Martin Charlton
Biographic Data
| ID | 3635935 |
|---|---|
| NAME | Martin Charlton |
| GIVEN NAMES | Martin |
| FAMILY NAME | Charlton |
| SIGNATURE | CHARLTON M |
| AFFILIATIONS | National University of Ireland, Maynooth |
| ORCID | 0000-0002-0622-393X |
| VERIFIED | Yes |
| TOTAL WORKS | 24 |
| TOTAL CITATIONS | 115 |
| AUTHOR COUNT | 24 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1986 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 6 |
A Route Map for Successful Applications of Geographically Weighted Regression
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, se…
The Importance of Scale in Spatially Varying Coefficient Modeling
Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the “spatial scale” of each data relationship is crucially important to make SVC modeling more stable and, in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of …
Data imputation in a short-run space-time series: A Bayesian approach
This paper discusses a project on the completion of a database of socio-economic indicators across the European Union for the years from 1990 onward at various spatial scales. Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database. A Markov Chain Monte Carlo approach was opted for. We describe the Markov Chain Monte Ca…
Mapping the changing residential geography of White British secondary school children in England using visually balanced cartograms and hexograms
In the context of debates about segregation within the UK, this paper maps the residential geography of two groups of White British school children, one of which was in secondary school in 2011 and the other in 2017. To present that geography, hexograms are introduced as a complement to visually balanced cartograms, both of which seek to address the problems of invisibility and distortion encountered with more conventional choropleth and cartogra…
More bark than bytes? Reflections on 21+ years of geocomputation
This year marks the 21st anniversary of the International GeoComputation Conference Series. To celebrate the occasion, Environment and Planning B invited some members of the geocomputational community to reflect on its achievements, some of the unrealised potential, and to identify some of the on-going challenges
Balancing visibility and distortion: Remapping the results of the 2015 UK General Election
A perennial problem when mapping demographic, social and other area-based data is that rural areas usually are of greater physical size than their urban counterparts. The consequence is that the places with fewest people dominate the map space, whereas those with the most are rendered small and illegible. A classic example is what happens if the results of the UK election are mapped, in this case for the 2015 election. As the left side of Figure …
Voting out of the European Union: Exploring the geography of Leave
On Thursday 23 June 2016, a majority of the UK electorate voted in favour of leaving the European Union by a margin of 51.9% to 48.1%. Throughout the night, as the results were reported, the media spoke about London, Scotland and Northern Ireland versus the rest of the UK in regard to their support for the European Union. In this commentary, we look at the geographical distribution of the share of the Leave votes and model it against the share of…
GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models
Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GWmodel we present techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localized calibration pro…
Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data
Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler’s first law of geography, each local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than…
The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models
In this study, we present a collection of local models, termed geographically weighted \n(GW) models, that can be found within the GWmodel R package. A GW model suits \nsituations when spatial data are poorly described by the global form, and for some \nregions the localised fit provides a better description. The approach uses a moving \nwindow weighting technique, where a collection of local models are estimated at target \nlocations. Commonly, …
The demographic impacts of the Irish famine: Towards a greater geographical understanding
The Irish famine of the 1840s had a dramatic effect both on the population within Ireland and the populations of countries such as the US, the UK and Australia, which received the bulk of the Irish diaspora resulting from the famine ( Kenny 2003 ). As such, the effects of the famine have been examined extensively across a range of disciplines. It is therefore a challenge to provide any new perspective on this well‐researched area. However, this p…
Investigating Material Decay of Historic Buildings Using Visual Analytics With Multi‐temporal Infrared Thermographic Data
This paper shows how visual analytics methodology can be used to facilitate interpretation of multi‐temporal thermographic imagery for the purpose of restoration of cultural heritage. We explore thermographic data in a visual environment from the unifying spatio‐temporal perspective in an attempt to identify spatial and spatio‐temporal patterns that could provide information about the structure and the level of decay of the material, and the pres…
