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Articles 1 - 11 of 11
Full-Text Articles in Entire DC Network
Gis Librarians' Involvement In Critical Information Literacy Instruction, Melissa Chomintra, Pete E. Pascuzzi
Gis Librarians' Involvement In Critical Information Literacy Instruction, Melissa Chomintra, Pete E. Pascuzzi
Libraries Faculty and Staff Scholarship and Research
Critical information literacy (CIL) is a theory and practice that considers the sociopolitical dimensions of information and knowledge production. As the field of geospatial information evolves, equipping librarians with the skills to critically engage with geospatial information becomes integral to facilitating informed research, promoting spatial literacy, and strengthening the accessibility of geospatial data for their user communities. To understand geospatial data librarians’ current involvement in CIL, librarians were surveyed to examine if they incorporate CIL into their instruction, how they incorporate CIL into their instruction, and what are the benefits and challenges of doing so. The survey gathered both quantitative …
Towards The Next Generation Of Geospatial Artificial Intelligence, Gengchen Mai, Yiqun Xie, Xiaowei Jia, Ni Lao, Jinmeng Rao, Qing Zhu, Zeping Liu, Yao-Yi Chiang, Jiao, Junfeng
Towards The Next Generation Of Geospatial Artificial Intelligence, Gengchen Mai, Yiqun Xie, Xiaowei Jia, Ni Lao, Jinmeng Rao, Qing Zhu, Zeping Liu, Yao-Yi Chiang, Jiao, Junfeng
Research Collection College of Integrative Studies
Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote …
Making Slums Legible, Visible And Calculable: Geospatial Technologies And The Governance Of Urban Land In Mumbai, Sanjana Krishnan
Making Slums Legible, Visible And Calculable: Geospatial Technologies And The Governance Of Urban Land In Mumbai, Sanjana Krishnan
Theses and Dissertations--Geography
This dissertation examines how geospatial technologies are used to make slums in Mumbai legible, calculable, and hypervisible, and how these practices reshape urban land governance. It situates mapping, remote sensing, and algorithmic systems within their political-economic contexts, showing how technologies intending to see the city often function as instruments of dispossession. The research has three interconnected empirical chapters. The first empirical chapter traces the history of slum mapping in Mumbai, from their absences in early development plans to their selective hypervisibility under the contemporary governance regimes. Using critical cartographic methods, it highlights how novel technologies such as remote sensing, biometric …
Exploring Inequality At Copan, Honduras: A 2d And 3d Geospatial Comparison Of Household Wealth, Heather Richards-Rissetto
Exploring Inequality At Copan, Honduras: A 2d And 3d Geospatial Comparison Of Household Wealth, Heather Richards-Rissetto
Department of Anthropology: Faculty Publications
The archaeological site of Copan was a cultural and commercial crossroads at the southeastern Maya frontier. Research indicates that the demographics and sociopolitical circumstances of the city of Copan and its location within a circumscribed pocket (24 km2) of the larger Copan Valley varied through time. These circumstances not only influenced its social, political, and economic interactions, but likely the size, construction, and organization of households, specifically plazuelas. Copan’s plazuelas differ from those located in other Maya regions because they often have smaller house platforms, comprise more than a single patio, and exhibit a larger than normal …
‘Big’ And ‘Little’ Quo Vadis? In The United States, 1913–1916: Using Gis To Map Rival Modes Of Feature Cinema During The Transitional Era, Jeffrey Klenotic
‘Big’ And ‘Little’ Quo Vadis? In The United States, 1913–1916: Using Gis To Map Rival Modes Of Feature Cinema During The Transitional Era, Jeffrey Klenotic
Faculty Publications
This article emanates from a geospatial database of over 600 premieres of the Cines company’s Quo Vadis? (1913), an eight-reel film distributed by George Kleine, and nearly 250 premieres of the Quo Vadis Film Company’s Quo Vadis? (1913), a three-reel film of ambiguous origins distributed by Paul De Outo. By mapping local premieres of both films across the United States from 1913 through 1916, the data show with spatiotemporal precision the spread of Quo Vadis? as one of cinema’s early blockbuster titles. Yet within this national phenomenon, the two films’ footprints reveal differing cultural geographies served by competing efforts to …
Machine Learning & Big Data Analyses For Wildfire & Air Pollution Incorporating Gis & Google Earth Engine, Abdullah Al Saim
Machine Learning & Big Data Analyses For Wildfire & Air Pollution Incorporating Gis & Google Earth Engine, Abdullah Al Saim
Graduate Theses and Dissertations
The climatic condition, the vegetation type, and the landscape of the United States have made it susceptible to wildfires. This research is divided into two parts based on the analysis of two different aspects of wildfires of two distinct regions. The first part of the study investigates the wildfire susceptibility in Arkansas. Arkansas is a natural state, and it is heavily dependent on its forest and agricultural resources. During the last 30 years, more than 1,000 wildfires occurred in Arkansas and caused more than 10,000 acres of burned areas. Therefore, identifying wildfire-susceptible areas is crucial for ensuring sustainable forest and …
Mapping Maya Hinterlands: Lidar Derived Visualization To Identify Small Scale Features In Northwestern Belize, Jeremy Mcfarland, Marisol Cortes-Rincon Ph.D.
