Sensitivity analysis was conducted for all parameters in the non-spatial model, excluding R0 and K which were kept constant, and q and β, which were calculated from other parameters. Sensitivity analysis results are presented in the S2 Text.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

Recent baseball scholarship has drawn attention to U.S. professional baseball’s complex twentieth century labor dynamics and expanding global presence. From debates around desegregation to discussions about the sport’s increasingly multicultural identity and global presence, the cultural politics of U.S. professional baseball is connected to the problem of baseball labor. However, most scholars address these topics by focusing on Major League Baseball (MLB), ignoring other teams and leagues—Minor League Baseball (MiLB)—that develop players for Major League teams. Considering Minor League Baseball is critical to understanding the professional game in the United States, since players who populate Major League rosters constitute a fraction of U.S. professional baseball’s entire labor force. As a digital humanities dissertation on baseball labor and globalization, this project uses digital humanities approaches and tools to analyze and visualize a quantitative data set, exploring how Minor League Baseball relates to and complicates MLB-dominated narratives around globalization and diversity in U.S. professional baseball labor. This project addresses how MiLB demographics and global dimensions shifted over time, as well as how the timeline and movement of foreign-born players through the Minor Leagues differs from their U.S.-born counterparts. This project emphasizes the centrality and necessity of including MiLB data in studies of baseball’s labor and ideological significance or cultural meaning, making that argument by drawing on data analysis, visualization, and mapping to address how MiLB labor complicates or supplements existing understandings of the relationship between U.S. professional baseball’s global reach and “national pastime” claims.

Dataset of house rental advertisments in Persian (LDA and CS code included)

This data was developed to represent city of cape coral citizen action center issues and their associated attributes for the purpose of mapping, analysis, and planning. The accuracy of this data varies and should not be used for precise measurements or calculations.

Thesis. Visit https://dataone.org/datasets/sha256%3Ac1296af90e46149d6ff29e0ed5da442829ef671b5e22508d66b52e09e65201ca for complete metadata about this dataset.

1As there appeared to be no difference in risk between non-artificial GlobCover categories (cultivated land, natural and semi-natural terrestrial vegetation, and natural and semi-natural aquatic vegetation) these were combined for analysis.

Our model is a full-annual-cycle population model {hostetler2015full} that tracks groups of bat surviving through four seasons: breeding season/summer, fall migration, non-breeding/winter, and spring migration. Our state variables are groups of bats that use a specific maternity colony/breeding site and hibernaculum/non-breeding site. Bats are also accounted for by life stages (juveniles/first-year breeders versus adults) and seasonal habitats (breeding versus non-breeding) during each year, This leads to four states variable (here depicted in vector notation): the population of juveniles during the non-breeding season, the population of adults during the non-breeding season, the population of juveniles during the breeding season, and the population of adults during the breeding season, Each vector's elements depict a specific migratory pathway, e.g., is comprised of elements, {non-breeding sites}, {breeding sites}The variables may be summed by either breeding site or non-breeding site to calculate the total population using a specific geographic location. Within our code, we account for this using an index column for breeding sites and an index column for non-breeding sides within the data table. Our choice of state variables caused the time step (i.e. (t)) to be 1 year. However, we recorded the population of each group during the breeding and non-breeding season as an artifact of our state-variable choice. We choose these state variables partially for their biological information and partially to simplify programming. We ran our simulation for 30 years because the USFWS currently issues Indiana Bat take permits for 30 years. Our model covers the range of the Indiana Bat, which is approximately the eastern half of the contiguous United States (Figure \ref{fig:BatInput}). The boundaries of our range was based upon the United States boundary, the NatureServe Range map, and observations of the species. The maximum migration distance was 500-km, which was based upon field observations reported in the literature \citep{gardner2002seasonal, winhold2006aspects}. The landscape was covered with approximately 33,000, 6475-ha grid cells and the grid size was based upon management considerations. The U.S.~Fish and Wildlife Service considers a 2.5 mile radius around a known maternity colony to be its summer habitat range and all of the hibernaculum within a 2.5 miles radius to be a single management unit. Hence the choice of 5-by-5 square grids (25 miles(^2) or 6475 ha). Each group of bats within the model has a summer and winter grid cell as well as a pathway connecting the cells. It is possible for a group to be in the cell for both seasons, but improbable for females (which we modeled). The straight line between summer and winter cells were buffered with different distances (1-km, 2-km, 10-km, 20-km, 100-km, and 200-km) as part of the turbine sensitivity and uncertainty analysis. We dropped the largest two buffer sizes during the model development processes because they were biologically unrealistic and including them caused all populations to go extinct all of the time. Note a 1-km buffer would be a 2-km wide path. An example of two pathways are included in Figure \ref{fig:BatPath}. The buffers accounts for bats not migrating in a straight line. If we had precise locations for all summer maternity colonies, other approaches such as Circuitscape \citep{hanks2013circuit} could have been used to model migration routes and this would have reduced migration uncertainty.

