1. Introduction

Visualizations are graphical representations of data that allow you to communicate, in a simple and effective way, the information linked to it. The visualization possibilities are very extensive, from basic representations such as line graphs, bar graphs or relevant metrics, to visualizations configured on interactive dashboards.

In the section of “Step-by-step visualizations” we are periodically presenting practical exercises making use of open data available in datos.gob.es or other similar catalogs. They address and describe in a simple way the steps necessary to obtain the data, carry out the transformations and analyses that are pertinent to finally obtain conclusions as a summary of this information.

In each of these hands-on exercises, conveniently documented code developments are used, as well as free-to-use tools. All generated material is available for reuse in the datos.gob.es GitHub repository.

Accede al repositorio del laboratorio de datos en Github.

Ejecuta el código de pre-procesamiento de datos sobre Google Colab.

 

2. Objetive

The main objective of this exercise is to show how to carry out, in a didactic way, a predictive analysis of time series based on open data on electricity consumption in the city of Barcelona. To do this, we will carry out an exploratory analysis of the data, define and validate the predictive model, and finally generate the predictions together with their corresponding graphs and visualizations.

Predictive time series analytics are statistical and machine learning techniques used to forecast future values in datasets that are collected over time. These predictions are based on historical patterns and trends identified in the time series, with their primary purpose being to anticipate changes and events based on past data.

The initial open dataset consists of records from 2019 to 2022 inclusive, on the other hand, the predictions will be made for the year 2023, for which we do not have real data.

Once the analysis has been carried out, we will be able to answer questions such as the following:

• What is the future prediction of electricity consumption?
• How accurate has the model been with the prediction of already known data?
• Which days will have maximum and minimum consumption based on future predictions?
• Which months will have a maximum and minimum average consumption according to future predictions?

These and many other questions can be solved through the visualizations obtained in the analysis, which will show the information in an orderly and easy-to-interpret way.

 

3. Resources

3.1. Datasets

The open datasets used contain information on electricity consumption in the city of Barcelona in recent years. The information they provide is the consumption in (MWh) broken down by day, economic sector, zip code and time slot.

These open datasets are published by Barcelona City Council in the datos.gob.es catalogue, through files that collect the records on an annual basis. It should be noted that the publisher updates these datasets with new records frequently, so we have used only the data provided from 2019 to 2022 inclusive.

• Series anuales de consumo eléctrico (MWh) en la ciudad de Barcelona.

These datasets are also available for download from the following  Github repository.

 

3.2. Tools

To carry out the analysis, the Python programming language written on a Jupyter Notebook hosted in the Google Colab cloud service has been used.

"Google Colab" or, also called Google Colaboratory, is a cloud service from Google Research that allows you to program, execute and share code written in Python or R on top of a Jupyter Notebook from your browser, so it requires no configuration. This service is free of charge.

The Looker Studio tool was used to create the interactive visualizations.

"Looker Studio", formerly known as Google Data Studio, is an online tool that allows you to make interactive visualizations that can be inserted into websites or exported as files.

If you want to know more about tools that can help you in data processing and visualization, you can refer to the "Data processing and visualization tools" report.

 

 

4. Predictive time series analysis

Predictive time series analysis is a technique that uses historical data to predict future values of a variable that changes over time. Time series is data that is collected at regular intervals, such as days, weeks, months, or years. It is not the purpose of this exercise to explain in detail the characteristics of time series, as we focus on briefly explaining the prediction model. However, if you want to know more about it, you can consult the following manual.

This type of analysis assumes that the future values of a variable will be correlated with historical values. Using statistical and machine learning techniques, patterns in historical data can be identified and used to predict future values.

The predictive analysis carried out in the exercise has been divided into five phases; data preparation, exploratory data analysis, model training, model validation, and prediction of future values), which will be explained in the following sections.

The processes described below are developed and commented on  in the following Notebook executable from Google Colab along with the source code that is available in our Github account.  

It is advisable to run the Notebook with the code at the same time as reading the post, since both didactic resources are complementary in future explanations.

 

4.1 Data preparation

This section can be found in point 1 of the Notebook.

In this section, the open datasets described in the previous points that we will use in the exercise are imported,  paying special attention to obtaining them and validating their content, ensuring that they are in the appropriate and consistent format for processing and that they do not contain errors that could condition future steps.

 

4.2 Exploratory Data Analysis (EDA)

This section can be found in point 2 of the Notebook.

In this section we will carry out an exploratory data analysis (EDA), in order to properly interpret the source data, detect anomalies, missing data, errors or outliers that could affect the quality of subsequent processes and results.

Then, in the following interactive visualization, you will be able to inspect the data table with the historical consumption values generated in the previous point, being able to filter by specific period. In this way, we can visually understand the main information in the data series.

 

Once you have inspected the interactive visualization of the time series, you will have observed several values that could potentially be considered outliers, as shown in the figure below. We can also numerically calculate these outliers, as shown in the notebook.

Figure 1. Time Series Outliers with Historical Data

 

Once the outliers have been evaluated, for this year it has been decided to modify only the one registered on the date "2022-12-05". To do this, the value will be replaced by the average of the value recorded the previous day and the day after.

The reason for not eliminating the rest of the outliers is because they are values recorded on consecutive days, so it is assumed that they are correct values affected by external variables that are beyond the scope of the exercise. Once the problem detected with the outliers has been solved, this will be the time series of data that we will use in the following sections.

Figure 2. Time series of historical data after outliers have been processed.

 

If you want to know more about these processes, you can refer to the Practical Guide to Introduction to Exploratory Data Analysis.

 

4.3 Model training

This section can be found in point 3 of the Notebook.

First, we create within the data table the temporal attributes (year, month, day of the week, and quarter). These attributes are categorical variables that help ensure that the model is able to accurately capture the unique characteristics and patterns of these variables. Through the following box plot visualizations, we can see their relevance within the time series values.

 

Figure 3. Box Diagrams of Generated Temporal Attributes

 

We can observe certain patterns in the charts above, such as the following:

• Weekdays (Monday to Friday) have a higher consumption than on weekends.
• The year with the lowest consumption values is 2020, which we understand is due to the reduction in service and industrial activity during the pandemic.
• The month with the highest consumption is July, which is understandable due to the use of air conditioners.
• The second quarter is the one with the lowest consumption values, with April standing out as the month with the lowest values.

Next, we divide the data table into training set and validation set.  The training set is used to train the model, i.e., the model learns to predict the value of the target variable from that set, while the validation set is used to evaluate the performance of the model, i.e., the model is evaluated against the data from that set to determine its ability to predict the new values.

This splitting of the data is important to avoid overfitting,  with the typical proportion of the data used for the training set being 70% and the validation set being approximately 30%. For this exercise we have decided to generate the training set with the data between "01-01-2019" to "01-10-2021", and the validation set with those between  "01-10-2021" and "31-12-2022" as we can see in the following graph.

Figure 4. Historical data time series divided into training set and validation set

 

For this type of exercise, we have to use some regression algorithm. There are several models and libraries that can be used for time series prediction. In this exercise we will use  the "Gradient Boosting" model, a supervised regression model that is a machine learning algorithm used to predict a continuous value based on the training of a dataset containing known values for the target variable (in our example the variable "value") and the values of the independent variables (in our exercise the temporal attributes).

It is based on decision trees and uses a technique called  "boosting" to  improve the accuracy of the model, being known for its efficiency and ability to handle a variety of regression and classification problems.

