Advanced Spatial Analysis of Blue-winged Teal habitat in Canadian Prairie Pothole Region

‘The Prairie Pothole Region (PPR) of North America is the primary breeding ground for many ducks; about 80% of the Region is in Canada’ (Batt et al. 1989 cited in Greenwood et al., 1995). ‘The region is characterized by a high density of shallow, productive wetlands that support an abundance of waterfowl and other water birds’ (Kantrud et al., 1989 cited in Austin et al., 2000). ‘The PPR of Canada (Fig. 1) is composed of about 480,000 km2 and spreads across southeastern Alberta, southern Saskatchewan, and southwestern Manitoba with a flat to gently rolling landscape dissected by several rivers’ (Greenwood et al., 1995).

‘The Blue-winged teal is one of the migratory North American waterfowl scientifically known as Anas discors’ (Sandilands 2005). According to Ducks Unlimited Canada (2008), ‘the blue-winged teal ranks fourth in numbers among North American ducks but their numbers have dropped as low as three million or less in recent years from a high of more than 5 million in the 1950s.’ They found out that the Blue-winged teal populations are highly sensitive to drought and removal of their habitat and its conversion by humans to other uses such as agriculture, suburban expansion and road-building. 

The decline of Blue-winged teal with several other migratory ducks in the Canadian PPR is an area of big concern and is the main reason behind carrying out this study. The Bluewinged teal’s habitat is taken into account by considering its population density, which is the main dependent variable, and factors such as precipitation, pond density, conserved soil moisture and land cover suitability are considered as the independent variables. 

This study makes use of secondary data from various websites. The nature and scope of this study is huge and it is almost impossible to capture all the factors that affect the population density of species such as the Blue-winged teal. However, the initiation of this study is crucial to understand the factors contributing to the decline of this species and provides a sound basis for further studies linked broadly with similar objectives set out in this study. Read entire article

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Geographically Weighted Regression in Advanced Spatial Analysis and Modeling

The main purpose of this study is to run a Geographically Weighted Regression (GWR) on the City of Calgary Census Data using Geographical Weighted Regression. The data available for study is the Census Tract data set for the City of Calgary provided in a geodatabase format. The dataset is similar to the first and fifth Assignments. However, the previous assignments involved the calculation of a simple linear regression and spatial regression which outputs global parameter rather than local. Therefore, in this study, the objective is to fit a GWR model to the Census data using "Average Income" as the dependent variable so as to allow local parameter to estimate the model. The "Geographically Weighted Regression 3" software package is used to compute the global weighted regression model. This paper includes many statistical techniques applied to obtain the final model. It also compares the results obtained from the linear regression model and spatial regression model  with the Geographically Weighted Regression model.

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Spatial Regression in Advanced Spatial analysis and Modeling

Introduction

The main purpose of this study is to explore the process of model selection and spatial regression using S-Plus and its Spatial module. The data available for study is the Census Tract data set for the City of Calgary provided both in GeoDatabase and S-Plus data frame. The dataset is similar to our first Assignment but with an addition of two columns for X and Y to represent Easting and Northing respectively. The previous assignment involved the calculation of a simple linear regression without considering spatial autocorrelation into account. Therefore, in this study, we will fit a regression model to the Census data using ‘Average Income’ as the dependent variable by considering spatial autocorrelation in the regression model. The S-Plus command line was used to compute a Simultaneous Autoregressive (SAR) model so as to describe the relationship between ‘Average Income’ and the other independent variables. This paper includes many statistical techniques applied to obtain the spatial regression model. It also attempts to compare the results obtained from the simple linear regression (in Assignment 1) with the model obtained in this study.

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