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Example formula ratios in some of the different areas on the border based on mobile usage survey result
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... Begitu pula dengan BPS, selama ini telah melakukan beberapa kajian dalam upaya untuk memanfaatkan big data. Beberapa kajian yang dilakukan BPS dengan memanfaatkan big data yang bisa digunakan untuk statistik resmi antaralain: data citra satelit untuk menghitung angka kemiskinan [5], data dari marketplace untuk menghitung statistik harga [6], mobile positioning data untuk menghitung jumlah wisatawan manca negara [7]; web scraping situs lowongan kerja untuk mendapatkan data jumlah pencari kerja [8], dsb. ...
The Central Statistics Agency (BPS) welcomes the challenge of utilizing big data. One of the BPS publications that can be supported using big data is the inflation figure collected from the consumer price survey. One part of the consumer price survey is the HK-4 Survey, which contains house contract rates. So far, the house contract rates produced by BPS have been underestimated or lower than the actual situation. Improvements to house contract rates are carried out by matching BPS data and web scraping of house rental sites using Propensity Score Matching
(PSM). The data used in this study includes DKI Jakarta, Bandung, and Semarang from September to October 2023. This study aims to find the best matching model using PSM to improve official statistics (house contract rates) by combining several propensity score value estimation methods and matching algorithms. Furthermore, the results matching the best model will be used to calculate the corrected house contract rates. The study results show that the best matching model generally uses logistic regression propensity score value estimation, the nearest neighbor
matching algorithm with returns and uses a 1:1 ratio. The corrected contract rates are far above the official ones (DKI Jakarta corrected 87.27%, Bandung 316.15%, and Semarang 60.04%). Web Scraping allows it to improve official statistics because it is cost and time-saving, enhances the quality of official statistical data, and supports better decision-making in various sectors.
... The coverage area of each cell is typically determined by the radio coverage of base station antennas (Ahas et al., 2008). The Indonesia National Statistics Office (locally known as BPS-Statistics Indonesia) has successfully implemented MPD since 2016 in collaboration with a major telecommunication provider and the Ministry of Tourism to calculate the number of international tourists in border areas that marked Indonesia as one of the few countries that have implemented MPD for official statistics and policymaking (Lestari et al., 2018). This MPD approach has completed the use of administrative data from immigration agency, which suffered from under-coverage issues due to the absence of immigration checkpoints in all border areas. ...
... Statistical Office of the European Union., 2022). The National Statistics Office (NSO) of Indonesia (also known as Statistics Indonesia), since 2016, has employed mobile positioning data (MPD) to assess inbound tourism within border regions (Lestari et al., 2018;Noviyanti et al., 2020). Since 2017, the World Tourism Organization (UNWTO) has advocated for exploring and utilising MPD as a potential tool for enhancing tourism-related activities (Demunter, 2017b). ...
... Despite the potential of MPD in the tourism statistics field, it has only been included in national tourism records of Indonesia and Estonia (Raun et al., 2016b;Lestari et al., 2018). Estonia has published inbound and outbound tourism data as part of its statistics (Ahas et al., 2008), while the Indonesian MPD has been combined with cross-border surveys since 2016 to generate tourism statistics (Lestari et al., 2018). ...
The tourism industry is increasingly utilizing big data to gain valuable insights and enhance decision-making processes. The advantages of big data make it a promising tool for analyzing various aspects of tourism, including sustainability. Moreover, integrating big data with prominent technologies like ML, AI and IoT has the potential to revolutionize smart and sustainable tourism.
Despite the potential benefits, the use of big data for sustainable tourism remains limited, and its implementation poses challenges related to governance, data privacy, ethics, stakeholder communication, and regulatory compliance. Therefore, strategies must be developed to navigate these issues through a proper governing system.
To bridge the existing gap, this dissertation focuses on the current research on big data for sustainable tourism and strategies for governing its use and implementation in conjunction with emerging technologies. Specifically, this PhD dissertation centers on mobile positioning data as a case due to its unique benefits, challenges, and complexity. Also, this project introduces three frameworks, namely: 1) a conceptual framework for digital twins for smart and sustainable tourism, 2) a documentation framework for architectural decisions to ensure the successful implementation of the DT technology as a governance mechanism, and 3) a big data governance framework for official statistics.
... The mobile positioning data improves the accuracy and efficiency of official tourism statistics. For example, by partnering with mobile operators, BPS Statistics Indonesia has successfully used mobile positioning data to complement the official tourism statistics (Lestari et al., 2018;Noviyanti et al., 2020;Pramana et al., 2017). The use of such innovative data science methods has also enhanced the speed of data collection and analysis. ...
Purpose
This chapter conceptualises a link between Industrial Revolution 4.0 (IR 4.0), big data, data science and sustainable tourism.
Design/Methodology/Approach
The author adopts a grounded theory and conceptual approach to endeavour in this exploratory research.
Findings
The outcome shows a significant rise of big data in the tourism sector under three major dimensions, i.e. business, governance and research. And, some exemplary evidence of institutions promoting the use of big data and data science for sustainable tourism has been discussed.