Its main advantages are the high degree of accuracy, robustness and flexibility, while some of its disadvantages are its sensitivity to outliers and that it requires careful optimization of parameters.

We will use the supervised regression model offered in the XGBBoost library, which can be adjusted with the following parameters:

• n_estimators: A parameter that affects the performance of the model by indicating the number of trees used. A larger number of trees generally results in a more accurate model, but it can also take more time to train.
• early_stopping_rounds: A parameter that controls the number of training rounds that will run before the model stops if performance in the validation set does not improve.
• learning_rate: Controls the learning speed of the model. A higher value will make the model learn faster, but it can lead to overfitting.
• max_depth: Control the maximum depth of trees in the forest. A higher value can provide a more accurate model, but it can also lead to overfitting.
• min_child_weight: Control the minimum weight of a sheet. A higher value can help prevent overfitting.
• Gamma: Controls the amount of expected loss reduction needed to split a node. A higher value can help prevent overfitting.
• colsample_bytree: Controls the proportion of features that are used to build each tree. A higher value can help prevent overfitting.
• Subsample: Controls the proportion of the data that is used to construct each tree. A higher value can help prevent overfitting.

These parameters can be adjusted to improve model performance on a specific dataset. It's a good idea to experiment with different values of these parameters to find the value that provides the best performance in your dataset.

Finally, by means of a bar graph, we will visually observe the importance of each of the attributes during the training of the model. It can be used to identify the most important attributes in a dataset, which can be useful for model interpretation and feature selection.

Figure 5. Bar Chart with Importance of Temporal Attributes

4.4 Model training

This section can be found in point 4 of the Notebook.

Once the model has been trained, we will evaluate how accurate it is for the known values in the validation set.

We can visually evaluate the  model by plotting the time series with the known values along with the predictions made for the validation set as shown in the figure below.

Figure 6. Time series with validation set data next to prediction data.

 

We can also numerically evaluate the accuracy of the model using different metrics. In this exercise, we have chosen to use the mean absolute percentage error (ASM) metric, which has been 6.58%. The accuracy of the model is considered high or low depending on the context and expectations in such a model, generally an ASM is considered low when it is less than 5%, while it is considered high when it is greater than 10%. In this exercise, the result of the model validation can be considered an acceptable value.

If you want to consult other types of metrics to evaluate the accuracy of models applied to time series, you can consult the following link.

 

4.5 Predictions of future values

This section can be found in point 5 of the Notebook.

Once the model has been generated and its MAPE = 6.58% performance has been evaluated, we will apply this model to all known data, in order to predict the unknown electricity consumption values for 2023.

First of all, we retrain the model with the known values until the end of 2022, without dividing it into a training and validation set. Finally, we calculate future values for the year 2023.

Figure 7. Time series with historical data and prediction for 2023

 

In the following interactive visualization you can see the predicted values for the year 2023 along with their main metrics, being able to filter by time period.

 

Improving the results of predictive time series models is an important goal in data science and data analytics. Several strategies that can help improve the accuracy of the exercise model are the use of exogenous variables, the use of more historical data or generation of synthetic data, optimization of parameters, ...

Due to the informative nature of this exercise and to promote the understanding of less specialized readers, we have proposed to explain the exercise in a way that is as simple and didactic as possible. You may come up with many ways to optimize your predictive model to achieve better results, and we encourage you to do so!

 

5. Conclusions of the exercise

Once the exercise has been carried out, we can see different conclusions such as the following:

• The maximum values for consumption predictions in 2023 are given in the last half of July, exceeding values of 22,500,000 MWh
• The month with the highest consumption according to the predictions for 2023 will be July, while the month with the lowest average consumption will be November, with a percentage difference between the two of 25.24%
• The average daily consumption forecast for 2023 is 17,259,844 MWh, 1.46% lower than that recorded between 2019 and 2022.

We hope that this exercise has been useful for you to learn some common techniques in the study and analysis of open data. We'll be back to show you new reuses. See you soon!

The UNESCO (United Nations Educational, Scientific and Cultural Organization) is a United Nations agency whose purpose is to contribute to peace and security in the world through education, science, culture and communication. In order to achieve its objective, this organisation usually establishes guidelines and recommendations such as the one published on 5 July 2023 entitled 'Open data for AI: what now?'

In the aftermath of the COVID-19 pandemic the UNESCO highlights a number of lessons learned:  

1.  Policy frameworks and data governance models must be developed, supported by sufficient infrastructure, human resources and institutional capacities to address open data challenges, in order to be better prepared for pandemics and other global challenges.
2. The relationship between open data and AI needs to be further specified, including what characteristics of open data are necessary to make it "AI-Ready".
3. A data management, collaboration and sharing policy should be established for research, as well as for government institutions that hold or process health-related data, while ensuring data privacy through anonymisation and anonymisation data privacy should be ensured through anonymisation and anonymisation.
4. Government officials who handle data that are or may become relevant to pandemics may need training to recognise the importance of such data, as well as the imperative to share them.
5. As much high quality data as possible should be collected and collated. The data needs to come from a variety of credible sources, which, however, must also be ethical, i.e. it must not include data sets with biases and harmful content, and it must be collected only with consent and not in a privacy-invasive manner. In addition, pandemics are often rapidly evolving processes, so continuous updating of data is essential.
6. These data characteristics are especially mandatory for improving inadequate AI diagnostic and predictive tools in the future. Efforts are needed to convert the relevant data into a machine-readable format, which implies the preservation of the collected data, i.e. cleaning and labelling.
7. A wide range of pandemic-related data should be opened up,  adhering to the FAIR principles.
8. The target audience for pandemic-related open data includes research and academia, decision-makers in governments, the private sector for the development of relevant products, but also the public, all of whom should be informed about the available data.​
9. Pandemic-related open data initiatives should be institutionalised rather than ad hoc, and should therefore be put in place for future pandemic preparedness. These initiatives should also be inclusive and bring together different types of data producers and users.
10. The beneficial use of pandemic-related data for AI machine learning techniques should also be regulated to prevent misuse for the development of artificial pandemics, i.e. biological weapons, with the help of AI systems.

The UNESCO builds on these lessons learned to establish Recommendations on Open Science by facilitating data sharing, improving reproducibility and transparency, promoting data interoperability and standards, supporting data preservation and long-term access.

As we increasingly recognise the role of Artificial Intelligence (AI), the availability and accessibility of data is more crucial than ever, which is why UNESCO is conducting research in the field of AI to provide knowledge and practical solutions to foster digital transformation and build inclusive knowledge societies.

Open data is the main focus of these recommendations, as it is seen as a prerequisite for planning, decision-making and informed interventions. The report therefore argues that Member States must share data and information, ensuring transparency and accountability, as well as opportunities for anyone to make use of the data.

UNESCO provides a guide that aims to raise awareness of the value of open data and specifies concrete steps that Member States can take to open their data. These are practical, but high-level steps on how to open data, based on existing guidelines. Three phases are distinguished: preparation, data opening and follow-up for re-use and sustainability, and four steps are presented for each phase. 

It is important to note that several of the steps can be carried out simultaneously, i.e. not necessarily consecutively.

1. Develop a data management and sharing policy: A data management and sharing policy is an important prerequisite for opening up data, as such a policy defines the governments' commitment to share data. The Open Data Institute suggests the following elements of an open data policy:

• A definition of open data, a general statement of principles, an outline of the types of data and references to any relevant legislation, policy or other guidance.
• Governments are encouraged to adhere to the principle "as open as possible, as closed as necessary". If data cannot be opened for legal, privacy or other reasons, e.g. personal or sensitive data, this should be clearly explained. 