Geographically weighted Poisson regression for disease association mapping
This paper describes geographically weighted Poisson regression (GWPR) and its semi-parametric variant as a new statistical tool for analysing disease maps arising from spatially non-stationary processes. The method is a type of conditional kernel regression which uses a spatial weighting function to estimate spatial variations in Poisson regression parameters. It enables us to draw surfaces of local parameter estimates which depict spatial varia…
Estimating the Population of Young People by Ethnic Group in the Northern Region of England, 1971-91
We estimate the numbers of young people aged 0-24 years, resident in the Northern Region in 1971, 1981, and 1991 by ethnic group, age, sex, and birth place (inside-outside the United Kingdom). In 1991 the numbers of residents by ethnic group, age, and sex were obtained from the census and adjusted for underenumeration; the numbers born inside and outside the United Kingdom were estimated from the 1991 Census Sample of Anonymised Records (SAR). In…
Some Notes on Parametric Significance Tests for Geographically Weighted Regression
The technique of geographically weighted regression (GWR) is used to model spatial ‘drift’ in linear model coefficients. In this paper we extend the ideas of GWR in a number of ways. First, we introduce a set of analytically derived significance tests allowing a null hypothesis of no spatial parameter drift to be investigated. Second, we discuss ‘mixed’ GWR models where some parameters are fixed globally but others vary geographically. Again, mod…
Geographically Weighted Regression
In regression models where the cases are geographical locations, sometimes regression coefficients do not remain fixed over space. A technique for exploring this phenomenon, geographically weighted regression is introduced. A related Monte Carlo significance test for spatial non-stationarity is also considered. Finally, an example of the method is given, using limiting long-term illness data from the 1991 UK census.
Spatial Nonstationarity and Autoregressive Models
Until relatively recently, the emphasis of spatial analysis was on the investigation of global models and global processes. Recent research, however, has tended to explore exceptions to general processes, and techniques have been developed which have as their focus the investigation of spatial variations in local relationships. One of these techniques, known as geographically weighted regression (GWR), developed by the authors is used here to inv…
Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis
Geographically weighted regression and the expansion method are two statistical techniques which can be used to examine the spatial variability of regression results across a region and so inform on the presence of spatial nonstationarity. Rather than accept one set of 'global' regression results, both techniques allow the possibility of producing 'local' regression results from any point within the region so that the output from the analysis is …
Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity
Spatial nonstationarity is a condition in which a simple “global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at differen…
Limb reduction defects in the northern region of England 1985-92
STUDY OBJECTIVE--To test the hypothesis that children born to mothers living near the sea are at increased risk of limb reduction defects. DESIGN--Descriptive data analysis. SETTING--The northern health region of England. PATIENTS--All children born between 1 January 1985 and 31 December 1992 in the northern region of England with isolated limb reduction defects. MAIN RESULTS--The birth prevalence of isolated limb reduction defects was not affect…
Cancer in young people in the north of England, 1968-85: Analysis by census wards
OBJECTIVE--To determine whether the seeming excess of childhood leukaemia and lymphoma identified in Seascale, Cumbria, UK, remains unusual when put into a wider context. DESIGN--Analysis of cancer incidence by geographical area. SETTING--The north of England including the Northern and North Western Regional Health Authority regions and the Southport and South Sefton districts of the Mersey Regional Health Authority. SUBJECTS--Altogether 6686 cas…
Searching for Leukaemia Clusters Using a Geographical Analysis Machine
A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets
This paper presents the first of a new generation of spatial analytical technology based on a fusion of statistical, GIS and computational thinking. It describes how to build what is termed a Geographical Analysis Machine (GAM), with high descriptive power. A GAM offers an imaginative new approach to the analysis of point pattern data based on a fully automated process whereby a point data set is explored for evidence of pattern without being und…
A Geographical Information and Mapping System for the BBC Domesday Optical Discs
