- ItemComparing spatial interaction models and flow interpolation techniques for predicting “cold start” bike-share trip demand(Wiley, 2022-04-21) Liu, Zheng; Oshan, TaylorBike-sharing systems are expanding rapidly in metropolitan areas all over the world and individual systems are updated frequently over space and time to dynamically meet demand. Usage trends are important for understanding bike demand, but an overlooked issue is that of “cold starts” or the prediction of demand at a new station with no previous usage history. In this article, we explore a methodology for predicting the bike trips from and to a cold start station in the NYC Citi Bike system. Specifically, gravity-type spatial interaction model and spatial interpolation models, including natural neighbor interpolation and kriging, are employed. The overall results come from experiments of a real-world bike-sharing system in NYC and indicate that the regression kriging model outperforms the other models by taking advantage of the robustness and interpretability of gravity-type spatial interaction regression models and the capability of ordinary kriging to capture spatial dependence.
- Itemtreetop: A Shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists(Wiley, 2022-06-06) Silva, Carlos Alberta; Hudak, Andrew T.; Vierling, Lee A.; Valbuena, Ruben; Cardil, Adrian; Mohan, Midhun; Alves de Almeida, Danilo Roberti; Broadbent, Eben N.; Almeyda Zambrano, Angelica M.; Wilkinson, Ben; Sharma, Ajay; Drake, Jason B.; Medley, Paul B.; Vogel, Jason G.; Atticciati Prata, Gabriel; Atkins, Jeff W.; Hamamura, Caio; Johnson, Daniel G.; Klauberg, CarineIndividual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR. The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open-source web-based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting-edge analyses of properties related to forest ecology and management. We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post-hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest. treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open-source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function.
- ItemChanging cropland in changing climates: quantifying two decades of global cropland changes(Institute of Physics, 2023-05-12) Kennedy, Jennifer; Hurtt, George C.; Liang, Xin-Zhong; Chini, Louise; Ma, LeiClimate change is impacting global crop productivity, and agricultural land suitability is predicted to significantly shift in the future. Responses to changing conditions and increasing yield variability can range from altered management strategies to outright land use conversions that may have significant environmental and socioeconomic ramifications. However, the extent to which agricultural land use changes in response to variations in climate is unclear at larger scales. Improved understanding of these dynamics is important since land use changes will have consequences not only for food security but also for ecosystem health, biodiversity, carbon storage, and regional and global climate. In this study, we combine land use products derived from the Moderate Resolution Imaging Spectroradiometer with climate reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 to analyze correspondence between changes in cropland and changes in temperature and water availability from 2001 to 2018. While climate trends explained little of the variability in land cover changes, increasing temperature, extreme heat days, potential evaporation, and drought severity were associated with higher levels of cropland loss. These patterns were strongest in regions with more cropland change, and generally reflected underlying climate suitability—they were amplified in hotter and drier regions, and reversed direction in cooler and wetter regions. At national scales, climate response patterns varied significantly, reflecting the importance of socioeconomic, political, and geographic factors, as well as differences in adaptation strategies. This global-scale analysis does not attempt to explain local mechanisms of change but identifies climate-cropland patterns that exist in aggregate and may be hard to perceive at local scales. It is intended to supplement regional studies, providing further context for locally-observed phenomena and highlighting patterns that require further analysis.
- ItemConsiderations for AI-EO for agriculture in Sub-Saharan Africa(Institute of Physics, 2023-03-24) Nakalembe, Catherine; Kerner, HannahAdapting to and mitigating climate change while addressing food insecurity are top priorities in SubSaharan Africa that require technologies to improve rural livelihoods with minimal environmental costs . Artificial intelligence (AI) offers great promise for climate-smart solutions that improve food security outcomes. While precision agriculture is often the foremost use case for AI in agriculture (e.g. automation of farm equipment or nutrient application), precision agriculture is out of reach for most African farmers due to the required capital and infrastructure. AI solutions using satellite Earth observations (EOs), which we call AI-EO, are more accessible in the near term. EO enables agricultural analyses and insights at global scales, and many datasets are freely available, making EO-based solutions affordable . AI-EO-derived products such as crop type maps and yield estimates are necessary to forecast food production surpluses or deficits, inform trade, and aid decisions. These products can support policies that accelerate the design and adoption of climate-smart agriculture and impact farmer livelihoods by increasing access to actionable early warning, risk financing or insurance , farm inputs, markets, and costreducing interventions [2, 4]. Despite their promise, AI-EO solutions for agriculture in Africa are still limited. Most techniques are not generalizable across heterogeneous landscapes. In this paper, we describe the principal sub-fields of research in AI-EO for agriculture in Africa and discuss examples and limitations of existing work. We also propose ten considerations for future work to help increase the impact of AI-EO research in Africa.
- ItemUnderstanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling(Springer Nature, 2023-02-13) Memarsadeghi, Natalie; Stewart, Kathleen; Li, Yao; Sornsakrin, Siriporn; Uthaimongkol, Nichaphat; Kuntawunginn, Worachet; Pidtana, Kingkan; Raseebut, Chatree; Wojnarski, Mariusz; Jongsakul, Krisada; Jearakul, Danai; Waters, Norman; Spring, Michele; Takala-Harrison, ShannonEstimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria exposure risk by analysing the spatial pattern of malaria cases (primarily Plasmodium vivax) in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution of P. vivax malaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes. A maximum entropy model was trained to estimate the distribution of P. vivax malaria for a period between January 2019 and April 2020, capturing estimated malaria occurrence for these provinces. A random simulation workflow was developed to make region-based case data usable for the machine learning approach. This workflow was used to generate a probability surface for the ecological niche regions. The resulting niche regions were analysed by occupation type, home and work locations, and work-related travel routes to determine the relationship between these variables and malaria occurrence. A one-way analysis of variance (ANOVA) test was used to understand the relationship between predicted malaria occurrence and occupation type. The MaxEnt (full name) model indicated a higher occurrence of P. vivax malaria in forested areas especially along the Thailand–Cambodia border. The ANOVA results showed a statistically significant difference between average malaria risk values predicted from the ecological niche model for rubber plantation workers and farmers, the two main occupation groups in the study. The rubber plantation workers were found to be at higher risk of exposure to malaria than farmers in Ubon Ratchathani and Sisaket provinces of Thailand. The results from this study point to occupation-related factors such as work location and the routes travelled to work, being risk factors in malaria occurrence and possible contributors to transmission among local populations.