In addition, governments should also encourage researchers and the private sector in their countries to develop data management and sharing policies that adhere to the same principles.

2. Collect and collate high quality data: Existing data should be collected and stored in the same repository, e.g. from various government departments where it may have been stored in silos. Data must be accurate and not out of date. Furthermore, data should be comprehensive and should not, for example, neglect minorities or the informal economy. Data on individuals should be disaggregated where relevant, including by income, sex, age, race, ethnicity, migration status, disability and geographic location.
3. Develop open data capabilities:  These capacities address two groups:

• For civil servants, it includes understanding the benefits of open data by empowering and enabling the work that comes with open data.
• For potential users, it includes demonstrating the opportunities of open data, such as its re-use, and how to make informed decisions.

4. Prepare data for AI: If data is not only to be used by humans, but can also feed AI systems, it must meet a few more criteria to be AI-ready.

• The first step in this regard is to prepare the data in a machine-readable format.
• Some formats are more conducive to readability by artificial intelligence systems than others.
• Data must also be cleaned and labelled, which is often time-consuming and therefore costly. 

The success of an AI system depends on the quality of the training data, including its consistency and relevance. The required amount of training data is difficult to know in advance and must be controlled by performance checks. The data should cover all scenarios for which the AI system has been created.

1. Select the datasets to be opened: The first step in opening the data is to decide which datasets are to be opened. The criteria in favour of openness are:

• If there have been previous requests to open these data
• Whether other governments have opened up this data and whether this has led to beneficial uses of the data. 

Openness of data must not violate national laws, such as data privacy laws. 

2. Open the datasets legally: Before opening the datasets, the relevant government has to specify exactly under which conditions, if any, the data can be used. In publishing the data, governments may choose the license that best suits their objectives, such as the creative Commons and Open. To support the licence selection the European Commission makes available JLA - Compatibility Checkera tool that supports this decision
3. Open the datasets technically: The most common way to open the data is to publish it in electronic format for download on a website, and APIs must be in place for the consumption of this data, either by the government itself or by a third party.

Data should be presented in a format that allows for localisation, accessibility, interoperability and re-use, thus complying with the FAIR principles.

In addition, the data could also be published in a data archive or repository, which should be, according to the UNESCO Recommendation, supported and maintained by a well-established academic institution, learned society, government agency or other non-profit organisation dedicated to the common good that allows for open access, unrestricted distribution, interoperability and long-term digital archiving and preservation. 

4. Create a culture driven by open data: Experience has shown that, in addition to legal and technical openness of data, at least two other things need to be achieved to achieve an open data culture:

• Government departments are often not used to sharing data and it has been necessary to create a mindset and educate them to this end.
• Furthermore, data should, if possible, become the exclusive basis for decision-making; in other words, decisions should be based on data.
• In addition, cultural changes are required on the part of all staff involved, encouraging proactive disclosure of data, which can ensure that data is available even before it is requested. 

1. Support citizen participation: Once the data is open, it must be discoverable by potential users. This requires the development of an advocacy strategy, which may include announcing the openness of the data in open data communities and relevant social media channels. 

Another important activity is early consultation and engagement with potential users, who, in addition to being informed about open data, should be encouraged to use and re-use it and to stay involved.  

2. Supporting international engagement: International partnerships would further enhance the benefits of open data, for example through south-south and north-south collaboration. Particularly important are partnerships that support and build capacity for data reuse, whether using AI or not.
3. Support beneficial AI participation: Open data offers many opportunities for AI systems. To realise the full potential of data, developers need to be empowered to make use of it and develop AI systems accordingly.  At the same time, the abuse of open data for irresponsible and harmful AI applications must be avoided. A best practice is to keep a public record of what data AI systems have used and how they have used it.
4. Maintain high quality data: A lot of data quickly becomes obsolete. Therefore, datasets need to be updated on a regular basis. The step "Maintain high quality data" turns this guideline into a loop, as it links to the step "Collect and collate high quality data".

These guidelines serve as a call to action by UNESCO on the ethics of artificial intelligence.  Open data is a necessary prerequisite for monitoring and achieving sustainable development monitoring and achieving sustainable development. 

Due to the magnitude of the tasks, governments must not only embrace open data, but also create favourable conditions for beneficial AI engagement that creates new insights from open data for evidence-based decision-making. 

If UNESCO Member States follow these guidelines and open their data in a sustainable way, build capacity, as well as a culture driven by open data, we can achieve a world where data is not only more ethical, but where applications on this data are more accurate and beneficial to humanity.

References

https://www.unesco.org/en/articles/open-data-ai-what-now

Author : Ziesche, Soenke , ISBN : 978-92-3-100600-5

Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of its author.

The Open Data Maturity Study 2022 provides a snapshot of the level of development of policies promoting open data in countries, as well as an assessment of the expected impact of these policies. Among its findings, it highlights that measuring the impact of open data is a priority, but also a major challenge across Europe. 

In this edition, there has been a 7% decrease in the average maturity level in the impact dimension for EU27 countries, which coincides with the restructuring of the impact dimension indicators. However, it is not so much a decrease in the level of maturity, but a more accurate picture of the difficulty in assessing the resulting impact of reuse of open data difficulty in assessing the impact resulting from the re-use of open data. 

Therefore, in order to better understand how to make progress on the challenge of measuring the impact of open data, we have looked at existing best practices for measuring the impact of open data in Europe. To achieve this objective, we have worked with the data provided by the countries in their responses to the survey questionnaire and in particular with those of the eleven countries that have scored more than 500 points in the Impact dimension, regardless of their overall score and their position in the ranking: France, Ireland, Cyprus, Estonia and the Czech Republic scoring the maximum 600 points; and Poland, Spain, Italy, Denmark and Sweden scoring above 510 points. 

In the report we provide a country profile for each of the ten countries, analysing in general terms the country's performance in all dimensions of the study and in detail the different components of the impact dimension, summarising the practices that have led to its high score based on the analysis of the responses to the questionnaire. 

Through this tabbed structure the document allows for a direct comparison between country indicators and provides a detailed overview of best practices and challenges in the use of open data in terms of measuring impact through the following indicators: 

• "Strategic awareness": It quantifies the awareness and preparedness of countries to understand the level of reuse and impact of open data within their territory. 

• "Measuring reuse": It focuses on how countries measure open data re-use and what methods they use. 

•  "Impact created": It collects data on the impact created within four impact areas: government impact (formerly policy impact), social impact, environmental impact and economic impact. 

Finally, the report provides a comparative analysis of these countries and draws out a series of recommendations and good practices that aim to provide ideas on how to improve the impact of open data on each of the three indicators measured in the study. 

If you want to know more about the content of this report, you can watch the interview with its author interview with its author.

Below, you can download the full report, the executive summary and a presentation-summary.

Content prepared by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization.

The contents and views expressed in this publication are the sole responsibility of the author.

1. Introduction

Visualizations are graphical representations of data that allow the information linked to them to be communicated in a simple and effective way. The visualization possibilities are very wide, from basic representations, such as line, bar or sector graphs, to visualizations configured on interactive dashboards. 

In this "Step-by-Step Visualizations" section we are regularly presenting practical exercises of open data visualizations available in datos.gob.es or other similar catalogs. They address and describe in a simple way the stages necessary to obtain the data, perform the transformations and analyses that are relevant to, finally, enable the creation of interactive visualizations that allow us to obtain final conclusions as a summary of said information. In each of these practical exercises, simple and well-documented code developments are used, as well as tools that are free to use. All generated material is available for reuse in the GitHub Data Lab repository.

