The ability to rapidly produce accurate land use and land cover maps regularly and consistently has been a growing initiative as they have increasingly become an important tool in the efforts to evaluate, monitor, and conserve Earth’s natural resources. Algorithms for supervised classification of satellite images constitute a necessary tool for the building of these maps and they have made it possible to establish remote sensing as the most reliable means of map generation. In this paper, we compare three machine learning techniques: Random Forest, Support Vector Machines, and Light Gradient Boosted Machine, using a 70/30 training/testing evaluation model. Our research evaluates the accuracy of Light Gradient Boosted Machine models against the more classic and trusted Random Forest and Support Vector Machines when it comes to classifying land use and land cover over large geographic areas. We found that the Light Gradient Booted model is marginally more accurate with a 0.01 and 0.059 increase in the overall accuracy compared to Support Vector and Random Forests, respectively, but also performed around 25% quicker on average.

Human behavior is a dominant factor in road accidents, contributing to more than 70% of such incidents. However, gathering detailed data on individual drivers’ behavior is a significant challenge in the field of road safety. As a result, researchers often narrow the scope of their studies thus limiting the generalizability of their findings. Our study aims to address this issue by identifying demographic-related variables and their indirect effects on road accident frequency. The theoretical basis is set through existing literature linking demographics to risky driving behavior and through the concept of “close to home” effect, finding that the upwards of 62% of accidents happen within 11km of a driver’s home. Using regression-based machine learning models, our study, looking at England, UK, explores the theoretical linkages between demographics of an area and road accident frequency, finding that census data is able to explain over 28% of the variance in road accident rates per capita. While not replacing more in-depth research on driver behavior, this research validates trends found in the literature through the use of widely available data with the use of novel methods. The results of this study support the use of demographic data from the national census that is obtainable at a large spatial and temporal scale to estimate road accident risks; additionally, it demonstrates a methodology to further explore potential indirect relationships and proxies between behaviors and road accident risk.

The urban heat island effect has been studied extensively by many researchers around the world with the process of urbanization coming about as one of the major culprits of the increasing urban land surface temperatures. Over the past 20 years, the city of Dallas, Texas, has consistently been one of the fastest growing cities in the United States and has faced rapid urbanization and great amounts of urban sprawl, leading to an increase in built-up surface area. In this study, we utilize Landsat 8 satellite images, Geographic Information System (GIS) technologies, land use/land cover (LULC) data, and a state-of-the-art methodology combining machine learning algorithms (eXtreme Gradient Boosted models, or XGBoost) and a modern game theoretic-based approach (Shapley Additive exPlanation, or SHAP values) to investigate how different land use/land cover classifications impact the land surface temperature and park cooling effects in the city of Dallas. We conclude that green spaces, residential, and commercial/office spaces have the largest impacts on Land Surface Temperatures (LST) as well as the Park’s Cooling Intensity (PCI). Additionally, we have found that the extent and direction of influence of these categories depends heavily on the surrounding area. By using SHAP values we can describe these interactions in greater detail than previous studies. These results will provide an important reference for future urban and park placement planning to minimize the urban heat island effect, especially in sprawling cities.

The benefits of green infrastructure have been verified at the site-level by many empirical studies. However, there is limited understanding of how local governments prepare and implement green infrastructure planning in practice. This study employs the content analysis method to examine the quality of local comprehensive plans regarding sustainable green infrastructure in 60 municipalities of the United States. The study uses regression analysis to explain the variance of plan quality. Study results indicate that key green infrastructure principles were not fully incorporated in the existing sampled plans, with average score of 19.6 out of 50. While plan quality scores were slightly higher in counties than in cities, both areas could significantly improve plan quality with detailed policies, action strategies, and implementation tactics for green infrastructure planning and management. Regression analysis further identified that planning capacities, as well as socio-economic characteristics of study area may impact overall plan quality. The findings of this study demonstrate the importance of incorporating detailed green infrastructure principles whenever local planners adopt or amend regional plans in order to improve plan quality and to support implementation.

This study uses a developed plan coding protocol in evaluating the quality of 76 comprehensive plans to examine whether local comprehensive plans have adequately integrated the concepts of sustainable stormwater management. The Chesapeake Bay watershed was chosen for the investigation because degraded stormwater runoff from nearby urban and suburban jurisdictions have critically polluted the watershed. The findings indicate that the majority of local governments have not sufficiently incorporated the sustainable stormwater management principles into their comprehensive plans. Five plan components (factual basis, goals and objectives, inter-organizational cooperation, policies, tools and strategies, and implementation) appear weak in realizing the concepts. The current study concludes by providing policy implications and recommendations to increase awareness and understanding of sustainable stormwater management concepts and to produce better implementation plans that integrate stormwater, ecosystem, and environmental planning comprehensively.

This study develops multiple evaluation indexes in the context of sustainable urban regeneration through introducing smart technologies/infrastructures and assesses 63 local urban regeneration strategic plans by using the content analysis method. A total of 107 indexes are developed based on the four aspects (economy, society and culture, environment, and livability) of sustainability. From our findings, the average plan quality score of 54 local governments’ plans is 17.5 out of 50, with the metropolitan governments’ plans averaging 16.8, which indicates that the plans currently sampled do not sufficiently reflect the basic concepts of sustainable and smart urban regeneration. The contents of most of the plans generally focus on specific sectors, such as society, culture, and housing, whereas smart technology-related information and policies are relatively deficient. Among the five plan components (factual bases, goals/objectives, policies/strategies, implementation, coordination) reviewed, the implementation component receives the highest score, while indicators related to action strategies are mentioned least often. In particular, the results reveal that indexes relating to the energy and transportation sectors are not frequently mentioned; as such, each municipality is recommended to work to increase awareness of smart technologies and policies. For urban regeneration projects to be sustainable, multi-faceted policies must be implemented by various stakeholders with a long-term perspective. The results of this study can be used as a base for local planners and decision-makers when adopting and supplementing existing regeneration plans, and can contribute to promoting more sustainable urban regeneration through actively adopting various smart technologies initiatives.