Spatial analysis (Statistics)

This study investigates the spatial disparities in flood risk and social vulnerability across 66,543 census tracts in the Conterminous United States (CONUS), emphasizing urban–rural differences. Utilizing the American Community Survey (ACS) 2016–2020 data, we focused on 16 social factors representing socioeconomic status, household composition, racial and ethnic minority status, and housing and transportation access. Principal Component Analysis (PCA) reduced these variables into five principal components: Socioeconomic Disadvantage, Elderly and Disability, Housing Density and Vehicle Access, Youth and Mobile Housing, and Group Quarters and Unemployment. An additive model created a comprehensive Social Vulnerability Index (SVI). Statistical analysis, including the Mann–Whitney U test, indicated significant differences in flood risk and social vulnerability between urban and rural areas. Spatial cluster analysis using Local Indicators of Spatial Association (LISA) revealed significant high flood risk and social vulnerability clusters, particularly in urban regions along the Gulf Coast, Atlantic Seaboard, and Mississippi River. Global and local regression models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), highlighted social vulnerability’s spatial variability and localized impacts on flood risk. The results showed substantial regional disparities, with urban areas exhibiting higher flood risks and social vulnerability, especially in southeastern urban centers.

Storms in the North Atlantic Ocean are observed on a continual basis yearly. Storm trajectories exhibit random behavior and are costly to society. Data from the National Oceanic and Atmospheric Administration (NOAA) contains every storm’s track from the year 1851 to 2022. Data of each storm’s track can aid in decision making regarding their behavior. In this article, data analysis is performed on historical storm tracks during the years 1966 to 2022, where access to satellite information is available. Analysis on this data will be used to determine if the storms’ trajectory is statistically dependent on other storm’s trajectories at varying distances in space. The proposed model is a spatial statistical model that is fitted on an in-sample data set to determine the spatial relationship for storm trajectories at all pairwise directions or orientations. Afterwards, the model is assessed on an out-of-sample test data set for performance evaluation.

The present study identifies settlement patterns of the Manteño culture within the cloud forest of
southern Manabí by surveying, recording and analyzing the stone architecture found within the drainage
basin of the Las Tusas River, Ecuador. The statistical methods used were: Triangulated Irregular Networks
or TIN (for topography interpretations), K-means (to determine natural groups for structures based on their
dimensions, shape, and wall thickness), Ripley’s K (to determine spatial nature of these groups) and Kernel
Density (to visualize their spatial organization). The cloud forest ecotone of southern Manabí was an
anthropogenic landscape during the late Integration period. The alluvial valleys of the upper Rio Blanco
drainage basin do not represent a hinterland or a periphery occupation but a series of Manteño nucleated
settlements raised on terraces and interconnected by strings of linear settlements and dispersed settlements
throughout the rugged terrain of this landscape.

System modeling has the potential to enhance system design productivity by providing a
platform for system performance evaluations. This model must be designed at an abstract
level, hiding system details. However, it must represent any subsystem or its components
at any level of specification details. In order to model such a system, we will need to
combine various models-of-computation (MOC). MOC provide a framework to model
various algorithms and activities, while accounting for and exploiting concurrency and
synchronization aspects. Along with supporting various MOC, a modeling environment
should also support a well developed library. In this thesis, we have explored various
modeling environments. MLDesigner (MLD) is one such modeling environment that
supports a well developed library and integrates various MOC. We present an overview
and discuss the process of system modeling with MLD. We further present an abstract
model of a Network-on-Chip in MLD and show latency results for various customizable
parameters for this model.

Urbanization is a fundamental reality in the developed and developing countries
around the world creating large concentrations of the population centering on cities and
urban centers. Cities can offer many opportunities for those residing there, including
infrastructure, health services, rescue services and more. The living space density of
cities allows for the opportunity of more effective and environmentally friendly housing,
transportation and resources. Cities play a vital role in generating economic production
as entities by themselves and as a part of larger urban complex. The benefits can provide
for extraordinary amount of people, but only if proper planning and consideration is
undertaken.
Global urbanization is a progressive evolution, unique in spatial location while
consistent to an overall growth pattern and trend. Remotely sensing these patterns from
the last forty years of space borne satellites to understand how urbanization has
developed is important to understanding past growth as well as planning for the future. Imagery from the Landsat sensor program provides the temporal component, it
was the first satellite launched in 1972, providing appropriate spatial resolution needed to
cover a large metropolitan statistical area to monitor urban growth and change on a large
scale. This research maps the urban spatial and population growth over the Miami – Fort
Lauderdale – West Palm Beach Metropolitan Statistical Area (MSA) covering Miami-
Dade, Broward, and Palm Beach counties in Southeast Florida from 1974 to 2010 using
Landsat imagery. Supervised Maximum Likelihood classification was performed with a
combination of spectral and textural training fields employed in ERDAS Image 2014 to
classify the images into urban and non-urban areas. Dasymetric mapping of the
classification results were combined with census tract data then created a coherent
depiction of the Miami – Fort Lauderdale – West Palm Beach MSA. Static maps and
animated files were created from the final datasets for enhanced visualizations and
understanding of the MSA evolution from 60-meter resolution remotely sensed Landsat
images. The simplified methodology will create a database for urban planning and
population growth as well as future work in this area.

