The geospatial humanities: past, present and future

Murrieta-Flores, Patricia; Martins, Bruno

doi:10.1080/13658816.2019.1645336

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Furthermore, we have the opportunity to expand the discussion relating to European and global trends by including the Portuguese case in academic discussions, and further contribute to the integration of long spatial time-series in scientific analysis that can strengthen the ability to assess past and current regional food production situations, essential for a country's food security evaluation. One major benefit of having such data is the possibility of conducting an in-depth analysis of the past to truly understand the present [10] and forecast probable future events [11]. A direction for future work would be to combine these statistical data with long-term meteorological data (e.g., ECWMF or GLDAS), as climate change is known to directly affect the variation of crop cultivation.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…As phenomena occur at multiple spatial and temporal scales, the creation of a long spatial time-series is essential, and can strengthen the ability to assess past occurrences via scientific analysis and provide insights in order to form more effective solutions for current and future phenomena faced by society [10][11][12]. Therefore, for different scientific subjects, it is crucial to contour the limitations of statistical data that hamper the creation of a longtime series, and the ability of cross-timescale analysis to detect spatiotemporal patterns and trends, as well as to forecast future events [10,11,13]. Accordingly, considering the importance of agricultural statistical data in reinterpreting both past and current agriculture and food supply situations, this study focused on the Portuguese agricultural statistics data to find solution in order to homogenize the data at the upper scales and to create a long spatial time-series to provide geographical and historical perspectives.…”

Section: Introductionmentioning

confidence: 99%

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Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective

Viana

Freire

Abrantes

et al. 2021

Land

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Agricultural statistical data enable the detection and interpretation of the development of agriculture and the food supply situation over time, which is essential for food security evaluation in any country. Based on the historical agricultural statistics, this study produces a long spatial time-series with annual production values of three cereals relevant to global food security—wheat, maize, and rice, aiming to provide geographical and historical perspectives. Therefore, we reconstructed past and current production patterns and trends at the district level over 169 years, which supported a space–time cross-reading of the general characteristics of the regional agricultural production value distributions and relative densities in Portugal. Particularly, the production trends of wheat, maize, and rice showed three different situations: growth (maize), stability (rice), and decline (wheat). For decades, maize and wheat production alternated, depending on agricultural years and political aspects, such as the Wheat Campaign (1929–1938). The changes over time presented a pattern that, in the case of these three cereals, enabled a clear division of the country into major regions according to cereal production. Overall, maize and rice, both grown on irrigated croplands, presented a similar pattern in some regions of Portugal, mainly the central region. In this study, a preliminary analysis was presented and related to successive public policies; however, notably, there are more lessons to be learned from this long spatial time-series.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective

Viana

Freire

Abrantes

et al. 2021

Land

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“There had been a Great Strength in those Stones”: Materiality and Archaeological Perspectives of Westerosi Fortifications

Collazo

2023

Game of Thrones - A View From the Humanities Vol. 1

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Creating building-level, three-dimensional digital models of historic urban neighborhoods from Sanborn Fire Insurance maps using machine learning

Lin,

Li,

Porr

et al. 2023

PLoS ONE

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Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods.

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The geospatial humanities: past, present and future

Cited by 4 publications

References 15 publications

Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective

Evolution of Agricultural Production in Portugal during 1850–2018: A Geographical and Historical Perspective

“There had been a Great Strength in those Stones”: Materiality and Archaeological Perspectives of Westerosi Fortifications

Creating building-level, three-dimensional digital models of historic urban neighborhoods from Sanborn Fire Insurance maps using machine learning

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