Integrating sounds from the same source and segregating sounds from different sources in an acoustic scene is an essential function of the auditory system. Naturally, the auditory system simultaneously makes use of multiple cues. Here, we investigate the interaction between spatial cues and frequency cues in stream segregation of European starlings (Sturnus vulgaris) using an objective measure of perception. Neural responses to streaming sounds were recorded while the bird was performing a behavioral task that results in a higher sensitivity during a one-stream than a two-stream percept. Birds were trained to detect an onset time shift of a B tone in an ABA- triplet sequence in which A and B could differ in frequency and/or spatial location. If the frequency difference or spatial separation between the signal sources or both were increased, the behavioral time shift detection performance deteriorated. Spatial separation had a smaller effect on the performance compared to the frequency difference and both cues additively affected the performance. Neural responses in the primary auditory forebrain were affected by the frequency and spatial cues. However, frequency and spatial cue differences being sufficiently large to elicit behavioral effects did not reveal correlated neural response differences. The difference between the neuronal response pattern and behavioral response is discussed with relation to the task given to the bird. Perceptual effects of combining different cues in auditory scene analysis indicate that these cues are analyzed independently and given different weights suggesting that the streaming percept arises consecutively to initial cue analysis.

This dataset is a non-spatial table that identifies the Invasive Species of NWSS.The aim of the Native Woodland Survey of Scotland (NWSS) was to undertake a baseline survey of all native woodlands, nearly native woodlands and PAWS sites in Scotland in order to create a woodland map linked to a dataset showing type, extent and condition of those woods. The objectives were to:Identify the location, type, extent and condition of all native and nearly native woodlands and Plantations on Ancient Woodland Sites (PAWS - as identified from the Ancient Woodland Inventory) in Scotland.Produce a baseline survey map of all native woodland, nearly native woodland and PAWS in Scotland.Collect baseline information to enable future monitoring of the extent and condition of the total Scottish native woodland resource.Provide information to support policy development and the delivery of social, environmental and development forestry.The following NWSS datasets are available from Scottish Forestry.Native Woodland Survey of Scotland (base map and polygon level attributes)NWSS Canopy StructureNWSS Habitat ComponentsNWSS Herbivore ImpactNWSS InvasivesNWSS Other TraitsNWSS Species StructuresThe following describes the layers available from Scottish Forestry and also gives an indication of the nature of the spatial data and the related component non-spatial data. (N.B. Every table contains a SCPTDATA_I field. This is a unique field which is used to link all other component tables). If you wish to carry out complex analysis, particularly involving elements of the components tables, e.g. species selection, you should do so using GIS software.NWSS Map:This is a straightforward view of the data which describes the type of NWSS polygon based on the following categories:Native woodland: >50% native species in the canopyNearly-native woodland: >=40% and <=50% native species in