Originality/Value
The conceptualised interlinkage of concepts like IR 4.0, big data, data science and sustainable development provides a valuable knowledge resource to policy-makers, researchers, businesses and students.
... Mobile positioning data form mobile phones of foreign visitors were used for measuring visitor flows to destinations in Estonia from 2011 to 2013 and they are used by the Estonian Tourist Board [41]. BPS-Statistics Indonesia has used mobile positioning data for official statistics since October 2016 [45]. The literature review of the recent contributions to the use of mobile phone data in quantifying the volume of tourist flows and a brief case study of the Metropolitan City of Florence in [46] showed the main weaknesses of mobile phone data, which include costs, privacy restrictions, statistical issues of representativeness, among others. ...
The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition.
... Using roaming data of the foreign visitors' mobile phones, a study conducted in Estonia found nearly half of the tourists only visited one county, and 35.8% of the tourists visited two counties (Raun et al. 2016). A study in Indonesia also used mobile signaling data to count arrival tourists (Lestari et al. 2018). Mobile signaling data was also used in exploring the travel distance of visitors and regular visitors (Nilbe et al. 2014). ...
Understanding tourists’ spatial distribution and subgroups is important for urban tourism planning and management. This study utilized mobile signaling data from 21 million equipments to examine the tourist spots travelers visited and their movements in the city of Shanghai. In addition, we used latent profile analysis (LPA) to identify potential tourist groups according to their duration of stay and the tourist spots they visited. The results indicated that historical tourist spots drew a lot of travelers and nearly half of all tourists visited at least one historical site/district. Areas within the inner ring and around the ancient towns beyond the outer ring were frequently visited by tourists. Tourists preferred to visit famous tourist spots sequentially, rather than stopping by nearby less famous spots. Moreover, the connections between these famous spots were more frequent than between other spots. Three groups of tourists were identified, including long-stay multi-interest traveler, short-stay history-lover, and short-stay culture-lover. This study contributes to the application of mobile signaling data in exploring urban tourists’ spatial distribution, as well as can shed light on urban tourism planning and strategy development.
... BPS has been using Mobile Positioning Data (MPD) as one of the data sources since 2016, initially in the project of counting foreign tourists who enter through cross-border [4]. The use of MPD must meet the eligibility standards following the framework used by BPS. ...
... The increase in the use of cellular telephones, both number and area, and the continuously generated data can further analysis in research involving human movement [5]. For example, in collaboration with the largest cellular operator in Indonesia, BPS has been using mobile positioning data in several studies such as tourism [4,6], commuter, and metropolitan area delineation [7]. ...
Statistics Indonesia (BPS) has been using Mobile Positioning Data (MPD) to support official statistics since 2016. As a source of big data, MPD also has veracity characteristics, indicating uncertainty in the data. Therefore, it is necessary to check that the data are good enough to allow further analysis and the quality assurance process. Currently, there is no established international standard for quality assurance of MPD. This paper describes the quality matrix used by BPS in examining data from mobile operators. BPS uses thirteen indicators in conducting quality assurance, where the inspection uses several different methods, such as setting a threshold, checking data completeness, and checking the form of data distribution. Exploratory Data Analysis is carried out to determine whether the data meets the requirements for further analysis. We conducted this research on a mobile network operator data for June-July 2020 as the basis for MPD analysis in 2021. Based on the inspection during this period, BPS can cooperate with this cellular operator to conduct data analysis in 2021. However, the operator must repeat the calculation of the required matrix as quality assurance every month.
... This raises remarkable challenges such as the data access conditions, new methodological and quality frameworks, a larger IT infrastructure (both in hardware and in software), a deep revision of the statistical disclosure control, and the identification of relevant aggregates (mostly included as part of legal regulations) for a diversity of stakeholders and users. Although a number of illustrative case studies dealing with official statistics can already be found in the literature Williams, 2016;Nurmi, 2016;Izquierdo-Valverde et al., 2016;Dattilo et al., 2016;Senaeve and Demunter, 2016;Meersman et al., 2016;Reis et al., 2017;Sakarovitch et al., 2019;Galiana et al., 2018;Lestari et al., 2018), we still lack a production framework with a new statistical process. ...
Mobile network data has proved to be an outstanding data source for the production of statistics in general, and for Official Statistics, in particular. Similarly to another new digital data sources, this poses the remarkable challenge of refurbishing a new statistical production process. In the context of the European Statistical System (ESS), we substantiate the so-called ESS Reference Methodological Framework for Mobile Network Data with a first modular and evolvable proposed statistical process comprising (i) the geolocation of mobile devices, (ii) the deduplication of mobile devices, (iii) the statistical filtering to identify the target population, (iv) the aggregation into territorial units, and (v) the inference to the target population. The proposal is illustrated with synthetic data generated from a network event data simulator developed for these purposes.