Then, and as a complement to the explanation that you will find below, you can access the code that we will use in the exercise and that we will explain and develop in the following sections of this post.

Access the data lab repository on Github.

Run the data pre-processing code on top of Google Colab.

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2. Objetive

The main objective of this exercise is to show how to perform a network or graph analysis based on open data on rental bicycle trips in the city of Madrid. To do this, we will perform a preprocessing of the data in order to obtain the tables that we will use next in the visualization generating tool, with which we will create the visualizations of the graph.

Network analysis are methods and tools for the study and interpretation of the relationships and connections between entities or interconnected nodes of a network, these entities being persons, sites, products, or organizations, among others. Network analysis seeks to discover patterns, identify communities, analyze influence, and determine the importance of nodes within the network. This is achieved by using specific algorithms and techniques to extract meaningful insights from network data.

Once the data has been analyzed using this visualization, we can answer questions such as the following: 

• What is the network station with the highest inbound and outbound traffic? 
• What are the most common interstation routes?
• What is the average number of connections between stations for each of them?
• What are the most interconnected stations within the network?

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3. Resources

3.1. Datasets

The open datasets used contain information on loan bike trips made in the city of Madrid. The information they provide is about the station of origin and destination, the time of the journey, the duration of the journey, the identifier of the bicycle, ...

These open datasets are published by the Madrid City Council, through files that collect the records on a monthly basis.

• Monthly series static data BICIMAD

These datasets are also available for download from the following Github repository.

 

3.2. Tools

To carry out the data preprocessing tasks, the Python programming language written on a Jupyter Notebook hosted in the Google Colab cloud service has been used.

"Google Colab" or, also called Google Colaboratory, is a cloud service from Google Research that allows you to program, execute and share code written in Python or R on a Jupyter Notebook from your browser, so it does not require configuration. This service is free of charge.

For the creation of the interactive visualization, the Gephi tool has been used.

"Gephi" is a network visualization and analysis tool. It allows you to represent and explore relationships between elements, such as nodes and links, in order to understand the structure and patterns of the network. The program requires download and is free.

If you want to know more about tools that can help you in the treatment and visualization of data, you can use the report "Data processing and visualization tools".

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4. Data processing or preparation

The processes that we describe below you will find them commented in the Notebook that you can also run from Google Colab.

Due to the high volume of trips recorded in the datasets, we defined the following starting points when analysing them:

• We will analyse the time of day with the highest travel traffic
• We will analyse the stations with a higher volume of trips

Before launching to analyse and build an effective visualization, we must carry out a prior treatment of the data, paying special attention to its obtaining and the validation of its content, making sure that they are in the appropriate and consistent format for processing and that they do not contain errors.

As a first step of the process, it is necessary to perform an exploratory analysis of the data (EDA), in order to properly interpret the starting data, detect anomalies, missing data or errors that could affect the quality of subsequent processes and results. If you want to know more about this process you can resort to the Practical Guide of Introduction to Exploratory Data Analysis

The next step is to generate the pre-processed data table that we will use to feed the network analysis tool (Gephi) that will visually help us understand the information. To do this, we will modify, filter and join the data according to our needs.

The steps followed in this data preprocessing, explained in this Google Colab Notebook, are as follows:

1. Installation of libraries and loading of datasets
2. Exploratory Data Analysis (EDA)
3. Generating pre-processed tables

You will be able to reproduce this analysis with the source code that is available in our GitHub account. The way to provide the code is through a document made on a Jupyter Notebook that, once loaded into the development environment, you can easily run or modify.

Due to the informative nature of this post and to favour the understanding of non-specialized readers, the code is not intended to be the most efficient but to facilitate its understanding, so you will possibly come up with many ways to optimize the proposed code to achieve similar purposes. We encourage you to do so!

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5. Network analysis

5.1. Definition of the network

The analysed network is formed by the trips between different bicycle stations in the city of Madrid, having as main information of each of the registered trips the station of origin (called "source") and the destination station (called "target").

The network consists of 253 nodes (stations) and 3012 edges (interactions between stations). It is a directed graph, because the interactions are bidirectional and weighted, because each edge between the nodes has an associated numerical value called "weight" which in this case corresponds to the number of trips made between both stations.

5.2. Loading the pre-processed table in to Gephi

Using the "import spreadsheet" option on the file tab, we import the previously pre-processed data table in CSV format. Gephi will detect what type of data is being loaded, so we will use the default predefined parameters.

 

Figure 1. Uploading data to Gephi

 

 

5.3. Network display options

5.3.1 Distribution window

First, we apply in the distribution window, the Force Atlas 2 algorithm. This algorithm uses the technique of node repulsion depending on the degree of connection in such a way that the sparsely connected nodes are separated from those with a greater force of attraction to each other.

To prevent the related components from being out of the main view, we set the value of the parameter "Severity in Tuning" to a value of 10 and to avoid that the nodes are piled up, we check the option "Dissuade Hubs" and "Avoid overlap".

 

Figure 2. Distribution window - Force Atlas 2 overlap

 

Dentro de la ventana de distribución, también aplicamos el algoritmo de Expansión con la finalidad de que los nodos no se encuentren tan juntos entre sí mismos.

Figure 3. Distribution window - Expansion algorithm

5.3.2 Appearance window

Next, in the appearance window, we modify the nodes and their labels so that their size is not equal but depends on the value of the degree of each node (nodes with a higher degree, larger visual size). We will also modify the colour of the nodes so that the larger ones are a more striking colour than the smaller ones. In the same appearance window we modify the edges, in this case we have opted for a unitary colour for all of them, since by default the size is according to the weight of each of them.

A higher degree in one of the nodes implies a greater number of stations connected to that node, while a greater weight of the edges implies a greater number of trips for each connection.

Figure 4. Appearance window

5.3.3 Graph window

Finally, in the lower area of the interface of the graph window, we have several options such as activating / deactivating the button to show the labels of the different nodes, adapting the size of the edges in order to make the visualization cleaner, modify the font of the labels, ...

Figure 5. Options graph window

 

Next, we can see the visualization of the graph that represents the network once the visualization options mentioned in the previous points have been applied.

Figure 6. Graph display

 

Activating the option to display labels and placing the cursor on one of the nodes, the links that correspond to the node and the rest of the nodes that are linked to the chosen one through these links will be displayed.

Next, we can visualize the nodes and links related to the bicycle station "Fernando el Católico". In the visualization, the nodes that have a greater number of connections are easily distinguished, since they appear with a larger size and more striking colours, such as "Plaza de la Cebada" or "Quevedo".

Figure 7. Graph display for station "Fernando el Católico"

 

5.4 Main network measures

Together with the visualization of the graph, the following measurements provide us with the main information of the analysed network. These averages, which are the usual metrics when performing network analytics, can be calculated in the statistics window.