the canopyOpen land habitat: <20% canopy cover, usually 100% surrounded by woodland and adjoining a native woodlandPAWS: A woodland area wholly or partially identified in the Ancient Semi-natural Woodland Inventory as ancient semi-natural but currently not semi-natural.NWSS Nativeness:Displays the percentage share of native species in the total canopy. This ranges from 0% to 100% in 5% classes.NWSS Habitat:This view of the data shows the priority woodland type and National Vegetation Classification (NVC) woodland community. Open land habitat is defined by UK Biodiversity Action Plan (BAP) type.A dominant habitat is recorded for each polygon, however some polygons have habitats of equal dominance. In this case only one of the habitats is recorded in the top level spatial data. To identify all of the habitats in a particular polygon please refer to the NWSS Habitat Components table.Plantations on Ancient Woodland Sites (PAWS) may not display in the Habitat layer if a surveyor has not recorded a native priority habitat type for the site. This will happen when a site is non-native.NWSS Canopy Cover:Displays as a percentage, an assessment of the area covered by trees/shrubs. Values range from 0% to 100% in 10% classes. A minimum of 20% canopy cover is required to define woodland, so the 10% and 20% bands are skewed to allow for this.NWSS Canopy Structures:This displays the number of different structures recorded in a polygon (ranging from 0 to 6). The types of recorded structures are veteran, mature, pole immature, shrub, established regeneration or visible regeneration.A dominant structure is recorded for each polygon, however some polygons have structures of equal dominance. In this case only one of the structures is recorded in the top level spatial data. To identify all of the structures in a particular polygon please refer to the NWSS Canopy Structures.Information on the species identified in each polygon is also in the NWSS Canopy Structures layer and table.* indicates a species which is classed as native for the purpose of the survey.+ indicates a species is a shrub not a tree.NWSS Semi-naturalness:This view of the data shows the percentage of the polygon that is semi-natural. Values range from 0% to 100% in 10% bands.NWSS Maturity:This indicates the approximate stage of woodland development as either: mature, young, regenerating, mixed or shrub. The value is based on the dominance of the structures recorded; a mixed maturity means that none of the others values are dominant.NWSS Other Traits:This layer records whether or not there are any other attributes which have been recorded in the polygon. The details of any other traits that have been found can be accessed by viewing the related information attached to a polygon.NWSS Herbivore Impact:This view of the data shows the overall impact that herbivores have had on a polygon.Summary of AttributesSCPTDATA_I Polygon ID (Unique identifier)PAWS_SURVY Surveyed as PAWSTYPE TypeCANOPY_PCT Canopy cover percentageNATIVE_PCT Native species percentageDOM_HABITA Dominant habitat typeDOM_HB_PCT Dominant habitat type percentageSEMINT_PCT Semi-natural percentageSTRUCT_NUM Number of structuresMATURITY MaturityDOM_STRUCT Dominant structureHERBIVORE Herbivore impactER_NAT_PCT Percentage of establish regeneration of native speciesINVASV_PCT Invasive species percentageINVASV_NUM Number of invasive speciesOTHR_TRAIT Other traits recordedHECTARES Area in hectaresFor more detailed information please see the metadata record on Scotland"s SpatialData.gov.scot Metadata Portal

