257 research outputs found
Is `Statistix Inferens' Still the Geographical Name for a Wild Goose?
After the recent death of Peter Gould, I decided to look once again at his insightful
paper `Is Statistix Inferens the Geographical Name for a Wild Goose?' (Gould 1970) -
hence the title for this guest editorial. For those readers who have not seen this article,
Gould outlines a number of shortcomings of the common statistical practices of
geographers of the day
Quantitative methods I: Reproducible research and quantitative geography.
Reproducible quantitative research is research that has been documented sufficiently rigorously that a third
party can replicate any quantitative results that arise. It is argued here that such a goal is desirable for
quantitative human geography, particularly as trends in this area suggest a turn towards the creation of
algorithms and codes for simulation and the analysis of Big Data. A number of examples of good practice in
this area are considered, spanning a time period from the late 1970s to the present day. Following this,
practical aspects such as tools that enable research to be made reproducible are discussed, and some
beneficial side effects of adopting the practice are identified. The paper concludes by considering some of the
challenges faced by quantitative geographers aspiring to publish reproducible research
Assessing the changing flowering date of the common lilac in North America: a random coefficient model approach
A data set consisting of Volunteered geographical information (VGI) and
data provided by expert researchers monitoring the first bloom dates of lilacs from
1956 to 2003 is used to investigate changes in the onset of the North American
spring. It is argued that care must be taken when analysing data of this kind, with
particular focus on the issues of lack of experimental design, and Simpson’s paradox.
Approaches used to overcome this issue make use of random coefficient modelling,
and bootstrapping approaches. Once the suggested methods have been employed,
a gradual advance in the onset of spring is suggested by the results of the analysis.
A key lesson learned is that the appropriateness of the model calibration technique
used given the process of data collection needs careful consideration
Estimating probability surfaces for geographical point data: An adaptive kernel algorithm
The statistical analysis of spatially referenced information has been acknowledged as an important component of geographical data processing. With the arrival of GIS there has been a need to devise statistical methods that are compatible with, and relevant to, GIS-based methodologies. Here an algorithm is presented which estimates a “risk surface” from a set of point-referenced events. Such a surface may be viewed as an object embedded in three-dimensional space, or as a contour map. In addition to this view, it is possible to incorporate these surfaces into a broader based GIS framework, allowing the mapping of these patterns in conjunction with other data, overlay analysis, and spatial query. The technique is adaptive, in the sense that parameters which control the surface estimation are adjusted over geographic space, allowing for local variations in point pattern characteristics. The paper is concluded with an example based on probabilistic mapping using data taken from Californian Redwood seedling data
Path Estimation from GPS Tracks
The widespread availability of hand-held GPS units has led to a proliferation in data on the tracks
of individuals as they walk, drive or otherwise go about journeys. This data has been used in
a number of ways - for example the OpenStreetMap project (The OpenStreetMap Foundation
2007). One characteristic of projects such as this is that there will often be several GPS tracks for
the same stretch of road. In general, repeatedly measuring something and taking the average of
measurements leads to a more accurate result. The question addressed here is “is it possible to
’average’ GPS tracks and if so, does this lead to a better estimate of road location?”
Spatial variations in the average rainfall - altitude relationship in Great Britain: an approach using geographically weighted regression
The relationship between annual rainfall totals and gauge elevation over Great Britain is re-examined using the
recently developed technique of geographically weighted regression (GWR). This enables the spatial drift of regression
parameters to be identified, estimated and mapped. It is shown that the rate of increase of precipitation with height,
or height coefficient, varies from around 4.5 mm:m in the northwest to almost zero in the southeast. There is a
particularly rapid change in this value across the English Midlands. The predicted sea level precipitation varies from
1250 mm to less than 600 mm in much the same way
Locally-varying explanations behind the United Kingdom\u27s vote to leave the European Union
Explanations behind area-based (Local Authority-level) voting preference in the 2016 referendum on membership of the European Union are explored using aggregate-level data. Developing local models, special attention is paid to whether variables explain the vote equally well across the country. Variables describing the post-industrial and economic successfulness of Local Authorities most strongly discriminate variation in the vote. To a lesser extent this is the case for variables linked to metropolitan and big city contexts, which assist the Remain vote, those that distinguish more traditional and nativist values, assisting Leave, and those loosely describing material outcomes, again reinforcing Leave. Whilst variables describing economic competitiveness co-vary with voting preference equally well across the country, the importance of secondary variables - those distinguishing metropolitan settings, values and outcomes - does vary by region. For certain variables and in certain areas, the direction of effect on voting preference reverses. For example, whilst levels of European Union migration mostly assist the Remain vote, in parts of the country the opposite effect is observed
Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor
In this study, we link and compare the geographically
weighted regression (GWR) model with the
kriging with an external drift (KED) model of geostatistics.
This includes empirical work where models are performance
tested with respect to prediction and prediction
uncertainty accuracy. In basic forms, GWR and KED
(specified with local neighbourhoods) both cater for nonstationary
correlations (i.e. the process is heteroskedastic
with respect to relationships between the variable of
interest and its covariates) and as such, can predict more
accurately than models that do not. Furthermore, on specification
of an additional heteroskedastic term to the same
models (now with respect to a process variance), locallyaccurate
measures of prediction uncertainty can result.
These heteroskedastic extensions of GWR and KED can be
preferred to basic constructions, whose measures of prediction
uncertainty are only ever likely to be globallyaccurate.
We evaluate both basic and heteroskedastic
GWR and KED models using a case study data set, where
data relationships are known to vary across space. Here
GWR performs well with respect to the more involved
KED model and as such, GWR is considered a viable
alternative to the more established model in this particular
comparison. Our study adds to a growing body of empirical
evidence that GWR can be a worthy predictor; complementing
its more usual guise as an exploratory technique for investigating relationships in multivariate spatial data
sets
Locally-varying explanations behind the United Kingdom\u27s vote to leave the European Union
Explanations behind area-based (Local Authority-level) voting preference in the 2016 referendum on membership of the European Union are explored using aggregate-level data. Developing local models, special attention is paid to whether variables explain the vote equally well across the country. Variables describing the post-industrial and economic successfulness of Local Authorities most strongly discriminate variation in the vote. To a lesser extent this is the case for variables linked to metropolitan and big city contexts, which assist the Remain vote, those that distinguish more traditional and nativist values, assisting Leave, and those loosely describing material outcomes, again reinforcing Leave. Whilst variables describing economic competitiveness co-vary with voting preference equally well across the country, the importance of secondary variables - those distinguishing metropolitan settings, values and outcomes - does vary by region. For certain variables and in certain areas, the direction of effect on voting preference reverses. For example, whilst levels of European Union migration mostly assist the Remain vote, in parts of the country the opposite effect is observed
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