The paper outlines the nature of the geographical information system that was developed for the BBC Domesday optical disc project. The successful exploitation of optical disc storage for geographical analysis and mappings dependent on the development of appropriate data structures that allow fastest possible data retrieval for geographical areas of interest. The authors describe how they achieved this objective such that a low powered BBC micro-c…
Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis
Geographically weighted regression and the expansion method are two statistical techniques which can be used to examine the spatial variability of regression results across a region and so inform on the presence of spatial nonstationarity. Rather than accept one set of 'global' regression results, both techniques allow the possibility of producing 'local' regression results from any point within the region so that the output from the analysis is …
The demographic impacts of the Irish famine: Towards a greater geographical understanding
The Irish famine of the 1840s had a dramatic effect both on the population within Ireland and the populations of countries such as the US, the UK and Australia, which received the bulk of the Irish diaspora resulting from the famine ( Kenny 2003 ). As such, the effects of the famine have been examined extensively across a range of disciplines. It is therefore a challenge to provide any new perspective on this well‐researched area. However, this p…
Searching for Leukaemia Clusters Using a Geographical Analysis Machine
Spatial Nonstationarity and Autoregressive Models
Until relatively recently, the emphasis of spatial analysis was on the investigation of global models and global processes. Recent research, however, has tended to explore exceptions to general processes, and techniques have been developed which have as their focus the investigation of spatial variations in local relationships. One of these techniques, known as geographically weighted regression (GWR), developed by the authors is used here to inv…
A Geographical Information and Mapping System for the BBC Domesday Optical Discs
The paper outlines the nature of the geographical information system that was developed for the BBC Domesday optical disc project. The successful exploitation of optical disc storage for geographical analysis and mappings dependent on the development of appropriate data structures that allow fastest possible data retrieval for geographical areas of interest. The authors describe how they achieved this objective such that a low powered BBC micro-c…
The Importance of Scale in Spatially Varying Coefficient Modeling
Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the “spatial scale” of each data relationship is crucially important to make SVC modeling more stable and, in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of …
Voting out of the European Union: Exploring the geography of Leave
On Thursday 23 June 2016, a majority of the UK electorate voted in favour of leaving the European Union by a margin of 51.9% to 48.1%. Throughout the night, as the results were reported, the media spoke about London, Scotland and Northern Ireland versus the rest of the UK in regard to their support for the European Union. In this commentary, we look at the geographical distribution of the share of the Leave votes and model it against the share of…
Balancing visibility and distortion: Remapping the results of the 2015 UK General Election
A perennial problem when mapping demographic, social and other area-based data is that rural areas usually are of greater physical size than their urban counterparts. The consequence is that the places with fewest people dominate the map space, whereas those with the most are rendered small and illegible. A classic example is what happens if the results of the UK election are mapped, in this case for the 2015 election. As the left side of Figure …
Cancer in young people in the north of England, 1968-85: Analysis by census wards
OBJECTIVE--To determine whether the seeming excess of childhood leukaemia and lymphoma identified in Seascale, Cumbria, UK, remains unusual when put into a wider context. DESIGN--Analysis of cancer incidence by geographical area. SETTING--The north of England including the Northern and North Western Regional Health Authority regions and the Southport and South Sefton districts of the Mersey Regional Health Authority. SUBJECTS--Altogether 6686 cas…
Estimating the Population of Young People by Ethnic Group in the Northern Region of England, 1971-91
We estimate the numbers of young people aged 0-24 years, resident in the Northern Region in 1971, 1981, and 1991 by ethnic group, age, sex, and birth place (inside-outside the United Kingdom). In 1991 the numbers of residents by ethnic group, age, and sex were obtained from the census and adjusted for underenumeration; the numbers born inside and outside the United Kingdom were estimated from the 1991 Census Sample of Anonymised Records (SAR). In…
A Geographical Information and Mapping System for the BBC Domesday Optical Discs
The paper outlines the nature of the geographical information system that was developed for the BBC Domesday optical disc project. The successful exploitation of optical disc storage for geographical analysis and mappings dependent on the development of appropriate data structures that allow fastest possible data retrieval for geographical areas of interest. The authors describe how they achieved this objective such that a low powered BBC micro-c…
