EcoService Models Library (ESML)
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Variables Details
: (EM-654)
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EM Identity and Description
EM-654 | |
Document Author
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Qiu, J. and M. G. Turner |
Document Year
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2013 |
Variable General Info
Proximity to population centers ?Comment:To quantify proximity to population centers, we created a population density layer with 500-m spatial resolution based on the US Census tract-level resident population data for 2000, by using a moving window analysis that summed the population density of tracts having their centers within 10 km of each grid cell. We also used Jenks algorithm to classify the population density layer into 10 natural breaks, then weighted these values to the range of 0–40. |
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Variable ID
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15360 |
Popi | |
Qualitative-Quantitative
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Qualitative (Class, Rating or Ranking) |
Cardinal-Ordinal
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Ordinal |
Not applicable |
Variable Typology
Proximity to population centers ?Comment:To quantify proximity to population centers, we created a population density layer with 500-m spatial resolution based on the US Census tract-level resident population data for 2000, by using a moving window analysis that summed the population density of tracts having their centers within 10 km of each grid cell. We also used Jenks algorithm to classify the population density layer into 10 natural breaks, then weighted these values to the range of 0–40. |
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Predictor-Intermediate-Response
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Predictor |
Predictor Variable Type
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Time- or Space-varying Variable |
Response Variable Type
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Not applicable |
Data Source/Type
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Map or database (e.g., wide coverage, wide availability, measured or modeled) |
Variable Classification Hierarchy
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3. Human Demographic Data |
--Population number, density and growth |
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Variable Spatial Characteristics
Proximity to population centers ?Comment:To quantify proximity to population centers, we created a population density layer with 500-m spatial resolution based on the US Census tract-level resident population data for 2000, by using a moving window analysis that summed the population density of tracts having their centers within 10 km of each grid cell. We also used Jenks algorithm to classify the population density layer into 10 natural breaks, then weighted these values to the range of 0–40. |
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Spatial Extent Area
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1000-10,000 km^2. |
Spatially Distributed?
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Yes |
Observations Spatially Patterned?
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Yes |
Spatial Grain Type
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area, for pixel or radial feature |
Spatial Grain Size
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500m x 500m |
Spatial Density
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Not applicable |
EnviroAtlas URL
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Dasymetric Allocation of Population |
Variable Temporal Characteristics
Proximity to population centers ?Comment:To quantify proximity to population centers, we created a population density layer with 500-m spatial resolution based on the US Census tract-level resident population data for 2000, by using a moving window analysis that summed the population density of tracts having their centers within 10 km of each grid cell. We also used Jenks algorithm to classify the population density layer into 10 natural breaks, then weighted these values to the range of 0–40. |
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Temporal Extent
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2000 |
Temporally Distributed?
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Not applicable |
Regular Temporal Grain?
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Not applicable |
Temporal Grain Size Value
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Not applicable |
Temporal Grain Size Units
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Not applicable |
Temporal Density
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Not applicable |
Variable Values
Proximity to population centers ?Comment:To quantify proximity to population centers, we created a population density layer with 500-m spatial resolution based on the US Census tract-level resident population data for 2000, by using a moving window analysis that summed the population density of tracts having their centers within 10 km of each grid cell. We also used Jenks algorithm to classify the population density layer into 10 natural breaks, then weighted these values to the range of 0–40. |
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Not applicable | |
Min Value
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Not applicable |
Max Value
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Not applicable |
Other Value Type
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Not applicable |
Other Value
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Not applicable |
Variable Variability and Sensitivity
Proximity to population centers ?Comment:To quantify proximity to population centers, we created a population density layer with 500-m spatial resolution based on the US Census tract-level resident population data for 2000, by using a moving window analysis that summed the population density of tracts having their centers within 10 km of each grid cell. We also used Jenks algorithm to classify the population density layer into 10 natural breaks, then weighted these values to the range of 0–40. |
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Variability Expression Given?
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No |
Variability Metric
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None |
Variability Value
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None |
Variability Units
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None |
Resampling Used?
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Not applicable |
Variability Expression Used in Modeling?
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Not applicable |
Variable Operational Validation (Response Variables only)
Variable ID
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Validated?
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Validation Approach (within, between, etc.)
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Validation Quality (Qual/Quant)
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Validation Method (Stat/Deviance)
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Validation Metric
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Validation Value
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Validation Units
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Use of Measured Response Data
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