Mapping Maya Hinterlands: Lidar Derived Visualization To Identify Small Scale Features In Northwestern Belize, Jeremy Mcfarland, Marisol Cortes-Rincon Ph.D.
Humboldt Journal of Social Relations
This paper will discuss the processes and methods of relief visualization of LiDAR-derived digital elevation models (DEM’s) and classification of secondary data to identify archaeological remains on the ancient Maya landscape in northwestern Belize. The basis of the research explores various Geographic Information System (GIS) and cartographic techniques to visualize topographical relief. Graphic terrain maps assist archaeologists with predictive settlement patterns. The Relief Visualization Toolbox (RVT 1.3) aids to visualize raster DEM datasets in the predictive identification and interpretation of small-scale archaeological features. This dataset and methodology can be utilized to answer questions of population estimates, mobility costs, and effectiveness …
Using Deep Learning To Forecast Spatiotemporal Crime Patterns, Shuzhan Fan
Using Deep Learning To Forecast Spatiotemporal Crime Patterns, Shuzhan Fan
LSU Doctoral Dissertations
The distributional patterns of crime occurrences are closely related to their spatial, temporal, and environmental contexts. It has been a hot topic for researchers and crime analysts to discover such complex relationships in order to forecast crime, both spatially and temporally. Many factors play a role in the occurrences of crimes. Conventional crime forecasting research has primarily relied on historical crime records and socioeconomic data, while ignoring the rich social media and other environmental context data. The large volume of data requires a more appropriate forecasting framework with the ability to take in massive multimodal data and possibly achieve better …
A Geospatial Study Of The Drought Impact On Surface Water Reservoirs: Study Cases From Texas And California, Zachary Asbury
A Geospatial Study Of The Drought Impact On Surface Water Reservoirs: Study Cases From Texas And California, Zachary Asbury
Graduate Theses and Dissertations
Drought in Texas and California has been a long-term problem. Over the past 60 years reservoir construction has occurred to remedy the situation. Satellite imagery has been used historically to measure and monitor fluctuations in surface water reservoirs. This investigation integrates remote sensing and geographic information system (GIS) technologies to study the impact of drought on selected surface water reservoirs in San Angelo and Dallas in Texas, and Lake Oroville in California. Expansion and shrinkage over the 2005-2016 period reveal the concrete impact that drought, along with other factors, have on the selected lakes. Fluctuations in reservoir sizes during summer …
Spatiotemporal Computing For Enabling Scientific Research And Engineering Development: A Gis Practice, Chaowei Yang
Spatiotemporal Computing For Enabling Scientific Research And Engineering Development: A Gis Practice, Chaowei Yang
Purdue GIS Day
No abstract provided.
Landscape Epidemiology And Machine Learning: A Geospatial Approach To Modeling West Nile Virus Risk In The United States, Sean Gregory Young
Landscape Epidemiology And Machine Learning: A Geospatial Approach To Modeling West Nile Virus Risk In The United States, Sean Gregory Young
Graduate Theses and Dissertations
The complex interactions between human health and the physical landscape and environment have been recognized, if not fully understood, since the ancient Greeks. Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. This theory suggests that pathogenic organisms (whether germs or larger vector and host species) are subject to environmental conditions that can be observed on the landscape, and by identifying where such organisms are likely to exist, areas at greatest risk of the disease can be derived. Machine learning is …