Eric Shook
Biographic Data
| ID | 360917 |
|---|---|
| NAME | Eric Shook |
| GIVEN NAMES | Eric |
| FAMILY NAME | Shook |
| SIGNATURE | SHOOK E |
| AFFILIATIONS | University of Minnesota |
| ORCID | 0000-0002-8144-5022 |
| VERIFIED | Yes |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 5 |
| WORKS AS AUTHOR | 10 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 1 |
Data Science
Data science represents a new interdisciplinary field that is situated at the intersection of computer science and information technology (IT), math and statistics, and domain knowledge, such as geography. This field aims to use powerful computational techniques to extract useful information and insights from large amounts of structured and unstructured data. Spatial data science applies new and established concepts and techniques from geography …
Introduction to Big Data Computing for Geospatial Applications
The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for …
Developing the Raster Big Data Benchmark
Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allow researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial anal…
Methodological Issues of Spatial Agent-Based Models
Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. A er several decades of progress, ABMs remain di icult to develop and use for many students, scholars, and policy makers. Thi…
Using word embeddings to generate data-driven human agent decision-making from natural language
Generating replicable and empirically valid models of human decision-making is crucial for the scientific accuracy and reproducibility of agent-based models. A two-fold challenge in developing models of decision-making is a lack of high resolution and high quality behavioral data and the need for more transparent means of translating these data into models. A common and largely successful approach to modeling is hand-crafting agent decision heuri…
Assessing the Geographic Context of Risk Perception and Behavioral Response to Potential Ebola Exposure
The 2014−2016 Ebola Virus Disease (EVD) epidemic outbreak reached over 28,000 cases and totaled over 11,000 deaths with 4 confirmed cases in the United States, which sparked widespread public concern about nationwide spread of EVD. Concern was elevated in locations connected to the infected people, which included Kent State University in Kent, Ohio. This threat of exposure enabled a unique opportunity to assess self-reported knowledge about EVD, …
The socio-environmental data explorer (Sede)
Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to socia…
Context and Spatial Nuance Inside a Neighborhood's Drug Hotspot
New geographic approaches are required to tease apart the underlying sociospatial complexity of neighborhood decline to target appropriate interventions. Typically maps of crime hotspots are used with relatively little attention being paid to geographic context. This article helps further this discourse using a topical study of a neighborhood drug microspace, a phrase we use to include the various stages of production, selling, acquiring, and tak…
Mapping in the cloud
Michael P. Peterson integrates cartographic principles with web-based mapping practices in Mapping in the Cloud. In 23 chapters that alternate between theory and practice, this book broadly overvie
Mapping the global Twitter heartbeat
In just under seven years, Twitter has grown to count nearly 3% of the entire global population among its active users who have sent more than 170 billion 140-character messages. Today the service plays such a significant role in American culture that the Library of Congress has assembled a permanent archive of the site back to its first tweet, updated daily. With its open API, Twitter has become one of the most popular data sources for social re…
Context and Spatial Nuance Inside a Neighborhood's Drug Hotspot
New geographic approaches are required to tease apart the underlying sociospatial complexity of neighborhood decline to target appropriate interventions. Typically maps of crime hotspots are used with relatively little attention being paid to geographic context. This article helps further this discourse using a topical study of a neighborhood drug microspace, a phrase we use to include the various stages of production, selling, acquiring, and tak…
The socio-environmental data explorer (Sede)
Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to socia…
Mapping the global Twitter heartbeat
In just under seven years, Twitter has grown to count nearly 3% of the entire global population among its active users who have sent more than 170 billion 140-character messages. Today the service plays such a significant role in American culture that the Library of Congress has assembled a permanent archive of the site back to its first tweet, updated daily. With its open API, Twitter has become one of the most popular data sources for social re…
Mapping in the cloud
Michael P. Peterson integrates cartographic principles with web-based mapping practices in Mapping in the Cloud. In 23 chapters that alternate between theory and practice, this book broadly overvie
The socio-environmental data explorer (Sede)
Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to socia…
Context and Spatial Nuance Inside a Neighborhood's Drug Hotspot
New geographic approaches are required to tease apart the underlying sociospatial complexity of neighborhood decline to target appropriate interventions. Typically maps of crime hotspots are used with relatively little attention being paid to geographic context. This article helps further this discourse using a topical study of a neighborhood drug microspace, a phrase we use to include the various stages of production, selling, acquiring, and tak…
Using word embeddings to generate data-driven human agent decision-making from natural language
Generating replicable and empirically valid models of human decision-making is crucial for the scientific accuracy and reproducibility of agent-based models. A two-fold challenge in developing models of decision-making is a lack of high resolution and high quality behavioral data and the need for more transparent means of translating these data into models. A common and largely successful approach to modeling is hand-crafting agent decision heuri…
Assessing the Geographic Context of Risk Perception and Behavioral Response to Potential Ebola Exposure
The 2014−2016 Ebola Virus Disease (EVD) epidemic outbreak reached over 28,000 cases and totaled over 11,000 deaths with 4 confirmed cases in the United States, which sparked widespread public concern about nationwide spread of EVD. Concern was elevated in locations connected to the infected people, which included Kent State University in Kent, Ohio. This threat of exposure enabled a unique opportunity to assess self-reported knowledge about EVD, …
Introduction to Big Data Computing for Geospatial Applications
The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for …
Developing the Raster Big Data Benchmark
Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allow researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial anal…
Methodological Issues of Spatial Agent-Based Models
Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. A er several decades of progress, ABMs remain di icult to develop and use for many students, scholars, and policy makers. Thi…
Data Science
Data science represents a new interdisciplinary field that is situated at the intersection of computer science and information technology (IT), math and statistics, and domain knowledge, such as geography. This field aims to use powerful computational techniques to extract useful information and insights from large amounts of structured and unstructured data. Spatial data science applies new and established concepts and techniques from geography …
Computer Science (9 works) · Data science (9 works) · Geography (9 works) · Cartography (6 works) · Geographic Information Systems Studies (4 works) · Artificial Intelligence (3 works) · Data mining (3 works) · Geospatial analysis (3 works) · Big data (2 works) · Data Management and Algorithms (2 works)