Figure 8. Statistics window

 

• Nodes (N): are the different individual elements that make up a network, representing different entities. In this case the different bicycle stations. Its value on the network is 243
• Links (L): are the connections that exist between the nodes of a network. Links represent the relationships or interactions between the individual elements (nodes) that make up the network. Its value in the network is 3014
• Maximum number of links (Lmax): is the maximum possible number of links in the network. It is calculated by the following formula Lmax= N(N-1)/2. Its value on the network is 31878
• Average grade (k): is a statistical measure to quantify the average connectivity of network nodes. It is calculated by averaging the degrees of all nodes in the network. Its value in the network is 23.8
• Network density ​(d): indicates the proportion of connections between network nodes to the total number of possible connections. Its value in the network is 0.047
• Diámetro (d_max ): is the longest graph distance between any two nodes of the res, i.e., how far away the 2 nodes are farther apart. Its value on the network is 7
• Mean distance ​(d):is the average mean graph distance between the nodes of the network. Its value on the network is 2.68
• Mean clustering coefficient ​(C): Indicates how nodes are embedded between their neighbouring nodes. The average value gives a general indication of the grouping in the network. Its value in the network is 0.208
• Related component​: A group of nodes that are directly or indirectly connected to each other but are not connected to nodes outside that group. Its value on the network is 24

 

5.5 Interpretation of results

The probability of degrees roughly follows a long-tail distribution, where we can observe that there are a few stations that interact with a large number of them while most interact with a low number of stations.

The average grade is 23.8 which indicates that each station interacts on average with about 24 other stations (input and output).

In the following graph we can see that, although we have nodes with degrees considered as high (80, 90, 100, ...), it is observed that 25% of the nodes have degrees equal to or less than 8, while 75% of the nodes have degrees less than or equal to 32.

Figure 9. Grade Allocation Chart

 

The previous graph can be broken down into the following two corresponding to the average degree of input and output (since the network is directional). We see that both have similar long-tail distributions, their mean degree being the same of 11.9

Its main difference is that the graph corresponding to the average degree of input has a median of 7 while the output is 9, which means that there is a majority of nodes with lower degrees in the input than the output.

Figure 10. Graphs distribution of degrees of input and output

 

 

 

The value of the average grade with weights is 346.07 which indicates the average of total trips in and out of each station.

Figure 11. Graph distribution of degrees with weights

 

The network density of 0.047 is considered a low density indicating that the network is dispersed, that is, it contains few interactions between different stations in relation to the possible ones. This is considered logical because connections between stations will be limited to certain areas due to the difficulty of reaching stations that are located at long distances.

The average clustering coefficient is 0.208 meaning that the interaction of two stations with a third does not necessarily imply interaction with each other, that is, it does not necessarily imply transitivity, so the probability of interconnection of these two stations through the intervention of a third is low.

Finally, the network has 24 related components, of which 2 are weak related components and 22 are strong related components.

 

5.6 Centrality analysis

A centrality analysis refers to the assessment of the importance of nodes in a network using different measures. Centrality is a fundamental concept in network analysis and is used to identify key or influential nodes within a network. To perform this task, you start from the metrics calculated in the statistics window.

• The degree centrality measure indicates that the higher the degree of a node, the more important it is. The five stations with the highest values are: 1º Plaza de la Cebada, 2º Plaza de Lavapiés, 3º Fernando el Católico, 4º Quevedo, 5º Segovia 45.

Figure 12. Graph visualization degree centrality

 

• The closeness centrality indicates that the higher the proximity value of a node, the more central it is, since it can reach any other node in the network with the least possible effort. The five stations with the highest values are: 1º Fernando el Católico 2º General Pardiñas, 3º Plaza de la Cebada, 4º Plaza de Lavapiés, 5º Puerta de Madrid.

Figure 13. Measured closeness centrality distribution

 

Figure 14. Graphic visualization closeness centrality

 

• The measure of betweenness centrality indicates that the greater the intermediation measure of a node, the more important it is since it is present in more interaction paths between nodes than the rest of the nodes in the network. The five stations with the highest values are: 1º Fernando el Católico, 2º Plaza de Lavapiés, 3º Plaza de la Cebada, 4º Puerta de Madrid, 5º Quevedo.

Figure 15. Measured betweenness centrality distribution

 

FIgure 16. Graphic visualization betweenness centrality

 

With the Gephi tool you can calculate a large number of metrics and parameters that are not reflected in this study, such as the eigenvector measure or centrality distribution "eigenvector".

 

5.7 Filters

Through the filtering window, we can select certain parameters that simplify the visualizations in order to show relevant information of network analysis in a clearer way visually.

Figure 17. Filtering windows

Next, we will show several filtered performed:

• Range (grade) filtering, which shows nodes with a rank greater than 50, assuming 13.44% (34 nodes) and 15.41% (464 edges).

Figure 18. Graph display filtered range (degree)

 

• Edge filtering (edge weight), showing edges weighing more than 100, assuming 0.7% (20 edges).

Figure 19. Visualization graph edge filtering (weight)

 

Within the filters window, there are many other filtering options on attributes, ranges, partition sizes, edges, ... with which you can try to make new visualizations to extract information from the graph. If you want to know more about the use of Gephi, you can consult the following courses and trainings about the tool.

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6. Conclusions of the exercice

Once the exercise is done, we can appreciate the following conclusions:

• The three stations most interconnected with other stations are Plaza de la Cebada (133), Plaza de Lavapiés (126) and Fernando el Católico (114).
• The station that has the highest number of input connections is Plaza de la Cebada (78), while the one with the highest number of exit connections is Plaza de Lavapiés with the same number as Fernando el Católico (57).
• The three stations with the highest number of total trips are Plaza de la Cebada (4524), Plaza de Lavapiés (4237) and Fernando el Católico (3526).
• There are 20 routes with more than 100 trips. Being the 3 routes with a greater number of them: Puerta de Toledo – Plaza Conde Suchil (141), Quintana Fuente del Berro – Quintana (137), Camino Vinateros – Miguel Moya (134).
• Taking into account the number of connections between stations and trips, the most important stations within the network are: Plaza la Cebada, Plaza de Lavapiés and Fernando el Católico.

We hope that this step-by-step visualization has been useful for learning some very common techniques in the treatment and representation of open data. We will be back to show you further reuses. See you soon!

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The digitalization in the public sector in Spain has also reached the judicial field. The first regulation to establish a legal framework in this regard was the reform that took place through Law 18/2011, of July 5th (LUTICAJ). Since then, there have been advances in the technological modernization of the Administration of Justice. Last year, the Council of Ministers approved a new legislative package to definitively address the digital transformation of the public justice service, the Digital Efficiency Bill.

This project incorporates various measures specifically aimed at promoting data-driven management, in line with the overall approach formulated through the so-called Data Manifesto promoted by the Data Office.

Once the decision to embrace data-driven management has been made, it must be approached taking into account the requirements and implications of Open Government, so that not only the possibilities for improvement in the internal management of judicial activity are strengthened, but also the possibilities for reuse of the information generated as a result of the development of said public service (RISP).

Open data: a premise for the digital transformation of justice

To address the challenge of the digital transformation of justice, data openness is a fundamental requirement. In this regard, open data requires conditions that allow their automated integration in the judicial field. First, an improvement in the accessibility conditions of the data sets must be carried out, which should be in interoperable and reusable formats. In fact, there is a need to promote an institutional model based on interoperability and the establishment of homogeneous conditions that, through standardization adapted to the singularities of the judicial field, facilitate their automated integration.

In order to deepen the synergy between open data and justice, the report prepared by expert Julián Valero identifies the keys to digital transformation in the judicial field, as well as a series of valuable open data sources in the sector.

If you want to learn more about the content of this report, you can watch the interview with its author.

 

Below, you can download the full report, the executive summary, and a summary presentation.