A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets
This paper presents the first of a new generation of spatial analytical technology based on a fusion of statistical, GIS and computational thinking. It describes how to build what is termed a Geographical Analysis Machine (GAM), with high descriptive power. A GAM offers an imaginative new approach to the analysis of point pattern data based on a fully automated process whereby a point data set is explored for evidence of pattern without being und…
Searching for Leukaemia Clusters Using a Geographical Analysis Machine
Cancer in young people in the north of England, 1968-85: Analysis by census wards
OBJECTIVE--To determine whether the seeming excess of childhood leukaemia and lymphoma identified in Seascale, Cumbria, UK, remains unusual when put into a wider context. DESIGN--Analysis of cancer incidence by geographical area. SETTING--The north of England including the Northern and North Western Regional Health Authority regions and the Southport and South Sefton districts of the Mersey Regional Health Authority. SUBJECTS--Altogether 6686 cas…
Limb reduction defects in the northern region of England 1985-92
STUDY OBJECTIVE--To test the hypothesis that children born to mothers living near the sea are at increased risk of limb reduction defects. DESIGN--Descriptive data analysis. SETTING--The northern health region of England. PATIENTS--All children born between 1 January 1985 and 31 December 1992 in the northern region of England with isolated limb reduction defects. MAIN RESULTS--The birth prevalence of isolated limb reduction defects was not affect…
Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity
Spatial nonstationarity is a condition in which a simple “global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geographically weighted regression, which attempts to capture this variation by calibrating a multiple regression model which allows different relationships to exist at differen…
Geographically Weighted Regression
In regression models where the cases are geographical locations, sometimes regression coefficients do not remain fixed over space. A technique for exploring this phenomenon, geographically weighted regression is introduced. A related Monte Carlo significance test for spatial non-stationarity is also considered. Finally, an example of the method is given, using limiting long-term illness data from the 1991 UK census.
Spatial Nonstationarity and Autoregressive Models
Until relatively recently, the emphasis of spatial analysis was on the investigation of global models and global processes. Recent research, however, has tended to explore exceptions to general processes, and techniques have been developed which have as their focus the investigation of spatial variations in local relationships. One of these techniques, known as geographically weighted regression (GWR), developed by the authors is used here to inv…
Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis
Geographically weighted regression and the expansion method are two statistical techniques which can be used to examine the spatial variability of regression results across a region and so inform on the presence of spatial nonstationarity. Rather than accept one set of 'global' regression results, both techniques allow the possibility of producing 'local' regression results from any point within the region so that the output from the analysis is …
Some Notes on Parametric Significance Tests for Geographically Weighted Regression
The technique of geographically weighted regression (GWR) is used to model spatial ‘drift’ in linear model coefficients. In this paper we extend the ideas of GWR in a number of ways. First, we introduce a set of analytically derived significance tests allowing a null hypothesis of no spatial parameter drift to be investigated. Second, we discuss ‘mixed’ GWR models where some parameters are fixed globally but others vary geographically. Again, mod…
Estimating the Population of Young People by Ethnic Group in the Northern Region of England, 1971-91
We estimate the numbers of young people aged 0-24 years, resident in the Northern Region in 1971, 1981, and 1991 by ethnic group, age, sex, and birth place (inside-outside the United Kingdom). In 1991 the numbers of residents by ethnic group, age, and sex were obtained from the census and adjusted for underenumeration; the numbers born inside and outside the United Kingdom were estimated from the 1991 Census Sample of Anonymised Records (SAR). In…
Geographically weighted Poisson regression for disease association mapping
This paper describes geographically weighted Poisson regression (GWPR) and its semi-parametric variant as a new statistical tool for analysing disease maps arising from spatially non-stationary processes. The method is a type of conditional kernel regression which uses a spatial weighting function to estimate spatial variations in Poisson regression parameters. It enables us to draw surfaces of local parameter estimates which depict spatial varia…
