updated affiliation, changed remaining visualiZ to visualiS

Author: mtennekes

vignettes/paper.Rmd

@@ -12,7 +12,7 @@ author:
    - name: Robin Lovelace
      affiliation: Institute for Transport Studies and Leeds Institute for Data Analytics, University of Leeds, UK
    - name: Martijn Tennekes
-     affiliation: Center for Big Data Statistics, Centraal Bureau voor de Statistiek, The Netherlands
+     affiliation: Department of Methodology, Statistics Netherlands, The Netherlands
    - name: Dustin Carlino
      affiliation: Independent Software Engineer, Lead Developer of A/B Street, USA
 vignette: >
@@ -117,7 +117,7 @@ setwd(wd_old)
 
 # Introduction
 
-Zoning is the process of generating areal units for aggregating, visualisating and potentially modelling geographic datasets.
+Zoning is the process of generating areal units for aggregating, visualising, and potentially modelling geographic datasets.
 The resulting zones --- also commonly referred to as 'areal units' or 'small areas' in the literature --- have long been used to support analysis of human systems.
 Historical examples highlighting the importance of zone layouts include 'tithe maps' determining land ownership and taxes in 18th Century England [@bryant_worcestershire_2007] and the division of cities into discrete areas including legally defined "business, industrial, and residential zones" to tame chaotic urban growth in the exploding US cities in the early 1900s [@baker_zoning_1925].
 
@@ -128,7 +128,7 @@ Research has made great progress in mathematical analysis of zones and more obje
 The gerrymandering problem is a manifestation of the modifiable area unit problem (MAUP), can be described as a mathematical optimization problem: "$n$ units are grouped into $k$ zones such that some cost function is optimized, subject to constraints on the topology of the zones" [@chou_taming_2006].
 Our aim in this paper is not to tackle the MAUP directly, but to provide a 'ready made' zoning system that can demonstrate some of its effects by providing another way to aggregate and present data.
 
-Prior work has demonstrated the sensitivity of urban analysis outcomes to zone system design, from the way cities are visualized to the [impact of the nature of 'traffic analysis zones' on transport model outputs](http://www.iasi.cnr.it/ewgt/13conference/145_binetti.pdf). 
+Prior work has demonstrated the sensitivity of urban analysis outcomes to zone system design, from the way cities are visualised to the [impact of the nature of 'traffic analysis zones' on transport model outputs](http://www.iasi.cnr.it/ewgt/13conference/145_binetti.pdf). 
 In fact, this problem is a concise definition of the broader "zoning problem" that starts from the assumption that zones are to be composed of one or more basic statistical units (BSUs) [@jelinski_modifiable_1996; @chandra_multi-objective_2021].
 Although the range of outcomes is a finite combinatorial optimisation problem (which combination of BSU-zone aggregations satisfy/optimize some pre-determined criteria), the zoning problem is still hard: "there are a tremendously large number of alternative partitions, a similar number of different results, and only a slightly smaller number of different interpretations" [@openshaw_optimal_1977].
 
@@ -186,7 +186,7 @@ The specific motivations for embarking on the idea, and its implementation in op
 
 -   Aggregation for descriptive statistics.
     It is often useful or necessary to present geographical data in an aggregate form.
-    A consistently sized and shaped set of zones can support attractive, clear and meaningful visualization.
+    A consistently sized and shaped set of zones can support attractive, clear and meaningful visualisation.
 
 -   Comparing cities.
     By using the zoning system to aggregate statistics (e.g. on population density, air quality, bicycle use, number of dwellings), cities can easily be compared.
@@ -208,7 +208,7 @@ Zoning systems for urban analysis should:
 
 -   contain intuitively named zones, enabling public communication of research, e.g. with reference common perceptions of space in terms of distance from the city center and direction relative to North
 -   have a well-balanced number of zones since too many or too few zones may cause issues with analysis and visualisation
-be easy to visualize without too many or too few zones
+be easy to visualise without too many or too few zones
 -   include zones of consistent and useful sizes, for example with zone areas increasing with distance from the urban centers to reflect relatively high densities in central locations
 -   be 'scale agnostic', capable of representing a range of urban forms ranging from extensive cities such as Mexico City to compact cities such as Hong Kong
 -   be extensible and based on open source software, enabling others to create alternative zoning systems suited to diverse needs
@@ -471,7 +471,7 @@ Because of the large and irregular zone shapes in map B, the strength of the rel
 This benefit is especially noticeable towards the outskirts of London, where large outer boroughs such as Bromley (far southeast London) fail to communicate the fact that PM10 levels drop below 1 ug/m^3 in outer London.
 
 
-```{r cityscale, fig.height=2, out.width="100%", fig.cap="Illustration of the ClockBoard zoning system used to visualize a geographically dependendent phenomena: air quality, measured in mass of PM10 particles, measured in micrograms per cubic meter, from the London Atmospheric Emissions Inventory (LAEI). The facets show the data in spatial grid available from the LAEI, facet Am and aggregated to London boroughs B, to ClockBoard zones covering all the input data shown in C, and ClockBoard zones clipped by the administrative boundary of Greater London in D."}
+```{r cityscale, fig.height=2, out.width="100%", fig.cap="Illustration of the ClockBoard zoning system used to visualise a geographically dependendent phenomena: air quality, measured in mass of PM10 particles, measured in micrograms per cubic meter, from the London Atmospheric Emissions Inventory (LAEI). The facets show the data in spatial grid available from the LAEI, facet Am and aggregated to London boroughs B, to ClockBoard zones covering all the input data shown in C, and ClockBoard zones clipped by the administrative boundary of Greater London in D."}
 # file.edit("data-raw/london-figures.R") # to reproduce the figure
 
 # For PDF version with vector graphics: