Open Data Website Research Articles

Regional Variability in MRI Scans with Different Magnetic Field Strengths in Japan: Implications for Healthcare Preparedness for Alzheimer's Disease Treatment.

(1) Background: The 2023 approval of lecanemab for early-stage Alzheimer's disease (AD) highlighted the need for routine 1.5T or 3.0T MRI scans to monitor amyloid-related imaging abnormalities (ARIAs). Regional disparities in MRI scan frequency, MRI scanner availability, and scanner magnetic field strengths could affect readiness for anti-amyloid therapy and lead to inconsistencies in ARIA detection nationwide. (2) Methods: We assessed regional variance in MRI scan frequency and field strength across Japan using the National Database (NDB) Open Data website, which summarizes Japanese public health insurance claims from the fiscal years (FYs) 2015 to 2021. We employed a mixed-effects model with prefecture-level random intercepts and slopes over time, subsequently categorizing prefectures into clusters based on MRI usage. (3) Results: 1.5T MRI was the most common magnetic field strength, remaining stable from FY2015 to FY2021. 3.0T MRI usage slightly increased, although the COVID-19 pandemic in FY2020 led to a maximum reduction of 5%. Prefecture-level variance was higher for 3.0T MRIs, with more frequent usage in western Japan. (4) Conclusions: This study highlights prefecture-level variance in MRI usage across Japan. The insights gained could be instrumental in improving healthcare preparedness for anti-amyloid treatment and patient management.

MODELING THE INCIDENCE OF MALNUTRITION IN BOGOR REGENCY USING ZERO-INFLATED NEGATIVE BINOMIAL MIXED EFFECT MODEL

Modeling response variables in the form of count data generally uses a model based on the Poisson distribution. However, some conditions, such as the presence of excess zero, can be found in the data that result in overdispersion, which will have an impact on the resulting variance in the model. In this paper, three approaches, namely the Poisson Mixed Model, the Negative Binomial (NB) Mixed Model, and the Zero-Inflated Negative Binomial (ZINB) Mixed Model, are used to model the incidence of malnutrition in Bogor Regency. The data used in this study are secondary data sourced from the West Java open data website. Based on the results of data analysis, it appears that the ZINB Mixed Model method is a method capable of accommodating random effects, overdispersion, and excess zero in modeling malnutrition in Bogor Regency. Variables that significantly affect the occurrence of malnutrition cases in villages in Bogor Regency include the Number of Children Weighed Routinely Every Month, Number of Children Measured for Length and Height Twice a Year, Number of Children under 12 Months Old Who Received Complete Basic Immunization, Number of Posyandu (Integrated Health Post), and Number of Parents/Caregivers Participating in Monthly Parenting (PAUD).

Assessing the energy efficiency of fossil fuel in ASEAN

The world's industries, transportation systems, and households rely heavily on fossil fuels despite their limited availability and high carbon content. Therefore, it is of the utmost importance to improve fossil fuel energy efficiency in order to facilitate the shift towards a sustainable energy system with reduced greenhouse gas emissions. This paper employs a slacks-based measure network data envelopment analysis model with undesirable outputs to assess the efficiencies of fossil fuel energy in the Association of Southeast Asian Nations (ASEAN) countries during a span of seven years, specifically from 2015 to 2021. The inclusion of undesirable outputs in this study is important because it allows for a more realistic assessment of efficiency by considering factors like CO2 emissions, which are undesirable outcomes associated with fossil fuel use. The datasets utilised in this study are sourced from the U.S. Energy Information Administration and the open data website of Our World in Data. Based on the findings, it can be observed that Singapore and the Philippines have demonstrated outstanding performance in maximising the utilisation of fossil fuels. In contrast, Myanmar exhibits the lowest level of efficiency in this analysis. By identifying top-performing countries in terms of fossil fuel efficiency, it is possible to implement measures to boost efficiency in under-performing countries. This can be achieved through the promotion and adoption of cleaner energy alternatives, specifically renewable energy sources that exhibit a low or negligible carbon footprint. These findings offer significant contributions to policymakers exploring sustainable energy usage, environmental stewardship, and the formulation and execution of comprehensive strategies that aim to mitigate carbon dioxide emissions arising from the consumption of fossil fuels in the ASEAN region.

Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction

This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuable insights. It is possible that additional data points, such as holidays and weather conditions, will further enhance the accuracy of the model in future research. As a result of implementing XGBoost, Jakarta's transportation system can optimize resource utilization and improve customer service in order to improve passenger satisfaction. Future studies may benefit from additional data points, such as holidays and weather conditions, in order to improve XGBoost's efficiency.

A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention

This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia.

Classification Classification of COVID-19 Monthly Cases Using Artificial Neural Network (ANN) Method

This study proposes the MLP type ANN method with the influence of Cross-Validation evaluation using three K-Fold tests, namely 3, 5, and 8. The data used are the data that relate to COVID-19 cases issued from the Bandung Public Health Office, climate report from Bandung Meteorological, Climatological, and Geophysical Agency (BMKG), population data from Bandung Population Office, citizen’s educational history from the Bandung Education Office and the West Java Open Data website. The data also was gathered from 151 sub-districts in Bandung City, with a total of 22 attributes collected from November 2020 to December 2021. The ANN method is included in the deep learning process. Therefore, the number of hidden layers utilized has a significant impact on the performance of the model being constructed. The implementation of Cross-Validation evaluation with K=8 results in an accuracy value of up to 98% and an error metric measurement of 0.3404 for MAE and 0.5994 for RMSE. This study's objective is to provide information on the optimal K-Fold Cross Validation parameters used in this ANN method to provide better performance during building a classification model for confirmed Covid-19 patients.

Epidemiological features and risk factors for measles and rubella in Taiwan during 2011 to 2020.

The risk of geographic transmission of infectious diseases due to air travel varies greatly. Our aim is to survey empirical data that provide a retrospective historical perspective on measles and rubella. This study used the open data website provided by the Taiwan Centers for Disease Control (TCDC) to extract the reported numbers of measles and rubella case between 2011 and 2020. There were 306 cases of measles and 135 cases of rubella. The incidence of measles and rubella per million population were 0 to 6.0 and 0 to 2.6, respectively. There was a gradual increase in the numbers of cases in those aged 20-39 years, and distinct duration patterns. It indicated that the risk of contracting rubella has significantly decreased in the last 5 years. Measles cases aged 20 to 39 years accounted for 72.5% of all cases. Rubella cases aged 20 to 39 years accounted for 59.3% of all cases. The male and residency in the Taipei metropolitan area or northern area were identified as potential risk factors for measles and rubella. Coverage with the first dose of the measles, mumps and rubella (MMR) vaccine in Taiwan increased from 97.31% to 98.86%, and the uptake rate of the second dose of the MMR vaccine increased from 95.73% to 98.39% between 2010 and 2020. Furthermore, the numbers of imported cases of measles (n = 0) and rubella (n = 0) reported during the coronavirus disease 2019 (COVID-19) pandemic were lower than those from 2011 to 2019. Measles and rubella cases were imported most frequently from Cambodia and Vietnam. This study represents the first report of confirmed cases of acquired measles and rubella from surveillance data of the TCDC between 2011 and 2020, also demonstrates that the numbers of cases of measles and rubella significantly decreased in Taiwan during the COVID-19 pandemic.

Applying a Pedestrian Level of Service in the Context of Social Distancing: The Case of the City of Madrid.