Investigating Material Decay of Historic Buildings Using Visual Analytics With Multi‐temporal Infrared Thermographic Data
This paper shows how visual analytics methodology can be used to facilitate interpretation of multi‐temporal thermographic imagery for the purpose of restoration of cultural heritage. We explore thermographic data in a visual environment from the unifying spatio‐temporal perspective in an attempt to identify spatial and spatio‐temporal patterns that could provide information about the structure and the level of decay of the material, and the pres…
The demographic impacts of the Irish famine: Towards a greater geographical understanding
The Irish famine of the 1840s had a dramatic effect both on the population within Ireland and the populations of countries such as the US, the UK and Australia, which received the bulk of the Irish diaspora resulting from the famine ( Kenny 2003 ). As such, the effects of the famine have been examined extensively across a range of disciplines. It is therefore a challenge to provide any new perspective on this well‐researched area. However, this p…
Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data
Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler’s first law of geography, each local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than…
The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models
In this study, we present a collection of local models, termed geographically weighted \n(GW) models, that can be found within the GWmodel R package. A GW model suits \nsituations when spatial data are poorly described by the global form, and for some \nregions the localised fit provides a better description. The approach uses a moving \nwindow weighting technique, where a collection of local models are estimated at target \nlocations. Commonly, …
GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models
Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GWmodel we present techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localized calibration pro…
Voting out of the European Union: Exploring the geography of Leave
On Thursday 23 June 2016, a majority of the UK electorate voted in favour of leaving the European Union by a margin of 51.9% to 48.1%. Throughout the night, as the results were reported, the media spoke about London, Scotland and Northern Ireland versus the rest of the UK in regard to their support for the European Union. In this commentary, we look at the geographical distribution of the share of the Leave votes and model it against the share of…
More bark than bytes? Reflections on 21+ years of geocomputation
This year marks the 21st anniversary of the International GeoComputation Conference Series. To celebrate the occasion, Environment and Planning B invited some members of the geocomputational community to reflect on its achievements, some of the unrealised potential, and to identify some of the on-going challenges
Balancing visibility and distortion: Remapping the results of the 2015 UK General Election
A perennial problem when mapping demographic, social and other area-based data is that rural areas usually are of greater physical size than their urban counterparts. The consequence is that the places with fewest people dominate the map space, whereas those with the most are rendered small and illegible. A classic example is what happens if the results of the UK election are mapped, in this case for the 2015 election. As the left side of Figure …
Data imputation in a short-run space-time series: A Bayesian approach
This paper discusses a project on the completion of a database of socio-economic indicators across the European Union for the years from 1990 onward at various spatial scales. Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database. A Markov Chain Monte Carlo approach was opted for. We describe the Markov Chain Monte Ca…
Mapping the changing residential geography of White British secondary school children in England using visually balanced cartograms and hexograms
In the context of debates about segregation within the UK, this paper maps the residential geography of two groups of White British school children, one of which was in secondary school in 2011 and the other in 2017. To present that geography, hexograms are introduced as a complement to visually balanced cartograms, both of which seek to address the problems of invisibility and distortion encountered with more conventional choropleth and cartogra…
The Importance of Scale in Spatially Varying Coefficient Modeling
Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the “spatial scale” of each data relationship is crucially important to make SVC modeling more stable and, in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of …
A Route Map for Successful Applications of Geographically Weighted Regression
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, se…
Computer Science (15 works) · Geography (15 works) · Mathematics (12 works) · Spatial and Panel Data Analysis (12 works) · Statistics (12 works) · Econometrics (9 works) · Economic and Environmental Valuation (7 works) · Regional Economics and Spatial Analysis (7 works) · Regression (7 works) · Regression analysis (7 works)