Spatial interpolation of mobile positioning data for population statistics

Aasa, Anto; Kamenjuk, Pilleriine; Saluveer, Erki; Šimbera, Jan; Raun, Janika

doi:10.1080/17489725.2021.1917710

Cited by 13 publications

(7 citation statements)

References 55 publications

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“…Instead of permanent residents, there will be an increasingly oscillating population. It also means that population registration and taxation systems must be adjusted to this new reality; the sources of available big data could possibly be applied for this purpose (Aasa et al, 2021; Taltech, 2022).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Estonian population and regional development during the last 30 years—back to the small town?

Raagmaa

2023

Regional Science Policy & Practice

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This article reviews the socioeconomic factors that have shaped Estonia's regional population development during the last 30 years. In the 1990s, primary and secondary industries declined and massive urbanization started, depopulating rural and old industrial areas while suburban sprawl developed around the capital Tallinn. Urban growth accelerated in the 2000s, and the 2008 global financial crisis prompted migration from the peripheries. Since 2015, Estonia's population has been growing thanks to returning emigrants and new immigrants. Recent years have witnessed the spread effect and spatial oscillation boosted by COVID‐19 and the Ukrainian war. Additionally, a growing number of people live temporarily in multiple places, including remote rural localities.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Estonian population and regional development during the last 30 years—back to the small town?

Raagmaa

2023

Regional Science Policy & Practice

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“…Therefore, as a first step, we mapped all the antennas to their respective BS based on their latitude (LAT) and longitude (LON) information. Next, we followed the technique mentioned in [9], [10] for the areal weighted interpolation of IRIS. We begin by modeling the coverage area of each BS using the Voronoi polygon.…”

Section: Areal Weighted Interpolation Of Irismentioning

confidence: 99%

Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France

Goel¹,

Furno²,

Sharma³

2023

Preprint

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Socio-economic indicators provide context for assessing a country's overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is critical for social research and government policing. Most data sources available today, such as censuses, have sparse population coverage or are updated infrequently. Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators. in This work investigates mobile app data to predict socioeconomic features. We present a large-scale study using data that captures the traffic of thousands of mobile applications by approximately 30 million users distributed over 550,000 km 2 and served by over 25,000 base stations. The dataset covers the whole France territory and spans more than 2.5 months, starting from 16 th March 2019 to 6 th June 2019. Using the app usage patterns, our best model can estimate socio-economic indicators (attaining an R-squared score upto 0.66). Furthermore, using models' explainability, we discover that mobile app usage patterns have the potential to reveal socio-economic disparities in IRIS 1 . Insights of this study provide several avenues for future interventions, including user temporal network analysis to understand evolving network patterns and exploration of alternative data sources.

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“…Compared with traditional data, big data on population flow provide accurate spatial and temporal information. Spatial-and-temporalbehavior big datasets make it possible to study the interaction between human activities and geospatial space in a larger study area with finer spatial units and more continuous time observations [28][29][30]. It has become an important trend to study the interactive process and mechanism of the human-land relationship using geographic big data.…”

Section: Introductionmentioning

confidence: 99%

Influence of Intercity Network on Land Comprehensive Carrying Capacity: A Perspective of Population Flow

Shi,

Yu,

Wang

et al. 2023

Land

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The world is experiencing the largest wave of urban growth in history. Maintaining the rapid growth of cities without causing land and resource shortages is a severe problem that must be solved urgently. With the rapid development of globalization and information technology, the meaning of land comprehensive carrying capacity presents new changes. It is no longer entirely dependent on local resources and is likely to benefit from intercity connections beyond urban boundaries. However, can an inter-city network be a non-local solution to sustain urban growth without increasing land pressure? To address this question, this study adopted 287 cities in China as the research object to describe the spatial carrying characteristics of land at the national level by constructing an evaluation index system for land comprehensive carrying capacity. Furthermore, we constructed a population flow network model through social network analysis to explore the influence of intercity network on land comprehensive carrying capacity. Our findings are as follows: (1) The regional differentiation characteristics of land comprehensive carrying capacity at the national scale are evident, and reveal a spatial pattern significantly related to the urban economic development level. (2) The weighted in-degree, weighted degree centrality, and betweenness centrality in the intercity network positively impact the land comprehensive carrying capacity, and land use efficiency has a partial mediating effect. (3) Land comprehensive carrying capacity can be determined by non-local factors rather than local factors. As an effective non-local channel, strengthening intercity population flow and network integration can flexibly manage urban land scarcity.

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Spatial interpolation of mobile positioning data for population statistics

Cited by 13 publications

References 55 publications

Estonian population and regional development during the last 30 years—back to the small town?

Estonian population and regional development during the last 30 years—back to the small town?

Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France

Influence of Intercity Network on Land Comprehensive Carrying Capacity: A Perspective of Population Flow

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