... The uniqueness of mobile positioning lies in the fact that it is the only new digital data source used in tourism research that has vast potential of being used in official national tourism statistics and has been already used for that reason. So far, two countries in the world use MPD to produce national tourism statistics: Estonia [71] and Indonesia [72]. Regardless of the potential of using MPD in tourism statistics, only a few wide-ranging examples are available, although the number is growing. ...
... That is mainly due to the vagueness in the legislation and the MNO's fear of acting against the law, which may result in monetary penalties. In Indonesia, mobile signaling data combined with cross-border surveys have been used since 2016 [72]. In addition, there have been some small-scale pilot studies, for example, in France [73] and Finland [74] and other countries, the results of which have mainly been presented at international conferences. ...
... MNOs are unwilling to provide data due to the necessity for maintaining business confidentiality, or due to privacy protection and ethical concerns, fear of being tracked, and a general disapproval by society of such methods. Despite this, there are two countries in the world where MPD is an official source for tourism statistics-Estonia [71] and Indonesia [72]. In both countries the spin-off company of University of Tartu, Estonia, Positium, has helped to build up the methodological workflow to use MPD for producing national tourism statistics. ...
The importance of data and evidence has increased considerably in policy planning, implementation, and evaluation. There is unprecedented availability of open and big data, and there are rapid developments in intelligence gathering and the application of analytical tools. While cultural heritage holds many tangible and intangible values for local communities and society in general, there is a knowledge gap regarding suitable methods and data sources to measure the impacts and develop data-driven policies of cultural tourism. In the tourism sector, rapid developments are particularly taking place around novel uses of mobile positioning data, web scraping, and open application programming interface (API) data, data on sharing, and collaborative economy and passenger data. Based on feedback from 15 European cultural tourism regions, recommendations are developed regarding the use of innovative tools and data sources in tourism management. In terms of potential analytical depth, it is especially advisable to explore the use of mobile positioning data. Yet, there are considerable barriers, especially in terms of privacy protection and ethics, in using such data. User-generated big data from social media, web searches, and website visits constitute another promising data source as it is often publicly available in real time and has low usage barriers. Due to the emergence of new platform-based business models in the travel and tourism sector, special attention should be paid to improving access and usage of data on sharing and collaborative economy.
... There have been some small-scale pilot studies in the Netherlands (Heerschap, Ortega, Priem, & Offermans, 2014), France (Gitton, 2016), Italy (Dattilo & Sabato, 2017), Finland (Nurmi, 2018), and other countries, the results of which have been presented at international conferences. There are, however, only two countries in the world using MPD to produce national tourism statistics: Estonia (Eesti Pank, 2019) and Indonesia (Lestari, Esko, Sarpono, Saluveer, & Rufiadi, 2018). In Estonia, the database of CDRs covers the entire country and one full economic cycle (inbound data have been collected since 2004 and outbound since 2008), and tourism statistics have been published since 2008 (Ahas et al., 2008). ...
... In Estonia, the database of CDRs covers the entire country and one full economic cycle (inbound data have been collected since 2004 and outbound since 2008), and tourism statistics have been published since 2008 (Ahas et al., 2008). In Indonesia, mobile signalling data combined with cross-border surveys have been used since 2016 (Lestari et al., 2018). ...
... One of the biggest obstacles so far in using MPD has been the access to data from MNOs due to regulatory limitations and the MNOs being unwilling to provide the data (problems related to business secrets, privacy, and fear of tracking). However, there are two countries in the world, Estonia (Eesti Pank, 2019) and Indonesia (Lestari et al., 2018), where MPD are an official source for tourism statistics, and additionally there are several countries where pilot studies have been carried out (United Nations, 2017). It is important to note, that even after getting access to these data, confidentiality requirements put limitations on what could be published in research papers so that no business secrets or privacy information would be disclosed, which in turn will limit the reproducibility of the research and overall transparency. ...
Due to the ongoing increase in daily mobility, reductions in border controls, and new trends in tourism, it is important to find new ways to record comprehensively the growing number of tourists. This paper describes a method of extracting cross-border statistics on tourism from roaming call activities found within passive mobile positioning data. Eesti Pank (the central bank of Estonia) has been using these data and methodology since 2008 to calculate the national balance of payments and publish tourism statistics. Statistics obtained from mobile positioning data are herein compared with statistics on accommodation. Results indicate that positioning data enables the generation of detailed statistics on tourism, and for inbound visits, there is a strong correlation with official statistics on accommodation.
This paper investigate the issues and challenges of Big Data governance in the case study of Mobile Positioning Data (MPD) for tourism statistics in Indonesia. Our research aims to identify challenges in the dimensions of the big data governance framework, specifically in addressing issues on the role and communication among stakeholders, institutions or organizations, data quality, and regulatory compliance. To that aim, we conducted a field study in Statistics Indonesia, consisting of semi-structured interviews with related stakeholders. Through the result findings of our qualitative research on the MPD case study, we expect both to provide more insight and understanding of the urgency of big data governance and its framework for official statistics.
