scholarly journals Mobile Positioning Data: Prediktor Produk Domestik Regional Bruto (PDRB) Pada Masa Pandemi

2021 ◽  
Vol 2021 (1) ◽  
pp. 1076-1082
Author(s):  
Amanda Pratama Putra ◽  
Heny Wulandari
Keyword(s):  
Big Data ◽  
Mobile Positioning

Pandemi wabah virus corona (COVID-19) telah melumpuhkan aktivitas ekonomi di banyak negara, termasuk Indonesia. Big data sebagai sumber data alternatif untuk mengukur aktivitas ekonomi terutama dalam situasi pandemi dapat menjadi tambang informasi yang berharga. Penelitian ini memberikan sebuah pendekatan baru dalam mengukur aktivitas ekonomi yang berbasis pada Mobile Positioning Data (MPD). Data yang digunakan adalah data sampel dari aktivitas pelanggan jaringan seluler yang dikumpulkan di tingkat kabupaten/kota dengan interval harian, di mana aktivitas tersebut didefinisikan sebagai jumlah transaksi seluler dan lokasi yang terdeteksi, dan jumlah pengguna unik. Penelitian ini menunjukkan bahwa Mobile Positioning Data (MPD) dapat menjadi proksi untuk mengestimasi aktivitas ekonomi di Indonesia, terutama pada saat kondisi pandemi.

Towards big data as official statistics: Case study of the use of mobile positioning data to delineate metropolitan areas in Indonesia

2020 ◽  
Vol 36 (4) ◽  
pp. 943-954
Author(s):  
Isnaeni Noviyanti ◽  
Panca D. Prabawa ◽  
Dwi Puspita Sari ◽  
Ade Koswara ◽  
Titi Kanti Lestari ◽  
...  
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Business Process ◽  
Business Processes ◽  
Metropolitan Areas ◽  
Data Use ◽  
Data Access ◽  
Official Statistics ◽  
Mobile Positioning ◽  
Data Source

Nowadays, the use of so-called big data as a new data source to complement official statistics has become an opportunity for organizations focusing on statistics. The use of big data can lead to a more efficient data collection. However, currently, there has not been any standard business process for big data collection and processing in BPS-Statistics Indonesia. Meanwhile, the adoption of technologies alone cannot determine the success of big data use. It is widely known that big data use can be challenging, since there are issues regarding data access, quality, and methodology, as well as the development of required skillsets. This paper proposes a framework for a business process that is specifically designed to support the use of big data for official statistics at BPS-Statistics Indonesia along with how existing technology will support it. The development of this framework is based on the wider Statistical Business Process Framework and Architecture (SBFA) developed by BPS-Statistics Indonesia to describe and manage its overall statistical business processes. The paper uses the example of the use of Mobile Positioning Data (MPD) as a big data source to delineate Metropolitan Areas in Indonesia as a way to explain the implementation of the framework.

scholarly journals Innovative Tools for Tourism and Cultural Tourism Impact Assessment

2020 ◽  
Vol 12 (18) ◽  
pp. 7470
Author(s):  
Tarmo Kalvet ◽  
Maarja Olesk ◽  
Marek Tiits ◽  
Janika Raun
Keyword(s):  
Big Data ◽  
Business Models ◽  
Application Programming Interface ◽  
Cultural Tourism ◽  
Data Sources ◽  
Web Searches ◽  
Tourism Impact ◽  
Travel And Tourism ◽  
Mobile Positioning ◽  
Tourism Sector

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.

scholarly journals Evaluation of Plan Implementation in the Fast-Growing Chinese Mega-City: A Case of a Polycentric System in Hangzhou Core Built-Up Area

2020 ◽  
Vol 12 (5) ◽  
pp. 1723
Author(s):  
Liang Ding ◽  
Cheng Shi ◽  
Xinyi Niu
Keyword(s):  
Big Data ◽  
Built Environment ◽  
Path Dependence ◽  
Spatial Relationship ◽  
Rapid Expansion ◽  
Plan Implementation ◽  
Full Sample ◽  
The Public ◽  
Mobile Positioning ◽  
Old City

Previous evaluations of plan implementation focused on whether the materiality construction was in accordance with the plan. Without proper data, it is difficult to confirm whether the planning goals have been achieved. In this study, two types of big data have been used—full sample built-environment data and mobile-positioning big data—to evaluate the results of the implementation of the polycentric system in master planning in the Hangzhou core built-up area. Using the full sample built-environment data, the evaluation of materiality construction will be more objective and accurate. Using the mobile-positioning big data, the evaluation of the planning goals can be realized; this was almost impossible in the past. However, two aspects are considered: whether daily public activities, such as employment and recreation, have been dispersed from the old city and subsequently re-gathered in multiple centers outside the old city, and whether the polycentric system aids in optimizing the spatial relationship between residence and public activity. The following conclusions were drawn. In terms of actual materiality construction, the results showed minimal discrepancy from the plan. Fifteen city-level public centers have been constructed at principal, secondary, and sub-secondary levels. However, the polycentric system failed to achieve the expected effects of the planning goals. First, the public centers contributed in the gathering of public activities; however, the level of gathering at the newly built-up public centers was considerably lower than that at traditional public centers. Second, the public centers failed to encourage people to visit the nearest blocks for daily public activities, mainly because of the path dependence on the traditional centers in the process of multi-centralization and over-rapid expansion of the city. Owing to this, residents did not have sufficient time to adjust to the spatial relationship between the residence and daily public activities.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

When the Sample Is the Population: Big Data

PsycCRITIQUES ◽  
2014 ◽  
Vol 59 (2) ◽  
Author(s):  
David J. Pittenger
Keyword(s):  
Big Data
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