scholarly journals A Geospatial Thinking Multiyear Study

2020 ◽  
Vol 12 (11) ◽  
pp. 4586
Author(s):  
Carlos Carbonell-Carrera ◽  
Jose Luis Saorin ◽  
Stephany Hess-Medler
Keyword(s):  
Academic Success ◽  
Water Flow ◽  
Spatial Data ◽  
Environmental Sustainability ◽  
Engineering Students ◽  
Stream Water ◽  
Landscape Management ◽  
Data Infrastructure ◽  
Agricultural Engineering ◽  
Geospatial Thinking

In the field of environmental sustainability and landscape management, geospatial thinking is necessary. A good level of geospatial thinking is related to academic success in engineering degrees. It is relevant, therefore, to detect the possible deficiencies that university students may have in tasks related to geospatial thinking. This research presents the results of a 2014‒2019 multiyear study with agricultural engineering students, in which seven geospatial tasks were analyzed. The statistical analysis shows that geospatial tasks related to slope, stream/water flow, visibility, and relief interpretation are the best at predicting the final course mark. The present research provides quantitative data on the efficiency that four technologies have to reinforce geospatial thinking focused on each task. Augmented Reality is an appropriate 3D technology for geospatial tasks related to route search, stream/water flow, and elevation points. SketchUp Make 2017 and Autodesk 123D Make showed their potential to solve tasks related to terrain slope and visibility analysis. Spatial Data Infrastructure has given the best results in geospatial tasks related to the photointerpretation of the relief and with topographic profiles of the terrain. Our findings will help teachers to select the most appropriate geospatial tasks to include in their courses.

scholarly journals Understanding Cumulative Hazards in a Rustbelt City: Integrating GIS, Archaeology, and Spatial History

Urban Science ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 83
Author(s):  
Dan Trepal ◽  
Don Lafreniere
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Environmental Sustainability ◽  
Historical Record ◽  
Decision Makers ◽  
Landscape Model ◽  
Final Model ◽  
Data Infrastructure ◽  
Spatial History ◽  
Spatial Big Data

We combine the Historical Spatial Data Infrastructure (HSDI) concept developed within spatial history with elements of archaeological predictive modeling to demonstrate a novel GIS-based landscape model for identifying the persistence of historically-generated industrial hazards in postindustrial cities. This historical big data approach draws on over a century of both historical and modern spatial big data to project the presence of specific persistent historical hazards across a city. This research improves on previous attempts to understand the origins and persistence of historical pollution hazards, and our final model augments traditional archaeological approaches to site prospection and analysis. This study also demonstrates how models based on the historical record, such as the HSDI, complement existing approaches to identifying postindustrial sites that require remediation. Our approach links the work of archaeologists more closely to other researchers and to municipal decision makers, permitting closer cooperation between those involved in archaeology, heritage, urban redevelopment, and environmental sustainability activities in postindustrial cities.

On the use of environmental maps in creating environmental companty spatial data infrastructure

2016 ◽  
Vol 910 (4) ◽  
pp. 18-25
Author(s):  
S.S. Dyshlyuk ◽  
◽  
O.N. Nikolaeva ◽  
L.A. Romashova ◽  
◽  
...  
Keyword(s):  
Spatial Data ◽  
Spatial Data Infrastructure ◽  
Data Infrastructure

Factors influencing integrated information management: Spatial data infrastructure in Pakistan

2021 ◽  
pp. 026666692110484
Author(s):  
Asmat Ali ◽  
Muhammad Imran ◽  
Munazza Jabeen ◽  
Zahir Ali ◽  
Syed Amer Mahmood
Keyword(s):  
Information Management ◽  
Spatial Data ◽  
Structural Equation ◽  
Explanatory Power ◽  
Primary Data ◽  
Equation Modeling ◽  
Geospatial Information ◽  
Data Infrastructure ◽  
Significance Level ◽  
Retrieval Processes

Spatial data is one of the core components in all information retrieval processes for decision-making. Spatial data acquisition consumes enormous monetary resources and time. The Integrated Geospatial Information Framework (IGIF) provides a basis and guide for developing, integrating, strengthening, and maximizing geospatial information management and related resources in all countries. To this, governments all over the world are establishing national spatial data infrastructures (SDIs). However, such initiatives face a considerable amount of resistance as organizations often do not want to share their data assets. The present study investigates these barriers in the establishment of national SDI in Pakistan. The constraints studied through the IGIF pathways and past studies were adapted via a pilot study and conceptualized in a hypothesized model. We collected primary data via the administration of 520 questionnaire surveys to 280 public and private organizations. Partial least squares structural equation modeling (PLS-SEM) was applied to statistically confirm the conceptual model of the barriers to disseminating spatial data. The results indicate institutional barriers from the absence of national data policy, lack of specified roles of stakeholders, poor inter-organizational coordination, missing data-sharing policy, and weak organizational partnerships, with coefficients 0.26, 1.555, 1.305, 8.288, and 0.136, respectively, at the p < 0.001 significance level. The PLS-SEM R2 0.65 indicates a good explanatory power of the model. The methodology developed in the present study will allow devising more sustainable policies for spatial data management and dissemination in Pakistan and beyond.

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