During the COVID-19 pandemic, there has been a lot of discussion about keeping interpersonal distance to prevent the virus from spreading. To keep this interpersonal distance, authorities at different levels have taken measures to reduce people’s interactions, such as reducing capacities, curfews, pop-up cycle lanes, temporary pedestrianisation, and lockdowns. Many of these temporary measures have been perceived from a static view. Nevertheless, in a scenario of “new normality” or in the face of a possible new pandemic, the amount of data (big data) generated by different sources, such as sensors, in large cities has extraordinary potential to be used together with tactical urbanism for quick adaptation. The aim of this study was to gain insight into the aforementioned issues by analysing spatio-temporal patterns of pedestrian mobility and developing a variation of the pedestrian level of service measure; the pandemic pedestrian level of service (P-PLOS). This measure provides a dynamic view of pavement capacities according to the interpersonal distance recommendations during the pandemic. P-PLOS was tested in the city of Madrid based on the pedestrian counter data that was provided by the local government through its open data website. We found that the application of P-PLOS, together with street design, allows for knowing where and when it is necessary to take tactical urbanism measures in order to maintain or improve the level of service, as well as where it is necessary to take measures to reduce pedestrian flow.

Tourism and Gross Domestic Product short-run causality revisited: A symbolic transfer entropy approach

We employ a symbolic transfer entropy panel data test in a large-scale data set to provide new insights on the worldwide short-term causality relations between growth and inbound tourists. Using a large data set on 145 countries from the World Bank Open Data website, we show that, despite the evidently strong correlation between these two magnitudes, claiming that the increases in inbound tourists Granger-cause positive shocks in GDP is not supported by the data. By contrast, the data seem to point out in the direction of a reverse causality in that it is GDP growth what drives international inbound tourists in the short run. JEL classification C12, C14, C33, C55.

Incidence and Risk Factors for Notifiable Typhoid and Paratyphoid in Taiwan during the Period 2011-2020.

The risk of the geographic transmission of emerging infectious diseases through air travel varies greatly. In this study, we collected data on cases of food-borne diseases between the years 2011 and 2020 in Taiwan to access the epidemiological features, differences, and trends in domestic and imported cases of typhoid and paratyphoid in terms of patient sex, age, month of confirmation, and area of residence. In this study, we made use of the open data website provided by Taiwan’s Centers for Disease Control (TCDC) to extract the reported numbers of cases of typhoid and paratyphoid between January and December from 2011 to 2020 for comparison. Univariate analysis was performed using the Chi-square test for categorical variables. Fisher’s exact test was performed if an expected frequency was less than 5. A total of 226 typhoid cases and 61 paratyphoid cases were analyzed from the database. The incidences of typhoid and paratyphoid per million of the population were 0.42–2.11 and 0–0.39, respectively. There was a significant difference in the incidence of the diseases between the age groups (p = 0.019), with a gradual increase in the 20–40 years group. A distinct seasonal (between fall and spring) variation was also observed (p = 0.012). There were 34 cases of children with typhoid in the period 2011–2015 and 12 cases of children with typhoid in the period 2016–2020. During these periods, there were two cases of paratyphoid. This study indicated that the risk of children suffering from typhoid has been significantly reduced in the last five years. Furthermore, we found that more women have acquired typhoid and paratyphoid than men, and that living in the Taipei metropolitan area and the northern area was a potential risk factor. Furthermore, the number of imported cases of typhoid (n = 3) and paratyphoid (n = 0) reported during the COVID-19 pandemic was lower than that reported for the same disease from 2011 to 2020. More typhoid and paratyphoid cases were imported from Indonesia, India, Myanmar, and Cambodia. This study represents the first report on confirmed cases of acquired typhoid and paratyphoid from surveillance data from Taiwan’s CDC for the period 2011–2020. This study also demonstrates that the cases of typhoid and paratyphoid decreased in Taiwan during the COVID pandemic. Big data were used in this study, which may inform future surveillance and research efforts in Taiwan.

Trends in COVID-19 School Related Inquiries Using 311 New York City Open Data.