The "Stories of Use Cases" series, organized by the European Open Data portal (data.europe.eu), is a collection of online events focused on the use of open data to contribute to common European Union objectives such as consolidating democracy, boosting the economy, combating climate change, and driving digital transformation. The series comprises four events, and all recordings are available on the European Open Data portal's YouTube channel. The presentations used to showcase each case are also published.

In a previous post on datos.gob.es, we explained the applications presented in two of the series' events, specifically those related to the economy and democracy. Now, we focus on use cases related to climate and technology, as well as the open datasets used for their development.

Open data has enabled the development of applications offering diverse information and services. In terms of climate, some examples can trace waste management processes or visualize relevant data about organic agriculture. Meanwhile, the application of open data in the technological sphere facilitates process management. Discover the highlighted examples by the European Open Data portal!

Open Data for Fulfilling the European Green Deal

The European Green Deal is a strategy by the European Commission aiming to achieve climate neutrality in Europe by 2050 and promote sustainable economic growth. To reach this objective, the European Commission is working on various actions, including reducing greenhouse gas emissions, transitioning to a circular economy, and improving energy efficiency. Under this common goal and utilizing open datasets, three applications have been developed and presented in one of the webinars of the series on data.europe.eu use cases: Eviron Mate, Geofluxus, and MyBioEuBuddy.

• Eviron Mate: It's an educational project aimed at raising awareness among young people about climate change and related data. To achieve this goal, Eviron Mate utilizes open data from Eurostat, the Copernicus Program and data.europa.eu.
• Geofluxus: This initiative tracks waste from its origin to its final destination to promote material reuse and reduce waste volume. Its main objective is to extend material lifespan and provide businesses with tools for better waste management decisions. Geofluxus uses open data from Eurostat and various national open data portals.
• MyBioEuBuddy is a project offering information and visualizations about sustainable agriculture in Europe, using open data from Eurostat and various regional open data portals.

The Role of Open Data in Digital Transformation

In addition to contributing to the fight against climate change by monitoring environment-related processes, open data can yield interesting outcomes in other digitally-operating domains. The combination of open data with innovative technologies provides valuable results, such as natural language processing, artificial intelligence, or augmented reality.

Another online seminar from the series, presented by the European Data Portal, delved into this theme: driving digital transformation in Europe through open data. During the event, three applications that combine cutting-edge technology and open data were presented: Big Data Test Infrastructure, Lobium, and 100 Europeans.

• "Big Data Test Infrastructure (BDTI)": This is a European Commission tool featuring a cloud platform to facilitate the analysis of open data for public sector administrations, offering a free and ready-to-use solution. BDTI provides open-source tools that promote the reuse of public sector data. Any public administration can request the free advisory service by filling out a form. BDTI has already aided some public sector entities in optimizing procurement processes, obtaining mobility information for service redesign, and assisting doctors in extracting knowledge from articles.
• Lobium: A website assisting public affairs managers in addressing the complexities of their tasks. Its aim is to provide tools for campaign management, internal reporting, KPI measurement, and government affairs dashboards. Ultimately, its solution leverages digital tools' advantages to enhance and optimize public management.
• 100 Europeans: An application that simplifies European statistics, dividing the European population into 100 individuals. Through scrolling navigation, it presents data visualizations with figures related to healthy habits and consumption in Europe.

These six applications are examples of how open data can be used to develop solutions of societal interest. Discover more use cases created with open data in this article we have published on datos.gob.es

Learn more about these applications in their seminars -> Recordings here

The combination and integration of open data with artificial intelligence (AI) is an area of work that has the potential to achieve significant advances in multiple fields and bring improvements to various aspects of our lives. The most frequently mentioned area of synergy is the use of open data as input for training the algorithms used by AI since these systems require large amounts of data to fuel their operations. This makes open data an essential element for AI development and utilizing it as input brings additional advantages such as increased equality of access to technology and improved transparency regarding algorithmic functioning.

Today, we can find open data powering algorithms for AI applications in diverse areas such as crime prevention, public transportation development, gender equality, environmental protection, healthcare improvement, and the creation of more friendly and liveable cities. All of these objectives are more easily attainable through the appropriate combination of these technological trends.

However, as we will see next, when envisioning the joint future of open data and AI, the combined use of both concepts can also lead to many other improvements in how we currently work with open data throughout its entire lifecycle. Let's review step by step how artificial intelligence can enrich a project with open data.

Utilizing AI to Discover Sources and Prepare Data Sets

Artificial intelligence can assist right from the initial steps of our data projects by supporting the discovery and integration of various data sources, making it easier for organizations to find and use relevant open data for their applications. Furthermore, future trends may involve the development of common data standards, metadata frameworks, and APIs to facilitate the integration of open data with AI technologies, further expanding the possibilities of automating the combination of data from diverse sources.

In addition to automating the guided search for data sources, AI-driven automated processes can be helpful, at least in part, in the data cleaning and preparation process. This can improve the quality of open data by identifying and correcting errors, filling gaps in the data, and enhancing its completeness. This would free scientists and data analysts from certain basic and repetitive tasks, allowing them to focus on more strategic activities such as developing new ideas and making predictions.

Innovative Techniques for Data Analysis with AI

One characteristic of AI models is their ability to detect patterns and knowledge in large amounts of data. AI techniques such as machine learning, natural language processing, and computer vision can easily be used to extract new perspectives, patterns, and knowledge from open data. Moreover, as technological development continues to advance, we can expect the emergence of even more sophisticated AI techniques specifically tailored for open data analysis, enabling organizations to extract even more value from it.

Simultaneously, AI technologies can help us go a step further in data analysis by facilitating and assisting in collaborative data analysis. Through this process, multiple stakeholders can work together on complex problems and find answers through open data. This would also lead to increased collaboration among researchers, policymakers, and civil society communities in harnessing the full potential of open data to address social challenges. Additionally, this type of collaborative analysis would contribute to improving transparency and inclusivity in decision-making processes.

The Synergy of AI and Open Data

In summary, AI can also be used to automate many tasks involved in data presentation, such as creating interactive visualizations simply by providing instructions in natural language or a description of the desired visualization.

On the other hand, open data enables the development of applications that, combined with artificial intelligence, can provide innovative solutions. The development of new applications driven by open data and artificial intelligence can contribute to various sectors such as healthcare, finance, transportation, or education, among others. For example, chatbots are being used to provide customer service, algorithms for investment decisions, or autonomous vehicles, all powered by AI. By using open data as the primary data source for these services, we would achieve higher

Finally, AI can also be used to analyze large volumes of open data and identify new patterns and trends that would be difficult to detect through human intuition alone. This information can then be used to make better decisions, such as what policies to pursue in each area to bring about the desired changes.

These are just some of the possible future trends at the intersection of open data and artificial intelligence, a future full of opportunities but at the same time not without risks. As AI continues to develop, we can expect to see even more innovative and transformative applications of this technology. This will also require closer collaboration between artificial intelligence researchers and the open data community in opening up new datasets and developing new tools to exploit them. This collaboration is essential in order to shape the future of open data and AI together and ensure that the benefits of AI are available to all in a fair and equitable way.

Content prepared by Carlos Iglesias, Open data Researcher and consultant, World Wide Web Foundation.

The contents and views reflected in this publication are the sole responsibility of the author.

Open data is a highly valuable source of knowledge for our society. Thanks to it, applications can be created that contribute to social development and solutions that help shape Europe's digital future and achieve the Sustainable Development Goals (SDGs).

