Curve Fit: a pixel‐level raster regression tool for mapping spatial patterns

Jager, Nathan R. De; Fox, Timothy J.

doi:10.1111/2041-210x.12068

Cited by 17 publications

(10 citation statements)

References 14 publications

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“…We used the pixel level regression Curve Fit tool [44][45][46] an extension in ArcMap (ArcGIS) [31]. This allowed us to run regression trend analysis on 72 cities using AVHRR raster datasets for temporal analysis (1990, 1991, 1995, 1996, 1997, 1998, and 2001 to 2010).…”

Section: Black Populationmentioning

confidence: 99%

“…To fill the gap, we experimented with a pixel level regression tool to perform trend analysis for 72 cities, analyzing correlation coefficient, standard error, and r-squared metrics against changes in vegetation phenology. This approach is similar to GWR [30,31]. However, whereas the purpose of GWR is to determine how the coefficients of explanatory variables vary in space, this approach explored how the prediction itself (e.g., some property of the landscape) changes with a single explanatory variable (e.g., time).…”

Section: Introductionmentioning

confidence: 99%

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A Spatial Analysis of the Relationship between Vegetation and Poverty

Dawson

Sandoval

Sagan

et al. 2018

IJGI

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The goal of this paper was to investigate poverty and inequities that are associated with vegetation. First, we performed a pixel-level linear regression on time-series and Normalized Difference Vegetation Index (NDVI) for 72 United States (U.S.) cities with a population ≥250,000 for 16 Radiometer 1-kilometer (1-km). Second, from the pixel-level regression, we selected five U.S. cities (Shrinking: Chicago, Detroit, Philadelphia, and Growing: Dallas and Tucson) that were one standard deviation above the overall r-squared mean and one standard deviation below the overall r-squared mean to show cities that were different from the typical cities. Finally, we used spatial statistics to investigate the relationship between census tract level data (i.e., poverty, population, and race) and vegetation for 2010, based on the 1-km grid cells using Ordinary Least Squares Regression and Geographically Weighted Regression. Our results revealed poverty related areas were significantly correlated with positive high and/or negative high vegetation in both shrinking and growing cities. This paper makes a contribution to the academic body of knowledge on U.S. urban shrinking and growing cities by using a comparative analysis with global and local spatial statistics to understand the relationship between vegetation and socioeconomic inequality. neighborhoods in crisis undergo abandonment and vacancy, all of which has the positive potential to drive an increase in vegetation due to clearance and natural ecosystem changes. This study addressed the following research question. What is the relationship between poverty and vegetation? Building on previous research on vegetation, our paper makes original and significant contributions to the corpus of literature on U.S. shrinking and growing cities [3,4]. First, we used a comparative analysis to study three shrinking and two growing cities that were one standard deviation above the overall r-squared mean and one standard deviation below the overall r-squared mean to show cities that were different from the typical city. Second, we used global and local spatial statistics to study the spatial relationship between vegetation and socioeconomic inequality. Finally, we employed a novel methodology to study this relationship by using 1-km grids, rather than U.S. census tracts. The findings from this research provide much needed insight on the difference between shrinking and growing cities.

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Section: Black Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Spatial Analysis of the Relationship between Vegetation and Poverty

Dawson

Sandoval

Sagan

et al. 2018

IJGI

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“…For each pixel, a simple linear regression was performed between the TBP and the annual maximum CI for that pixel using the CurveFit extension to ESRI’s ArcMap software (De Jager & Fox, 2013). We focused on the regression coefficient of determination ( R 2 ) and the slope (referred to as “ a ” in the CurveFit software) as indicators of the fit and effect size of the model relating TBP and CI.…”

Section: Methodsmentioning

confidence: 99%

Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie

Larson

Hlavacek

DeJager

et al. 2020

Ecology and Evolution

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Since the early 2000s, Lake Erie has been experiencing annual cyanobacterial blooms that often cover large portions of the western basin and even reach into the central basin. These blooms have affected several ecosystem services provided by Lake Erie to surrounding communities (notably drinking water quality). Several modeling efforts have identified the springtime total bioavailable phosphorus (TBP) load as a major driver of maximum cyanobacterial biomass in western Lake Erie, and on this basis, international water management bodies have set a phosphorus (P) reduction goal. This P reduction goal is intended to reduce maximum cyanobacterial biomass, but there has been very limited effort to identify the specific locations within the western basin of Lake Erie that will likely experience the most benefits. Here, we used pixel-specific linear regression to identify where annual variation in spring TBP loads is most strongly associated with cyanobacterial abundance, as inferred from satellite imagery. Using this approach, we find that annual TBP loads are most strongly associated with cyanobacterial abundance in the central and southern areas of the western basin. At the location of the Toledo water intake, the association between TBP load and cyanobacterial abundance is moderate, and in Maumee Bay (near Toledo, Ohio),

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“…Curve Fit 10.1 (De Jager & Fox ), an extension to ArcMap, was used to run a regression analysis on the previously interpolated raster datasets from 1998 to 2016 (Fig. ).…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal changes in species composition of submersed aquatic vegetation reveal effects of river restoration

Carhart

Jager

2018

Restoration Ecology

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We examined temporal changes in spatial patterns of submersed aquatic vegetation (SAV) in response to the restoration of geomorphic habitat in Navigation Pool 8 of the Upper Mississippi River from 1998 to 2016. The frequency of occurrence and species composition of SAV at sampling sites were spatially interpolated for each year to create annual maps. Linear models were fitted to temporal changes in SAV within each map pixel. The frequency of occurrence of SAV (across all species) increased over time in much of the impounded region of the pool, including areas near restored islands. However, impounded areas maintained a relatively consistent species composition over time, with species known to be tolerant of higher flow velocities (>0.10 m/second) and wind fetch distances (>1,000 m) (e.g. Vallisneria americana) being most abundant. In contrast, areas protected by newly constructed islands transitioned from V. americana to species found in other protected backwater habitats and known to be intolerant of high flow velocities and wind fetch distances (e.g. Ceratophyllum demersum). The results suggest that previously reported improvements in water clarity may have improved growing conditions for all SAV species, especially in the lower impounded portion of the pool, while island restoration created more backwater-like habitats and facilitated changes in species composition. Assessing changes in SAV occurrence alone offers only a partial view of local-scale river restoration (e.g. island building), while analyses of species composition are likely to be more indicative of the types of changes (i.e. reduced flow velocity and wind fetch) associated with restoring geomorphic habitat. Implications for Practice• Long-term increases in water clarity promote increases in the frequency of occurrence of submersed aquatic vegetation (SAV) in areas with suitable water depth and substrate conditions. • Assessing changes in the frequency of occurrence of SAV alone offers only a partial view of river restoration projects. Spatial changes in species composition, due to alterations in water depth, flow velocity, and wind fetch, better assess the effects of restoration. • Island construction reduces wind fetch and flow velocity thereby creating backwater-like habitat and facilitates changes in macrophyte species composition from flow-tolerant species (predominantly Vallisneria americana) to species that tend to be more abundant in poorly connected, low velocity areas (predominantly Ceratophyllum demersum). • Our analyses suggest that protected, backwater-like areas may be more temporally persistent in terms of SAV frequency and species composition, as compared to the more open-water and lotic impounded areas.

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Curve Fit: a pixel‐level raster regression tool for mapping spatial patterns

Cited by 17 publications

References 14 publications

A Spatial Analysis of the Relationship between Vegetation and Poverty

A Spatial Analysis of the Relationship between Vegetation and Poverty

Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie

Spatial and temporal changes in species composition of submersed aquatic vegetation reveal effects of river restoration

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