Spatial data science

The field of data science has had a significant impact in both academia and industry, and with good reason [...]This research was partially funded by the Portuguese Foundation for Science and Technology (FCT),under projects IPSTERS (DSAIPA/AI/0100/2018), and foRESTER (PCIF/SSI/0102/2017)

Multi-criteria spatial analysis with machine learning algorithm : an application in the South of Brazil

This paper explores a multicriteria spatial analysis methodology with a machine learning algorithm, the Classification Tree Analysis (CTA) within Idrisi GIS, to classify and identify homogeneous regions. The proposed approach is tested in a case study carried out in the South of Brazil. All the municipalities were classified and grouped within areas according to similar condition of urban preponderance in socioeconomic and environmental indicators. The results are evaluated and compared with two other methodologies previously implement by the authors: (a) a ranking of municipality through an aggregate index; and (b) using Kohonen´s Self-Organizing Map (SOM) as an unsupervised classifier. The identification of similar areas with analogous socioeconomic and environmental characteristics is important to the development of regional and municipal common sustainable strategies and advances in municipality partnerships

Digital Government: Knowledge Management Over Time-Varying Geospatial Datasets

Spatially-related data is collected by many government agencies in various formats and for various uses. This project seeks to facilitate the integration of these data, thus providing new uses. This will require the development of a knowledge management framework to provide syntax, context, and semantics, as well as exploring the introduction of time-varying data into the framework. Education and outreach will be part of the project through the development of an on-line short courses related to data integration in the area of geographical information systems. The grantees will be working with government partners (National Imagery and Mapping Agency, the National Agricultural Statistics Service, and the US Army Topographic Engineering Center), as well as an industrial organization, Base Systems, and the non-profit OpenGIS Consortium, which works closely with vendors of GIS products

Spatial Clustering Using Hierarchical SOM

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Towards a Vectorial Approach to Predict Beef Farm Performance

Abbona, F., Vanneschi, L., & Giacobini, M. (2022). Towards a Vectorial Approach to Predict Beef Farm Performance. Applied Sciences, 12(3), 1-16. [1137]. https://doi.org/10.3390/app12031137 ------------------------------------ Funding: This work was partially supported by FCT, Portugal, through funding of projects BINDER (PTDC/CCI-INF/29168/2017) and AICE (DSAIPA/DS/0113/2019).Accurate livestock management can be achieved by means of predictive models. Critical factors affecting the welfare of intensive beef cattle husbandry systems can be difficult to be detected, and Machine Learning appears as a promising approach to investigate the hundreds of variables and temporal patterns lying in the data. In this article, we explore the use of Genetic Programming (GP) to build a predictive model for the performance of Piemontese beef cattle farms. In particular, we investigate the use of vectorial GP, a recently developed variant of GP, that is particularly suitable to manage data in a vectorial form. The experiments conducted on the data from 2014 to 2018 confirm that vectorial GP can outperform not only the standard version of GP but also a number of state-of-the-art Machine Learning methods, such as k-Nearest Neighbors, Generalized Linear Models, feed-forward Neural Networks, and long- and short-term memory Recurrent Neural Networks, both in terms of accuracy and generalizability. Moreover, the intrinsic ability of GP in performing an automatic feature selection, while generating interpretable predictive models, allows highlighting the main elements influencing the breeding performance.publishersversionpublishe

Mapping and discrimination of soya bean and corn crops using spectro-temporal profiles of vegetation indices

The use of remote-sensing technology has been studied as a way to make the monitoring of agricultural crops more efficient, dynamic, and reliable. The use of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has proved to be an interesting tool regarding the mapping of large areas, however, some challenges still need to be addressed. One of these is the identification of specific types of crops, especially when they have similar phenologies. The purpose of this study was to perform discrimination and mapping of soya bean and corn crops in the state of Parana, Brazil, for the 2010/2011 and 2011/2012 crop years. A methodology using spectro-temporal profile information of the crops derived from vegetation indices (VIs), the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the wide dynamic range vegetation index (WDRVI) based on MODIS data was appraised. This method generated a series of maps of the respective crops that were later qualitatively or quantitatively appraised. Some of the maps drawn showed a global accuracy rate above 80% and a kappa coefficient (kappa) of over 0.7. The data areas showed an average difference of 6% for the cultivation of soya beans, and 11% for corn when compared to official data. The WDRVI and EVI were similar and showed better performance when compared to the NDVI in the assessments made. The results demonstrate that the soya bean crop was better mapped compared to corn, particularly in terms of the size of the crop area. The use of spectro-temporal profiles of the VIs assisted in obtaining important information, enabling better identification of crops from regional scale mapping using the MODIS data36718091824CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPE

Rules Britannia: Board Games, Britain, and the World, c. 1759-1860

Focusing on Georgian and Victorian Britain, this thesis examines didactic boardgames as cultural artefacts exploring the bounds of moral sympathy and responsibility in an ostensibly Anglocentric world. It refutes previous conclusions that exposure to imperial ideology via these games in childhood necessarily led to an imperialist identity in adulthood and thence to imperialist activity later in the nineteenth century, highlighting instead how games encouraged players to question the appropriateness of affiliating oneself with the British imperial project by accounting for circumstantial differences at home and abroad. It defies a hypodermic model of communication which posits players as passive and highly susceptible to manipulation by demonstrating instances of player modifications to rules and/or content that, in changing the values and assumptions of the original game, suggest what contemporaries found to be objectionable or missing in standard gameplay. It examines this dialectic between game and player across four thematic categories: teleological games, geographical games, ethnographic games, and zoological games

The Robert E. Gard Reader : To Change the Face of America, From Writings by Robert E. Gard

This Reader draws from the works of Robert E. Gard, professor at the University of Wisconsin, Extension. His chief areas of activity were in the theatre arts and in creative writing, with a strong side activity in collecting and publishing the folklore of the state. He established the functional area of arts development under University Extension and remained a specialist in the arts in smaller communities and rural areas.https://engagedscholarship.csuohio.edu/scholbks/1000/thumbnail.jp