The European Open Data portal (data.europe.eu) organizes online events to showcase projects that have been carried out using open data sources and have helped address some of the challenges our society faces: from combating climate change and boosting the economy to strengthening European democracy and digital transformation.

In the current year, 2023, four seminars have been held to analyze the positive impact of open data on each of the mentioned themes. All the material presented at these events is published on the European data portal, and recordings are available on their YouTube channel, accessible to any interested user.

In this post, we take a first look at the showcased use cases related to boosting the economy and democracy, as well as the open data sets used for their development.

Solutions Driving the European Economy and Lifestyle

In a rapidly evolving world where economic challenges and aspirations for a prosperous lifestyle converge, the European Union has demonstrated an unparalleled ability to forge innovative solutions that not only drive its own economy but also elevate the standard of living for its citizens. In this context, open data has played a pivotal role in the development of applications that address current challenges and lay the groundwork for a prosperous and promising future. Two of these projects were presented in the second webinar of the series "Stories of Use Cases”, an event focused on "Open Data to Foster the European Economy and Lifestyle": UNA Women and YouthPOP.

The first project focuses on tackling one of the most relevant challenges we must overcome to achieve a just society: gender inequality. Closing the gender gap is a complex social and economic issue. According to estimates from the World Economic Forum, it will take 132 years to achieve full gender parity in Europe. The UNA Women application aims to reduce that figure by providing guidance to young women so they can make better decisions regarding their education and early career steps. In this use case, the company ITER IDEA has used over 6 million lines of processed data from various sources, such as data.europa.eu, Eurostat, Censis, Istat (Italy's National Institute of Statistics), and NUMBEO.

The second presented use case also targets the young population. This is the YouthPOP application (Youth Public Open Procurement), a tool that encourages young people to participate in public procurement processes. For the development of this app, data from data.europa.eu, Eurostat, and ESCO, among others, have been used. YouthPOP aims to improve youth employment and contribute to the proper functioning of democracy in Europe.

Open Data for Boosting and Strengthening European Democracy

In this regard, the use of open data also contributes to strengthening and consolidating European democracy. Open data plays a crucial role in our democracies through the following avenues:

• Providing citizens with reliable information.
• Promoting transparency in governments and public institutions.
• Combating misinformation and fake news.

 

The theme of the third webinar organized by data.europe.eu on use cases is "Open Data and a New Impetus for European Democracy". This event presented two innovative solutions: EU Integrity Watch and the EU Institute for Freedom of Information.

Firstly, EU Integrity Watch is a platform that provides online tools for citizens, journalists, and civil society to monitor the integrity of decisions made by politicians in the European Union. This website offers visualizations to understand the information and provides access to collected and analyzed data. The analyzed data is used in scientific disclosures, journalistic investigations, and other areas, contributing to a more open and transparent government. This tool processes and offers data from the Transparency Register.

The second initiative presented in the democracy-focused webinar with open data is the EU Institute for Freedom of Information (IDFI), a Georgian non-governmental organization that focuses on monitoring and supervising government actions, revealing infractions, and keeping citizens informed.

The main activities of the IDFI include requesting public information from relevant bodies, creating rankings of public bodies, monitoring the websites of these bodies, and advocating for improved access to public information, legislative standards, and related practices. This project obtains, analyzes, and presents open data sets from national public institutions.

In conclusion, open data makes it possible to develop applications that reduce the gender wage gap, boost youth employment, or monitor government actions. These are just a few examples of the value that open data can offer to society.

Learn more about these applications in their seminars -> Recordings here.

1. Introduction

Visualizations are graphical representations of data that allow the information linked to them to be communicated in a simple and effective way. The visualization possibilities are very wide, from basic representations, such as line, bar or sector graphs, to visualizations configured on interactive dashboards.   

In this "Step-by-Step Visualizations" section we are regularly presenting practical exercises of open data visualizations available in datos.gob.es or other similar catalogs. They address and describe in a simple way the stages necessary to obtain the data, perform the transformations and analyses that are relevant to, finally, enable the creation of interactive visualizations that allow us to obtain final conclusions as a summary of said information. In each of these practical exercises, simple and well-documented code developments are used, as well as tools that are free to use. All generated material is available for reuse in the GitHub Data Lab repository.  

Then, as a complement to the explanation that you will find below, you can access the code that we will use in the exercise and that we will explain and develop in the following sections of this post. 

Access the data lab repository on Github.

Run the data pre-processing code on top of Google Colab.

 

2. Objetive

The main objective of this exercise is to show how to generate an interactive dashboard that, based on open data, shows us relevant information on the food consumption of Spanish households based on open data. To do this, we will pre-process the open data to obtain the tables that we will use in the visualization generating tool to create the interactive dashboard.   

Dashboards are tools that allow you to present information in a visual and easily understandable way. Also known by the term "dashboards", they are used to monitor, analyze and communicate data and indicators. Your content typically includes charts, tables, indicators, maps, and other visuals that represent relevant data and metrics. These visualizations help users quickly understand a situation, identify trends, spot patterns, and make informed decisions.    

Once the data has been analyzed, through this visualization we will be able to answer questions such as those posed below:   

• What is the trend in recent years regarding spending and per capita consumption in the different foods that make up the basic basket?  
• What foods are the most and least consumed in recent years?   
• In which Autonomous Communities is there a greater expenditure and consumption in food?  
• Has the increase in the cost of certain foods in recent years meant a reduction in their consumption?   

These, and many other questions can be solved through the dashboard that will show information in an orderly and easy to interpret way. 

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3. Resources

3.1. Datasets

The open datasets used in this exercise contain different information on per capita consumption and per capita expenditure of the main food groups broken down by Autonomous Community. The open datasets used, belonging to the Ministry of Agriculture, Fisheries and Food (MAPA), are provided in annual series (we will use the annual series from 2010 to 2021)  

Annual series data on household food consumption  

These datasets are also available for download from the following Github repository. 

• Annual series data on hosehold food consumption 

These datasets are also available for download from the following Github repository.

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3.2. Tools

To carry out the data preprocessing tasks, the Python programming language written on a Jupyter Notebook hosted in the Google Colab cloud service has been used. 

"Google Colab" or, also called Google Colaboratory, is a cloud service from Google Research that allows you to program, execute and share code written in Python or R on a Jupyter Notebook from your browser, so it does not require configuration. This service is free of charge. 

For the creation of the dashboard, the Looker Studio tool has been used. 

"Looker Studio" formerly known as Google Data Studio, is an online tool that allows you to create interactive dashboards that can be inserted into websites or exported as files. This tool is simple to use and allows multiple customization options.   

If you want to know more about tools that can help you in the treatment and visualization of data, you can use the report "Data processing and visualization tools". 

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4. Processing or preparation of data

The processes that we describe below you will find commented in the following Notebook that you can run from Google Colab. 

Before embarking on building an effective visualization, we must carry out a prior treatment of the data, paying special attention to its obtaining and the validation of its content, making sure that it is in the appropriate and consistent format for processing and that it does not contain errors.    

As a first step of the process, once the initial data sets are loaded, it is necessary to perform an exploratory data analysis (EDA) to properly interpret the starting data, detect anomalies, missing data or errors that could affect the quality of subsequent processes and results. If you want to know more about this process, you can resort to the Practical Guide of Introduction to Exploratory Data Analysis.   

The next step is to generate the pre-processed data table that we will use to feed the visualization tool (Looker Studio). To do this, we will modify, filter and join the data according to our needs.

