Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

Geoprocess of geospatial urban data in Tallinn, Estonia

The new digital era brings increasingly massive and complex interdisciplinary projects in various fields. At the same time, the availability of an accurate and reliable database plays a crucial role in achieving project goals. Meanwhile, urban projects and issues usually need to be analyzed to support the objectives of sustainable development of the built environment. Furthermore, the volume and variety of spatial data used to describe urban elements and phenomena have grown exponentially in recent decades. The scope of this dataset is to process spatial data to provide input data for the urban heat island (UHI) assessment project in Tallinn, Estonia. The dataset builds the generative, predictive, and explainable machine learning UHI model. The dataset presented here consists of multi-scale urban data. It provides essential baseline information for (i) urban planners, researchers, and practitioners to incorporate urban data in their research activities, (ii) architects and urban planners to improve the features of buildings and the city, considering urban data and the UHI effect, (iii) stakeholders, policymakers and administration in cities implementing built environment projects, and supporting urban sustainability goals. The dataset is available for download as supplementary material to this article.

Spatial and temporal characteristics of rural livability and its influencing factors: implications for the development of rural revitalization strategy

Building livable rural regions is a key objective of China's rural revitalization, and fostering coordinated and sustainable regional development in China necessitates investigating the geographical and temporal distribution and influencing variables of livability in China's rural areas. This paper compares the concept and connotation of rural livability based on regional economic development theory and sustainable development theory, integrates China's rural revitalization strategy, proposes an index system for evaluating rural livability in China, and conducts an empirical analysis of livable rural areas. Firstly, the level of rural livability development is measured by the entropy method; secondly, the aggregation effect of provinces is demonstrated by using the Moran I index, and finally, the spatial Durbin model is used to explore the influencing factors of rural livability and its spatial spillover effects. Findings of our study reveal that the results of the entropy method indicate that the livability of rural China is in a growing development trend in all provinces, but the livability shows some regional disparities. The Moran index reveals that there are obvious spatial autocorrelations in the livability of Chinese rural areas, and the most typical features are the "high" and "low" clusters. The spatial Durbin model suggests that the influences on the development of rural livability are diverse. Besides, other variables such as environmental pollution and per capita housing area show a negative association with rural livability, and investment in fixed assets, cultural atmosphere and medical health show a positive relationship with rural livability. Based on the study findings, it is proposed that in the rural revitalization strategy process, the formulation of monitoring system should be strengthened and intensify the promotion of coordinated inter-regional development.

Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features

Lumpy skin disease virus (LSDV) causes an infectious disease in cattle. Due to its direct relationship with the survival of arthropod vectors, geospatial and climatic features play a vital role in the epidemiology of the disease. The objective of this study was to assess the ability of some machine learning algorithms to forecast the occurrence of LSDV infection based on meteorological and geological attributes. Initially, ExtraTreesClassifier algorithm was used to select the important predictive features in forecasting the disease occurrence in unseen (test) data among meteorological, animal population density, dominant land cover, and elevation attributes. Some machine learning techniques revealed high accuracy in predicting the LSDV occurrence in test data (up to 97%). In terms of area under curve (AUC) and F1 performance metric scores, the artificial neural network (ANN) algorithm outperformed other machine learning methods in predicting the occurrence of LSDV infection in unseen data with the corresponding values of 0.97 and 0.94, respectively. Using this algorithm, the model consisted of all predictive features and the one which only included meteorological attributes as important features showed similar predictive performance. According to the findings of this research, ANN can be used to forecast the occurrence of LSDV infection with high precision using geospatial and meteorological parameters. Applying the forecasting power of these methods could be a great help in conducting screening and awareness programs, as well as taking preventive measures like vaccination in areas where the occurrence of LSDV infection is a high risk.

Exploring the Spatiotemporal Patterns of Residents’ Daily Activities Using Text-Based Social Media Data: A Case Study of Beijing, China

The use of social media data provided powerful data support to reveal the spatiotemporal characteristics and mechanisms of human activity, as it integrated rich spatiotemporal and textual semantic information. However, previous research has not fully utilized its semantic and spatiotemporal information, due to its technical and algorithmic limitations. The efficiency of the deep mining of textual semantic resources was also low. In this research, a multi-classification of text model, based on natural language processing technology and the Bidirectional Encoder Representations from Transformers (BERT) framework is constructed. The residents’ activities in Beijing were then classified using the Sina Weibo data in 2019. The results showed that the accuracy of the classifications was more than 90%. The types and distribution of residents’ activities were closely related to the characteristics of the activities and holiday arrangements. From the perspective of a short timescale, the activity rhythm on weekends was delayed by one hour as compared to that on weekdays. There was a significant agglomeration of residents’ activities that presented a spatial co-location cluster pattern, but the proportion of balanced co-location cluster areas was small. The research demonstrated that location conditions, especially the microlocation condition (the distance to the nearest subway station), were the driving factors that affected the resident activity cluster patterns. In this research, the proposed framework integrates textual semantic analysis, statistical method, and spatial techniques, broadens the application areas of social media data, especially text data, and provides a new paradigm for the research of residents’ activities and spatiotemporal behavior.