This study aimed to assess the coronavirus disease (COVID-19) school-related information New York City residents sought through the 311 Call Center. July to November inquiries were downloaded from the NYC Open Data website for 2018–2020. Calls were categorized as related to “Schools”, “Access”, “Food”, “Hospitals”, “Transportation”, and “Unemployment”. Overall call types, and among school-related calls, detailed call types, were compared over the years, using chi-squared tests. School-related inquiries increased by 71% from 2018 to 2020. During 2020, the most common (49%, n = 22,471) call description was “Coronavirus and Schools”, encompassing calls about learning options, safety, and resources. Spikes in these calls corresponded to official announcements, including those about Fall reopening plans (August 31: n = 678; September 1: n = 624) and schedules and staffing (September 16th: n = 1043; September 17th: n = 713), and after the start of in-person learning (September 21: n = 680). This study demonstrates that as government officials updated NYC schooling plans for Fall 2020, there were increased concerns among NYC residents. Future COVID-19 schooling changes need to be conveyed clearly and disseminated effectively in order to avoid confusion about NYC’s pandemic learning strategy and to address health and safety concerns.

Using NYC 311 Call Center Data to Assess Short- and Long-Term Needs Following Hurricane Sandy.

Hurricane Sandy made landfall across New York City (NYC) in October 2012, but the long-term consequences of the storm are still not fully understood. We analyzed NYC data to quantify the extent of Hurricane Sandy-related concerns over time. Data on NYC 311 Call Center inquiries were downloaded from the NYC Open Data website (October 29, 2012 to May 26, 2020) to provide information about Sandy-related calls using the keywords "Hurricane" and "Sandy". In the first 2 wk after Hurricane Sandy, 15.6% of 311 calls were related to the storm. From 2012 to 2020, the volume of inquiries decreased from 87,209 to 25. The majority of calls in 2012 (49,181; 56%) was requesting general Hurricane Sandy information, and in 2020 assistance with property restoration (20; 79%). The long-term consequences of Hurricane Sandy in NYC persist into 2020, almost 8 y after the initial event. The needs of Hurricane Sandy victims have changed over time from requiring general information regarding closures, property destruction and immediate disaster relief to aid with legal, financial, and mental health consequences. Disaster response policy-makers must understand the changing needs of NYC residents to provide resources and prepare for future disasters.

Assessing population needs for more COVID-19 information across New York State

Background: The novel coronavirus (COVID-19) is a potentially fatal respiratory infection that originated in Wuhan, China in December 2019 and has since rapidly spread around the world causing widespread concern. New York State (NYS) currently has 142 confirmed cases. As the number of confirmed cases and those quarantined have increased, individuals have relied on public resources to learn about COVID-19. We analyzed trends in NYS and New York City (NYC) data to quantify COVID-19 related concerns. Methods: Data from NYC 311 Call Center Inquiry was downloaded from the NYC Open Data website (12/1/2019-3/10/20) to provide information about COVID-19 related calls across NYC. This dataset contains the date of the call and a brief description. Calls containing the keyword: “Coronavirus” were included. Additionally, the “interest over time” of the search term “Coronavirus” across NYS was extracted from Google Trends from 12/1/19 to 3/10/2020. Results: 1955 COVID-19 related calls were placed to 311, constituting 0.14% of all 311 calls in this time period. The proportion of COVID-19 related calls increased over time: January: 51 [0.011% of all 311 calls]; February: 783 [0.195%]; March: 1121 [1.054%]. The majority of calls sought information on COVID-19 (52.5%), with subsets gathering information regarding recent travel to Hubei Province in China (825 calls, 42.2%) and the Middle East (103 calls, 5.27%). This spike in 311 calls parallels an increase in “interest over time” of “Coronavirus” across NYS according to Google Trends, which has exponentially increased since the end of February 2020, and has peaked at the time of abstract submission. Conclusion: Interest in and queries about COVID-19 have increased sharply since December 2019, culminating in a current peak of information-seeking behavior across NYS and NYC. Accordingly, it is vital that public health officials provide clear and up-to-date information about protective measures and health related protocols.