Within the U.S. Geological Survey (USGS), three-dimensional (3D) geologic models are created as part of geologic framework studies, to support energy, minerals, or water resource assessments, and to inform geologic hazard assessments. Such models are often used within the organization as digital input into process and predictive models. 3D geological modeling typically supports research and project work within a specific part of the USGS – called Mission Areas – and as a result, 3D modeling activities are decentralized and model results are released on a project-by-project basis. This digital data release inventories and catalogs, for the first time, 3D geological models constructed by the USGS across all Mission Areas. This inventory assembles in catalog form the spatial locations and salient characteristics of previously published USGS 3D geological models. This inventory covers the time period from 2004, the date of the earliest published model through 2022. This digital dataset contains spatial extents of the 3D geologic models as polygon features that are attributed with unique identifiers that link the spatial data to nonspatial tables that define the data sources used and describe various aspects of each published model. The nonspatial DataSources table includes full citation and URL address for both published model reports and any digital model data released as a separate publication. The nonspatial ModelAttributes table classifies the type of model, using several classification schemes, identifies the model purpose and originating agency, and describes the spatial extent, depth, and number of layers included in each model. A tabular glossary defines terms used in the dataset. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the paper gives some examples from simple and advanced GIS and spatial data analysis highlighting both that big data systems have been around long before the current hype of big data and that they follow some design principles which are inevitable for spatial data including distributed data structures and messaging, which are, however, incompatible with the popular MapReduce paradigm. Throughout this discussion, the need for a replacement or extension of the MapReduce paradigm for spatial data is derived. This paradigm should be able to deal with the imperfect data locality inherent to spatial data hindering full independence of non-trivial computational tasks. We conclude that more research is needed and that spatial big data systems should pick up more concepts like graphs, shortest paths, raster data, events, and streams at the same time instead of solving exactly the set of spatially separable problems such as line simplifications or range queries in manydifferent ways.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

BLM Alaska PLSS Intersected: This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non rectangular components of the PLSS) Meandered Water, Corners and Conflicted Areas (known areas of gaps or overlaps between Townships or state boundaries). The Entity-Attribute section of this metadata describes these components in greater detail.

aOR: odds ratio.bBCI: Bayesian credible interval.au is scalar parameter representing the rate of decline of correlation with distance between points.bσ2 is the estimate of the geographic variability.cDIC is the measure for the model fit. A smaller DIC indicates a better performance of the model.

Peatland ACTION reported hectares of completed and in-progress restoration projects since 2013.

Cloud GIS Market size was valued at USD 890.81 Million in 2024 and is projected to reach USD 2298.38 Million by 2032, growing at a CAGR of 14.5% from 2026 to 2032.

Key Market Drivers

• Increased Adoption of Cloud Computing: Cloud computing provides scalable resources that can be adjusted based on demand, making it easier for organizations to manage and process large GIS datasets. The pay-as-you-go pricing models of cloud services reduce the need for significant upfront investments in hardware and software, making GIS more accessible to small and medium-sized enterprises.

• Growing Need for Spatial Data Integration: The ability to integrate and analyze large volumes of spatial and non-spatial data helps organizations make more informed decisions. The proliferation of Internet of Things (IoT) devices generates massive amounts of spatial data that can be processed and analyzed using Cloud GIS.

This layer shows a hosted table detailing information relating to the City of Grande Prairie's Capital Projects budget for 2019, including categories such as funding amount and project description. This information was downloaded from the city's open data portal for use in visualizing spatial and non-spatial data using GIS tools. It is used in an associated map and dashboard. All data is maintained by the City of Grande Prairie GIS department.

Under the direction and funding of the National Cooperative Mapping Program with guidance and encouragement from the United States Geological Survey (USGS), a digital database of three-dimensional (3D) vector data, displayed as two-dimensional (2D) data-extent bounding polygons. This geodatabase is to act as a virtual and digital inventory of 3D structure contour and isopach vector data for the USGS National Geologic Synthesis (NGS) team. This data will be available visually through a USGS web application and can be queried using complimentary nonspatial tables associated with each data harboring polygon. This initial publication contains 60 datasets collected directly from USGS specific publications and federal repositories. Further publications of dataset collections in versioned releases will be annotated in additional appendices, respectfully. These datasets can be identified from their specific version through their nonspatial tables. This digital dataset contains spatial extents of the 2D geologic vector data as polygon features that are attributed with unique identifiers that link the spatial data to nonspatial tables that define the data sources used and describe various aspects of each published model. The nonspatial DataSources table includes full citation and URL address for both published model reports, any digital model data released as a separate publication, and input type of vector data, using several classification schemes. A tabular glossary defines terms used in the dataset. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

Non-Spatial Discrimination Reversal Learning Examples