Object recognition is imperfect; often incomplete processing or deprived
information yield misperceptions (i.e., misidentification) of objects. While quickly
rectified and typically benign, instances of such errors can produce dangerous
consequences (e.g., police shootings). Through a series of experiments, this study
examined the competitive process of multiple object interpretations (candidates) during
the earlier stages of object recognition process using a lexical decision task paradigm.
Participants encountered low-pass filtered objects that were previously demonstrated to
evoke multiple responses: a highly frequented interpretation (“primary candidates”) and a
lesser frequented interpretation (“secondary candidates”). When objects were presented
without context, no facilitative effects were observed for primary candidates. However,
secondary candidates demonstrated evidence for being actively suppressed.

Spatial and temporal interpolation methods are commonly used methods for estimating missing precipitation rain gauge data based on values recorded at neighboring gauges. However, these interpolation methods have not been comprehensively checked for their ability to preserve time series characteristics. Assessing the preservation of time series characteristics helps achieving a threshold criteria of length of gaps in a data set that is acceptable to be filled. This study evaluates the efficacy of optimal weighting interpolation for estimation of missing data in preserving time series characteristics. Rain gauges in the state of Kentucky are used as a case study. Several model performance measures are also evaluated to validate the filling model; followed by time series characteristics to evaluate the accuracy of estimation and preservation of precipitation data characteristics. This study resulted in a definition of region-specific threshold of the maximum length of gaps allowed in a data set at five percent.

Planners and managers often rely on coarse population distribution data from the
census for addressing various social, economic, and environmental problems. In the
analysis of physical vulnerabilities to sea-level rise, census units such as blocks or block
groups are coarse relative to the required decision-making application. This study
explores the benefits offered from integrating image classification and dasymetric
mapping at the household level to provide detailed small area population estimates at the
scale of residential buildings. In a case study of Boca Raton, FL, a sea-level rise
inundation grid based on mapping methods by NOAA is overlaid on the highly detailed
population distribution data to identify vulnerable residences and estimate population
displacement. The enhanced spatial detail offered through this method has the potential to
better guide targeted strategies for future development, mitigation, and adaptation efforts.

Reliable geographic intelligence is essential for urban areas; land-cover classification creates the data for urban spatial decision making. This research tested a methodology to create a land-cover map for the main campus of Florida Atlantic University in Boca Raton, Florida. The accuracy of nine separate land-cover classification results were tested; the one with the highest accuracy was chosen for the final map. Object-based image segmentation was applied to fused and LiDAR point cloud (elevation and intensity) data and aerial imagery. These were classified by Random Forest, k-Nearest Neighbor and Support Vector Machines classifiers. Shadow features were reclassified hierarchically in order to create a complete map. The Random Forest classifier used with the fused data set gave the highest overall accuracy at 82.3%, and a Kappa value at 0.77. When combined with the results from the shadow reclassification, the overall accuracy increased to 86.3% and the Kappa value improved to 0.82.

This study is a spatial analysis conducted in the Main Chamber of Actun Tunichil Muknal, a Terminal Classic Maya ceremonial cave (A.D. 830--950), located in Western Belize. The research examines ancient Maya ritual cave use by analyzing artifact deposition patterns. Using a Geographical Information System (GIS), it provides a methodology for the development of comparative models of spatial organization. The system facilitated data visualization, exploration, and generation. The GIS was instrumental in the analysis of the proximity of artifacts to natural morphological features of the cave. Artifact deposition patterns were correlated with known ritual behavior patterns from the region. Using this method, boundary markers, artifact pathways, and a centrally located symbolic three-stone-hearth feature were identified. This study suggests that, within the cave, the ancient Maya employed a cognitive model of spatial organization similar to that witnessed by ethnographers in other venues, or reported in ethnohistorical texts in rites of foundation.