Analyzing New York City 311 Call Center Data related to Hurricane Sandy

Background: Hurricane Sandy made landfall on October 29 2012 and caused immediate health and economic impacts in the greater New York City (NYC) area. However, the long-term consequences of the storm are not fully understood. We analyzed NYC 311 data to quantify the extent of Hurricane Sandy related concerns in NYC. Methods: Data from NYC 311 Call Center Inquiry and Service Requests were downloaded from the NYC Open Data website (9/1/2010-3/20/20). We included only calls containing the keyword: “Sandy”. Descriptive analyses were conducted to understand the characteristics of the 311 calls, including the date, agency contacted, the geographic location of the caller if available, a brief description and the resolution of each call. Results: Eighty percent of the 78,541 calls specific to Hurricane Sandy were made in 2012, representing 2.1% of all 311 calls in 2012. The top three contacted agencies were 311 Call Center (36.1%), Department of Education (16.2%), and City Hall (13.6%). The main call-in reasons were to obtain information about the City's response to Hurricane Sandy (36.0%), public school openings/closings related to Sandy (14.9%), and locations of walk-in centers offering assistance with recovery services (11.2%). There were 231 service requests made between 9/1/2012 and 12/31/2017 about destroyed buildings due to Sandy, of which 95.9% occurred in 2013. Calls were from Queens (83%), Staten Island (15%) and Brooklyn (2%), with the majority occurring in the Breezy Point neighborhood of Queens (62.6%). Conclusion: The 311 call center plays an important role in receiving and disseminating information about hurricane preparedness and recovery/relief in both the immediate and long-term aftermath of Hurricane Sandy. Evidenced by Hurricane Sandy calls persisting into 2020, ongoing efforts must be made to enhance disaster preparedness and resilience in communities, in addition to immediately responding to needs of communities after natural disasters.

The Northern Tornadoes Project: Uncovering Canada’s True Tornado Climatology

AbstractCanada is a vast country with most of its population located along its southern border. Large areas are sparsely populated and/or heavily forested, and severe weather reports are rare when thunderstorms occur there. Thus, it has been difficult to accurately assess the true tornado climatology and risk. It is also important to establish a reliable baseline for tornado-related climate change studies. The Northern Tornadoes Project (NTP), led by Western University, is an ambitious multidisciplinary initiative aimed at detecting and documenting every tornado that occurs across Canada. A team of meteorologists and wind engineers collects research-quality data during each damage investigation via thorough ground surveys and high-resolution satellite, aircraft, and drone imaging. Crowdsourcing through social media is also key to tracking down events. In addition, NTP conducts research to improve our ability to detect and accurately assess tornadoes that affect forests, cropland, and grassland. An open data website allows sharing of resulting datasets and analyses. Pilot investigations were carried out during the warm seasons of 2017 and 2018, with the scope expanding from the detection of any tornadoes in heavily forested regions of central Canada in 2017 to the detection of all EF1+ tornadoes in Ontario plus all significant events outside of Ontario in 2018. The 2019 season was the first full campaign, systematically collecting research-quality tornado data across the entire country. To date, the project has found 89 tornadoes that otherwise would not have been identified, and increased the national tornado count in 2019 by 78%.

Permanencia y Titulación de las carreras universitarias en Chile. Seguimiento de cohortes 2009 a 2017 empleando datos abiertos

This article reports on the retention of students and their completion rates in Chile´s five years undergraduate university programmes, covering the performance of cohorts entered between the years 2009 and 2017 at the national level. These figures were computed employing data available in the Open Data website of the Ministry of Education. The findings show that three out of five students remain in the university and the same tertiary program after 4 years from when they entered it; 16% completed their formation after 5 years; and 46% do so over a period that covers up to three additional years. At the institutional level, second-year retention and institutional accreditation show a correlation with the completion rates after 8 years, but do not show the same with the fifth-year ones. It concludes by highlighting the need of deepen the public debate over the competition effectiveness concept intended for the national tertiary education system, as well as, the relevance of incorporating this type of information into the set of indicators periodically reported by official bodies.

