Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Spatial-temporal distribution and key factors of urban land use ecological efficiency in the Loess Plateau of China

Urban land use ecological efficiency is crucial to the urbanization process and urban ecosystem sustainability. Cities in ecologically sensitive zones with frequent natural disasters need more complex land use patterns and plans. Achieving the goal of harmonizing economy and ecosystem is key for sustainable development policy makers in these cities. Aiming to explore the urban land use ecological efficiency (LUEE) of ecologically sensitive areas, urban land use ecological efficiency index system of the Loess Plateau was constructed, the SBM-Tobit model was adopted to measure the LUEE and influencing factors from 2009 to 2018, and the characteristics of spatial-temporal evolution was discussed. The results indicated that there were significant spatial differences of LUEE in ecologically sensitive zone. The high-level cities of LUEE were located in the southwest areas, while low-level cities of LUEE were mostly situated in the northeast zones, and the temporal variation trend showed the characteristic of "W" curve. Additionally, the results of key factors identification demonstrated that science and technology expenditure and public transport development had positive effects on urban LUEE, while the land expansion, GDP growth, the second industry and real estate development will limit the improvement of urban LUEE. This study used the scientific evaluation index system and key factors identification method to explore the land use ecological efficiency in ecologically sensitive zones, aiming to provide a case study reference for urban land management and optimization in ecologically fragile areas.

Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam

Population growth is one factor relevant to land-use transformation and expansion in urban areas. This creates a regular mission for local governments in evaluating land resources and proposing plans based on various scenarios. This paper discussed the future trend of three kinds of land-use in the five central coast provinces. Afterwards, the paper deployed machine learning such as Multivariate Adaptive Regression Splines (MARS), Random Forest Regression (RFR), and Lasso Linear Regression (LLR) to analyze the trend of rural land use and industrial land-use to urban land-use in the Central Coast Region of Vietnam. The input variables of land-use from 2010 to 2020 were obtained by the five provinces of the Department of Natural Resources and Environment (DONRE). The results showed that these models provided pieces of information about the relationship between urban, rural, and industrial land-use change data. Furthermore, the MARS model proved to be accurate in the Quang Binh, Quang Tri, and Quang Nam provinces, whereas RFR demonstrated efficiency in the Thua Thien-Hue province and Da Nang city in the fields of land change prediction. Furthermore, the result enables to support land-use planners and decision-makers to propose strategies for urban development.