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

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.

Mobility functional areas and COVID-19 spread

This work introduces a new concept of functional areas called Mobility Functional Areas (MFAs), i.e., the geographic zones highly interconnected according to the analysis of mobile positioning data. The MFAs do not coincide necessarily with administrative borders as they are built observing natural human mobility and, therefore, they can be used to inform, in a bottom-up approach, local transportation, spatial planning, health and economic policies. After presenting the methodology behind the MFAs, this study focuses on the link between the COVID-19 pandemic and the MFAs in Austria. It emerges that the MFAs registered an average number of infections statistically larger than the areas in the rest of the country, suggesting the usefulness of the MFAs in the context of targeted re-escalation policy responses to this health crisis. The MFAs dataset is openly available to other scholars for further analyses.

Crowd Sensing Urban Healthy Street Monitoring Based on Mobile Positioning System

With the rapid development of social economy, the importance of ecological civilization is increasing day by day. The level of environmental monitoring will directly affect the control of total pollution sources and the evaluation of environmental quality. Crowd sensing is to use the group computing power of users with smart devices to quickly collect surrounding multidimensional data, analyze and calculate based on these massive perception data, and then dig out group behavior patterns and other regular information. In order to meet the basic requirements of environmental protection work put forward by the government in the new era, this paper proposes mobile positioning and crowd sensing technologies, explains the distribution map method and adaptive weighting data fusion related algorithms, and designs and develops a mobile positioning system based on mobile positioning. The system of crowd sensing of urban healthy street monitoring is designed, and then we use this PM2.5 monitoring system to monitor the relevant environmental data of a street in Shanghai and use normalization to process the experimental data. The experimental data and statistical results show that this performance of the system is good, and the accuracy of image PM2.5 monitoring reaches 90%–95%. More than 90% of street residents are also very satisfied with this system, believing that it can play a role in supervising urban healthy street.

A Semantics-Based Trajectory Segmentation Simplification Method

With the development of mobile positioning technology, a large amount of mobile trajectory data has been generated. Therefore, to store, process and mine trajectory data in a better way, trajectory data simplification is imperative. Current trajectory data simplification methods are either based on spatiotemporal features or semantic features, such as road network structure, but they do not consider semantic features of a trajectory stop. To overcome this limitation, this study presents a trajectory segmentation simplification method based on stop features. The proposed method first extracts the stop feature of a trajectory, then divides the trajectory into move segments and stop segments based on the stop features, and finally simplifies the obtained segments. The proposed method is verified by experiments on personal trajectory data and taxi trajectory data. Compared with the classic spatiotemporal simplification method, the proposed method has higher spatiotemporal and semantic accuracy under different simplification scales. The proposed method is especially suitable for trajectory data with more stop features.

Forecast of Thunderstorm Cloud Trend Based on Monitoring Data of Thunder Mobile Positioning System

As a natural phenomenon, thunder and lightning have a major impact on human production and life. As an important part of lightning protection technology, the main task of the lightning mobile positioning system is to detect and determine the location of lightning and, at the same time, provide more accurate lightning discharge parameters for lightning research. It is a new technology that serves the entire society and is in urgent need of development. This paper aims to study the trend prediction of thunderstorm cloud based on the monitoring data of the thunder and lightning mobile positioning system. In this thesis, the inverse distance-weighted interpolation method can be used to determine the lightning area and the principle of lightning monitoring and positioning, and the classification of lightning and the practical significance of lightning mobile positioning system monitoring are also studied. Finally, the Hurst index of this paper can reveal the trend elements in the time series well through the experiment, so as to judge the lightning strikes. At the same time, it also introduces everyone’s satisfaction survey on the lightning mobile positioning system. The results of this paper show that the lightning mobile positioning system has been widely used in our country’s meteorological monitoring stations, and it plays a very important role in our national defense lightning strikes and effectively realizes the lightning strike prediction in the monitoring process, which can better enable the competent department to take timely and accurate measures to prevent lightning strikes. Experimental analysis shows that the accuracy of the lightning mobile positioning system has reached 92%, and the practicability has reached 88%.