrisc/ |here]].
@@ Line 495: / Line 495: @@
-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.
+In this section, the process of creating an interactive dashboard for our data will be explored. An interactive dashboard is a versatile tool that allows data to be interacted with, analyzed, visualized, and key information to be monitored by users.
-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.
+This section discusses two approaches to creating a dashboard. The objective of this dashboard is to provide a user-friendly and interactive interface for data exploration and visualization, accessible even to non-programmers. Such a dashboard plays a crucial role in enhancing data comprehension and is widely utilized by managers and decision-makers to facilitate informed decision-making processes.
-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.
+ One notable example of this type of dashboard is the Covid-19 dashboard, which has gained widespread usage worldwide, including in Germany. The Covid-19 dashboard provides users with valuable insights into the number of new cases and deaths reported over various time periods. It helps individuals track the progression of the pandemic and understand the impact it has had on different regions and countries.
-** 3.1 Plotly Dash: **
+In our specific case, the objective is to develop a straightforward dashboard that showcases the map of North Rhine-Westphalia (NRW) alongside the concentration rates of Nitrate and Sulfate at different time intervals. This dashboard will provide a visual representation of the spatial distribution of these pollutants and enable users to observe any temporal variations in their concentrations within NRW.
+** 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.
+** Dash: ** Dash is a framework to build data apps rapidly not only in Python but also in R, Julia, and F#. According to Plotly's 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.
+To learn more about creating a dashboard with Plotly Dash, you can follow the link provided below. This resource contains comprehensive tutorials that guide you through the process of building a simple dashboard using Plotly Dash. These tutorials will provide you with step-by-step instructions and examples to help you create interactive and visually appealing dashboards using Plotly Dash.
@@ Line 517: / Line 519: @@
 All the source codes of the dash gallery are available in [[ https://github.com/plotly/dash-sample-apps/|| here]]
+** Dashboard Design: **
+I have developed a web application dashboard that effectively visualizes time series data for Nitrate and Sulfate. The image below shows the main page of our dashboard.
+|  {{:eolab:openhype:main_page.jpg?600|}}  |
+|  // Image 14- Main page of the dashboard //  |
+In order to run the above dashboard on your local system, you need to do the following:
+[[ https://github.com/sina7272/LANUV_Groundwater |Click here and pull the repository ]]
+direct to the "Dashboard" folder, then all the Python codes and SQL queries are available to run the application.
+There is a yml file inside the folder which name is "dashboard_environment". This yml file will create an environment with all the necessary packages which you will need to run the application. The first step needs to load this file to the Anaconda, if you are not sure how to do that please refer to the 2.3.3 Anaconda section.
+The next step is to add the credentials (Username, DB name, password and ...) of the database to the "credential_temp" file.
+And the last step is to run the "app.py" inside the new environment that you have already created with the help of "dashboard_environment.yml"
-** 3.2 Panel: **
 ===== 4. Result=====
@@ Line 525: / Line 547: @@
 ==== Nitrate concentration 2000-2010 ====
-The below video has shown the concentration of nitrate in NRW from 2000 to 2010.
+The video below shows the concentration of nitrate in North Rhine-Westphalia (NRW) from 2000 to 2010. This visualization was created using QGIS 3.16. By watching the video, you can observe the temporal changes in nitrate levels across NRW during the specified time period.
-The video is created with QGIS 3.16
 {{ youtube>IMznT88l2VQ?medium }}
@@ Line 534: / Line 555: @@
 ==== Nitrate concentration 2010-2020 ====
-The below video has shown the concentration of nitrate in NRW from 2010 to 2020.
+The video below shows the concentration of nitrate in NRW from 2010 to 2020.
-The video is created with QGIS 3.16
+The video was created with QGIS 3.16
@@ Line 546: / Line 567: @@
 ==== 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
+The video below shows the concentration of sulfate in NRW  from 2000 to 2010. The video was created with QGIS 3.16
 {{ youtube>Jz2cINIOwWs?medium }}
@@ Line 553: / Line 574: @@
 ==== 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
+The video below shows the concentration of sulfate in NRW from 2010 to 2020. The video is created with QGIS 3.16
 {{ youtube>0SqfLi2lPH8?medium }}