Acceptance, Quality and Trust Factors – Conceptual Model for Open Government Data Potential Use

– To implement Big Data extensively in the public sector, many governments around the world are taking their initiative to ensure that Open Government Data (OGD) is a success. Even though the governments had provided thousands of datasets in the open data website, the level of use of open government data is still a question that has not been resolved. Therefore, the objective of this paper is to propose a new model of measuring the level of use of open government data. By integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), IS Success Model (ISSM) and Trust factors, this new model perhaps can be a major contribution to the governments in ensuring that citizens are getting benefits from the datasets provided in the open data website.

Effects of monetary policy on economic growth in Zimbabwe

This research examines the hypothesis of money neutrality in Zimbabwe. After studying the relevant literature on the effects of changes in money supply on real variables, it outlines the research design for a macro-level study on the impact of changes in money supply on real variables. The hypothesis is that there is a positive relationship between money supply and real variables (GDP). The researcher used real GDP as the dependent variable whilst money supply (M3), interest rate and government expenditure were used as explanatory variables. A VAR model has been applied using the country’s macroeconomic data from 1990 to 2017 which was obtained from ZIMSTATS and World Bank Open Data website. Impulse response functions and variance decomposition were used to analyse the impact of the explanatory variables on real GDP. The results suggest that money positively affects real GDP in the short run but in the long it is insignificant in influencing real output. This means that in Zimbabwe, money is non-neutral in the long run, but it is neutral in the long run. Government expenditure has an insignificant influence on GDP both in the short and long run whilst interest rate has a positive effect on GDP in the long run. The recommendations which were given are that the government; should use expansionary monetary policy to increase real GDP, demonetise the bond note as well as the RTGS and adopting the Rand, curbing inflation through increasing production and ensuring transparency in the manner in which loans are given.

Determinants of Continuance Intention to Use Open Data Website: An Insight from Indonesia

Open data is a flagship initiative in open government program to increase transparency, social and commercial value, and participatory governance. Open data is a relatively new field and the factors affecting its continuance use by citizen has not been widely studied. A better understanding of these factors can help government to formulate policies and strategies that can improve the acceptance and use of open data website. The research model is based on the Theory of Planned Behavior and integrated with the technology quality and trust factors. Data collection was conducted using questionnaire. Structural Equation Modeling (SEM) was used to test the research model. The results show that attitude, subjective norm, perceived behavioral control and trust directly affect continuance intention to use open data website. Systems quality affects perceived ease of use, while information quality positively affects perceived usefulness, perceived ease of use, and trust. Both perceived usefulness and perceived ease of use affect user’s attitude. The analysis of this study suggests that each influencing factor provides implications for government to stimulate the continuance use of open data website.

EUREGIO: The Construction of a Global IO Database With Regional Detail for Europe for 2000–2010

This paper introduces the EUREGIO database: the first time-series (annual, 2000-2010) of global IO tables with regional detail for the entire large trading bloc of the European Union. The construction of this database, which allows for regional analysis at the level of so-called NUTS2 regions, is presented in detail for its methodology and applications. The tables merge data from WIOD (the 2013 release) with, regional economic accounts, and interregional trade estimates developed by PBL Netherlands Environmental Assessment Agency, complemented with survey-based regional input-output data for a limited number of countries. All used data are survey data and only non-behavioral assumptions have been made to estimate the EUREGIO dataset. These two general rules of data construction allow empirical analyses focused on impacts of changes in behavior (of economies, firms, policies) without endogenously having this behavior embedded already by construction. The tables are publicly available to the research community, from the Dutch government open data website. In this project, both a regional trade database, and production and consumption data of different actors in different regions, were used. The focus is thus intentionally on the regionalization of both trade and the regional use and supply of products by different economic actors. The paper is organized around the successive steps of the data construction: (1) adjustment of WIOD, (2) regional information, (3) Construction of tables, (4) conclusions on the usefulness of this type of regional IO tables with an overview of current applications of the EUREGIO database.