The steps followed in this data preprocessing, explained in the following Google Colab Notebook, are as follows:  

1. Installation of libraries and loading of datasets  
2. Exploratory Data Analysis (EDA)  
3. Generating preprocessed tables  

You will be able to reproduce this analysis with the source code that is available in our GitHub account. The way to provide the code is through a document made on a Jupyter Notebook that once loaded into the development environment you can run or modify easily. Due to the informative nature of this post and to favor the understanding of non-specialized readers, the code is not intended to be the most efficient, but to facilitate its understanding so you will possibly come up with many ways to optimize the proposed code to achieve similar purposes. We encourage you to do so! 

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5. Displaying the interactive dashboard

Once we have done the preprocessing of the data, we go with the generation of the dashboard. A scorecard is a visual tool that provides a summary view of key data and metrics. It is useful for monitoring, decision-making and effective communication, by providing a clear and concise view of relevant information.  

For the realization of the interactive visualizations that make up the dashboard, the Looker Studio tool has been used. Being an online tool, it is not necessary to have software installed to interact or generate any visualization, but it is necessary that the data table that we provide is properly structured, which is why we have carried out the previous steps related to the preprocessing of the data. If you want to know more about how to use Looker Studio, in the following link you can access training on the use of the tool.  

Below is the dashboard, which can be opened in a new tab in the following link. In the following sections we will break down each of the components that make it up. 

 

 

5.1. Filters

Filters in a dashboard are selection options that allow you to visualize and analyze specific data by applying various filtering criteria to the datasets presented in the dashboard. They help you focus on relevant information and get a more accurate view of your data.   

Figure 1. Filters dashboard

 

The filters included in the generated dashboard allow you to choose the type of analysis to be displayed, the territory or Autonomous Community, the category of food and the years of the sample.   

It also incorporates various buttons to facilitate the deletion of the chosen filters, download the dashboard as a report in PDF format and access the raw data with which this dashboard has been prepared. 

 

5.2. Interactive visualizations

The dashboard is composed of various types of interactive visualizations, which are graphical representations of data that allow users to actively explore and manipulate information. 

Unlike static visualizations, interactive visualizations provide the ability to interact with data, allowing users to perform different and interesting actions such as clicking on elements, dragging them, zooming or reducing focus, filtering data, changing parameters and viewing results in real time. 

This interaction is especially useful when working with large and complex data sets, as it makes it easier for users to examine different aspects of the data as well as discover patterns, trends and relationships in a more intuitive way.  

To define each type of visualization, we have based ourselves on the data visualization guide for local entities presented by the NETWORK of Local Entities for Transparency and Citizen Participation of the FEMP.

5.2.1 Data tables

Data tables allow the presentation of a large amount of data in an organized and clear way, with a high space/information performance. 

However, they can make it difficult to present patterns or interpretations with respect to other visual objects of a more graphic nature. 

Figure 3. Dashboard data table

                                                                                                                                                                                                                    

5.2.2 Map of chloropetas

t is a map in which numerical data are shown by territories marking with intensity of different colours the different areas. For its elaboration it requires a measure or numerical data, a categorical data for the territory and a geographical data to delimit the area of each territory. 

Figure 3. Dashboard Chloropeta Map

                                                                                                                                                                        

5.2.3 Pie chart

It is a graph that shows the data from polar axes in which the angle of each sector marks the proportion of a category with respect to the total. Its functionality is to show the different proportions of each category with respect to a total using pie charts.   

Figure 4. Dashboard pie chart

                                                                                                                                                   

5.2.4 Line chart

It is a graph that shows the relationship between two or more measurements of a series of values on two Cartesian axes, reflecting on the X axis a temporal dimension, and a numerical measure on the Y axis. These charts are ideal for representing time data series with a large number of data points or observations. 

Figure 5. Dashboard line chart

5.2.5 Bar chart

It is a graph of the most used for the clarity and simplicity of preparation. It makes it easier to read values from the ratio of the length of the bars. The chart displays the data using an axis that represents the quantitative values and another that includes the qualitative data of the categories or time.

Figure 6. Dashboard bar chart

5.2.6 Hierarchy chart

It is a graph formed by different rectangles that represent categories, and that allows hierarchical groupings of the sectors of each category. The dimension of each rectangle and its placement varies depending on the value of the measurement of each of the categories shown with respect to the total value of the sample.

Figure 7. Dashboard Hierarchy chart

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6. Conclusions

Dashboards are one of the most powerful mechanisms for exploiting and analyzing the meaning of data. It should be noted the importance they offer us when it comes to monitoring, analyzing and communicating data and indicators in a clear, simple and effective way.  

As a result, we have been able to answer the questions originally posed:  

• The trend in per capita consumption has been declining since 2013, when it peaked, with a small rebound in 2020 and 2021.  
• The trend of per capita expenditure has remained stable since 2011 until in 2020 it has suffered a rise of 17.7%, going from being the average annual expenditure of 1052 euros to 1239 euros, producing a slight decrease of 4.4% from the data of 2020 to those of 2021. 
• The three most consumed foods during all the years analyzed are: fresh fruits, liquid milk and meat (values in kgs)  
• The Autonomous Communities where per capita spending is highest are the Basque Country, Catalonia and Asturias, while Castilla la Mancha, Andalusia and Extremadura have the lowest spending.  
• The Autonomous Communities where a higher per capita consumption occurs are Castilla y León, Asturias and the Basque Country, while in those with the lowest are Extremadura, the Canary Islands and Andalusia.  

We have also been able to observe certain interesting patterns, such as a 17.33% increase in alcohol consumption (beers, wine and spirits) in the years 2019 and 2020.   

You can use the different filters to find out and look for more trends or patterns in the data based on your interests and concerns.  

We hope that this step-by-step visualization has been useful for learning some very common techniques in the treatment and representation of open data. We will be back to show you new reuses. See you soon! 

 

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Open solutions, including Open Educational Resources (OER), Open Access to Scientific Information (OA), Free and Open-Source Software (FOSS), and open data, encourage the free flow of information and knowledge, serving as a foundation for addressing global challenges, as reminded by UNESCO.

The United Nations Educational, Scientific and Cultural Organization (UNESCO) recognizes the value of open data in the educational field and believes that its use can contribute to measuring the compliance of the Sustainable Development Goals, especially Goal 4 of Quality Education. Other international organizations also recognize the potential of open data in education. For example, the European Commission has classified the education sector as an area with high potential for open data.

Open data can be used as a tool for education and training in different ways. They can be used to develop new educational materials and to collect and analyze information about the state of the educational system, which can be used to drive improvement.

The global pandemic marked a milestone in the education field, as the use of new technologies became essential in the teaching and learning process, which became entirely virtual for months. Although the benefits of incorporating ICT and open solutions into education, a trend known as Edtech, had been talked about for years, COVID-19 accelerated this process.

Benefits of Using Open Data in the Classroom

In the following infographic, we summarize the benefits of utilizing open data in education and training, from the perspective of both students and educators, as well as administrators of the education system.

There are many datasets that can be used for developing educational solutions. At datos.gob.es, there are more than 6,700 datasets available, which can be supplemented by others used for educational purposes in different fields, such as literature, geography, history, etc.

Many solutions have been developed using open data for these purposes. We gather some of them based on their purpose: firstly, solutions that provide information on the education system to understand its situation and plan new measures, and secondly, those that offer educational material to use in the classroom.

In essence, open data is a key tool for the strengthening and progress of education, and we must not forget that education is a universal right and one of the main tools for the progress of humanity.

 

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