Journal of Spatial Information Science

Estimating scenic beauty in Chinese villages: a novel approach based on 3D real scene models

He Wu, Wen Dai, Chun Wang, Yiyi Cen, Wenzheng Jia, Yu Tao, Mengtian Fan — Thu, 26 Dec 2024 00:00:00 +0000

The color landscape is an essential aspect of each village, representing both natural scenery and human history. However, previous research has not provided a thorough and quantitative assessment of the color spatial pattern of regions and their surroundings. In this study, color patches were extracted from 3D real scene models, and color landscape indices were used to quantify the color landscape pattern. A questionnaire was utilized to establish the association between the color landscape indices and the Scenic Beauty Estimation (SBE) scores, which was then used to predict the SBE without the need for another questionnaire. The results showed that: 1) the color landscape indices extracted using 3D real scene models can reveal the scenic beauty of villages, with different villages presenting various color landscape patterns; 2) the SBE scores obtained through the questionnaire have a strong correlation with various color landscape indices, such as COHESION, LPI, SPILT, Y-MPS, and GE-MPS; 3) the SBE model based on color landscape indices was developed using stepwise linear regression, with an R2 value of 0.822 and an average error of 0.248, which can predict SBE in various places without the use of a questionnaire. This study introduces a new perspective and approach for estimating scenic beauty, which will help with rural planning and beautiful countryside development.

Enhancing urban vitality: integrating traditional metrics with big data and socio-economic insights

Kofoworola Modupe Osunkoya, Jenni Partanen — Thu, 26 Dec 2024 00:00:00 +0000

A city is an intricate system where interactions between transport, land use, the environment, and the population occur at various scales. This complexity makes it challenging to predict and govern these interactions. However, big data on human activity patterns allows researchers to discover dynamic, temporary patterns in the activity landscape and understand the choreographies of people's behavior to enhance urban areas' vitality through planning. In this article, we hypothesized that a higher diversity of urban spatio-functional and socio-economic features indicates higher urban vitality in Tallinn, Estonia. We explored multi-sourced indexes to interpret this formation of urban vitality using complex agent variables of location, cluster, diversity, and similar actors generating self-organizing patterns of urban life. We used functional and morphological components and socio-economic data identified as traditional, `slow' vitality measures (SM), and mobile phone location data as dynamic metrics (DM), respectively. We analyzed them in a geographic information system (GIS) environment to measure the types of spatial configurations, temporal variation of vital places, and their correlation. The results indicate a positive correlation (r=0.5116) between the slow metrics and the high mobile phone activity. These correlations demonstrate that cell phone data provides a detailed and accurate view of people's daily rhythms and choreographies. The diversity indicators offer a new method to interpret urban vitality in cities and make planning decisions that support its emergence.

Predicting the geolocation of tweets using transformer models on customized data

Kateryna Lutsai, Christoph H. Lampert — Thu, 26 Dec 2024 00:00:00 +0000

This research is aimed to solve the tweet/user geolocation prediction task and provide a flexible methodology for the geo-tagging of textual big data. The suggested approach implements neural networks for natural language processing (NLP) to estimate the location as coordinate pairs (longitude, latitude) and two-dimensional Gaussian Mixture Models (GMMs). The scope of proposed models has been finetuned on a Twitter dataset using pretrained Bidirectional Encoder Representations from Transformers (BERT) as base models. Performance metrics show a median error of fewer than 30 km on a worldwide-level, and fewer than 15 km on the US-level datasets for the models trained and evaluated on text features of tweets' content and metadata context. Our source code and data are available at https://github.com/K4TEL/geo-twitter.git.

GeoAI for Science and the Science of GeoAI

Fri, 20 Sep 2024 00:00:00 +0000

This paper reviews trends in GeoAI research and discusses cutting-edge advances in GeoAI and its roles in accelerating environmental and social sciences. It addresses ongoing attempts to improve the predictability of GeoAI models and recent research aimed at increasing model explainability and reproducibility to ensure trustworthy geospatial findings. The paper also provides reflections on the importance of defining the "science" of GeoAI in terms of its fundamental principles, theories, and methods to ensure scientific rigor, social responsibility, and lasting impacts.