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<title level="a" type="full">Locations of Markets in English Market Towns, 1813. Constructing a dataset</title>
<title level="a" type="short">English Markets</title>
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<div type="chapter">
<ab>
<list type="unordered">
<item>Dataset: <bibl><title type="desc">Locations of Markets in English Market Towns, 1813</title></bibl></item>
<item>Contributor: Philip Allfrey (<ref target="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/data-curation/">Data curation</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/investigation/">Investigation</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/methodology/">Methodology</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/resources/">Resources</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/software/">Software</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/validation/">Validation</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/visualization/">Visualization</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing&#160;– original draft</ref>&#160;|
<ref target="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing&#160;– review &amp; editing</ref>)
</item>
<item>Version: 2</item>
<item>First published: 20.12.2024</item>
<item>Last updated: 20.12.2024</item>
<item>License: <ref target="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International (CC BY 4.0)</ref></item>
<item>Repository: <ref target="https://works.hcommons.org/records/cwart-61790">Knowledge Commons Works</ref></item>
<item>DOI: <ref target="https://doi.org/10.17613/cwart-61790">10.17613/cwart-61790</ref></item>
<item>Suggested citation: Philip Allfrey: Locations of Markets in English Market Towns, 1813. 20.12.2024. Knowledge Commons Works. DOI: 10.17613/cwart-61790</item>
</list>
</ab>
<ab>
<hi rend="bold">Related publications:</hi>
<list type="unordered">
<item>Philip Allfrey: english-market-towns-1813. In: Philip Allfrey (ed.): philipallfrey. GitHub. 21.07.2024. [<ref target="https://github.com/philipallfrey/english-market-towns-1813">online</ref>]</item>
</list>
</ab>
</div>
<div type="chapter">
<head>1. Context</head>
<p>Historically the right to hold a regular fair or market in England
was regulated, most often by the grant of a royal charter. A new market charter
would not normally be granted to a town within 6 ⅔ miles of an existing market, in
order to protect its rights. A market town was thus an important focus of the local
economy, and a frequent destination for residents of the surrounding area. The
recipient of such a charter was the body with jurisdiction over the site of the
market, either the corporation of a borough (self-governing town), or the Lord of
the Manor. Consequently, market towns are associated either with a certain level of
urbanisation, or with a residence of a member of the gentry (landowning class).<note type="footnote"> For more on markets
see <ref type="bibliography" target="#letters_gazetters_2013">Letters 2013</ref>, Full Introduction.</note>
</p>
<p>My research focuses not on markets, but on the taxation of coats of
arms in Britain between 1798 and 1944.<note type="footnote">
<ref type="bibliography" target="#allfrey_duty_2019">Allfrey 2019</ref>.</note> Preliminary
studies have found these taxpayers were unevenly distributed throughout the country&#160;
either concentrated in certain urban areas, or isolated in rural areas. The latter
can easily be explained as members of the gentry on their estates, but the type of
urban areas in which taxpayers were concentrated needs to be further characterised.
While the number of taxpayers does increase with population, this is not the only
factor. Market towns provide a way of identifying ›important‹ towns which does not
depend on population or geographical size. </p>
<p>One of the most large-scale and fine-grained surviving record sets
of the armorial bearings tax contains the amount collected in each English parish
from 1802 to 1830.<note type="footnote">
The National Archives (UK), series E 182.</note> During this period two
censuses were taken (in 1811 and 1821), which also recorded their data by parish.
Thus, the location of market towns in one of these years would be ideal for
comparing to taxation and demographic records and producing geographic
visualisations. The closest that I could find was the 1813 edition of <bibl>
<title type="desc">Owen’s New Book of Fairs</title>
</bibl> (hereafter <bibl>
<title type="desc">Owen</title>
</bibl>) which forms the basis for the dataset described
below.<note type="footnote">
<ref type="bibliography" target="#owen_book_1813">Owen 1813</ref>. The dataset is available as
<ref type="bibliography" target="#allfrey_location_2024a">Allfrey 2024a</ref>.</note>
</p></div>
<div type="chapter">
<head>2. Data collection and processing</head>
<p>I used the British Library copy of <bibl>
<title type="desc">Owen</title>
</bibl> as digitised by Google Books, and performed three passes of data
entry and cleaning. The first pass transformed the printed text into markers on a
map. The second pass fine-tuned the location of the market and added identifiers
from the Ordnance Survey (OS) Open Names ontology. The third pass validated the data to ensure no
fields were missing.</p>
<div type="subchapter">
<head>2.1 First pass</head>
<p>On pages 1–86 of <bibl>
<title type="desc">Owen</title>
</bibl>
<hi rend="italic">,</hi> the author provides a county-by-county listing of towns
holding markets and fairs in a standardised format. For example, under
Buckinghamshire the entry ›Amersham** (26). Whit-Monday, Sept. 19, sheep. T‹ denotes
that the town of Amersham elects two Members of Parliament (MP) (two asterisks), lies 26
miles by road from London, holds sheep fairs annually on Whit-Monday (seven weeks
after Easter) and on September 19, and has a weekly market on a Tuesday. For my
purposes I wanted to record the name, county, distance, and number of MPs, and also
represent the location of the town on a map. Although a town has a spatial extent,
determining historic boundaries for some 700 towns would have significantly
increased the time taken, so I chose to represent each town by a simple point
marker. One obvious choice for the location of the marker is the centre of the town,
but many of these towns have changed size over the centuries, so the modern centre
of the town does not necessarily coincide with the historic one. This discrepancy
may prove significant when comparing to census or taxation data. However, since each
town in the dataset hosted a market, and the location of the market as of 1813 (the
publication date of <bibl>
<title type="desc">Owen</title>
</bibl>) is a well-defined quantity,
I chose to use this instead. I entered data using the free Mapbox Studio Dataset
Editor (hereafter the Mapbox Editor) as this application supports place name search,
point marker types, multiple properties per marker, and the ability to export data
in GeoJSON format.<note type="footnote">
<ref type="bibliography" target="#mapbox_hg_studio_2024">Mapbox 2024</ref>.</note> I followed the algorithm in <ref type="graphic" target="#english_markets_001">Figure 1</ref> in deciding where to add a marker to the map.</p>
<figure>
<graphic xml:id="english_markets_001" url="Medien/english_markets_001.png">
<desc>
<ref type="intern" target="#english_markets_001">Fig. 1</ref>: Algorithm for determining where to add a marker to
the map. [Philip Allfrey 2025]</desc>
</graphic>
</figure>
<p>In the ideal case, if an entry in <bibl>
<title type="desc">Owen</title>
</bibl> had at least one letter denoting a market day, I searched for that
town in the Mapbox Editor search, clicked on the correct result to zoom the map,
then clicked ›Add to Dataset‹. However, not all entries were this straightforward.
Wales has a different heraldic tradition to England, so I excluded towns in Welsh
counties from my research.<note type="footnote"> I excluded Anglesey, Brecknockshire, Cardiganshire,
Carmarthenshire, Carnarvonshire, Denbighshire, Flintshire, Glamorganshire,
Merionethshire, Monmouthshire, Montgomeryshire, Pembrokeshire, and
Radnorshire.</note> In a handful of cases the entry states something
like <quote>fortnight markets</quote>
<note type="footnote">
<ref type="bibliography" target="#owen_book_1813">Owen 1813</ref>, p.&#160;50 (Seeching,
Norfolk).</note> or <quote>large market on Thursday</quote>
<note type="footnote">
<ref type="bibliography" target="#owen_book_1813">Owen 1813</ref>, p.&#160;49 (Fakenham,
Norfolk).</note> rather than giving a day of the week. These towns were
included in the dataset. Due to the quality of the printing or the digitisation, the
town names are often hard to read. In these cases I performed a Google search for my
best guess at the name, together with the county as given in <bibl>
<title type="desc">Owen</title>
</bibl>, plus the phrase ›market town‹, e. g. ›Annerstam
Buckinghamshire market town‹. This was usually sufficient to find the correct town
(Amersham in this example), either via Google’s autocorrect ›Showing results for…‹
feature, or a search result for a Wikipedia article beginning ›X is a market town in
Y county‹, which I judged to be a sufficiently reliable source on such points of
fact. When searching for the town in the Mapbox Editor I often encountered zero or
multiple results with the correct name and county. The latter sometimes occurred for
the town and an eponymous region, e. g. ›Bedford, Bedford, England, United Kingdom‹
and ›Bedford, England, United Kingdom‹. In this case I chose the more specific
location to add to the dataset. In the other cases of ambiguous or non-existent
results, a check in Wikipedia was sufficient to identify the correct town; I used
the coordinates from the Wikipedia article to locate the correct place within the
Mapbox Editor, and manually added a point to the dataset by clicking with the point
marker tool.</p>
<p>Once the marker was added I used the property panel in the Mapbox
Editor to enter the name, county, distance from London, and number of MPs (if any)
for that town. If the modern name of the town was different from the name used in
<hi rend="italic">Owen</hi>, I added the modern name in an <hi rend="italic">alternate_name</hi> property. This affected 163 of 698 towns. Of these
approximately 55&#160;% comprised simple addition or deletion of letters or punctuation,
representing changes in spelling or pronunciation over time, e. g. Ashborn →
Ashbourne, Culliton → Colyton, Hales-Owen → Halesowen. A further 40&#160;% involved
adding or removing a descriptor, e. g. East Dereham → Dereham, Lyme → Lyme Regis. The
remaining 5&#160;% are given in <ref type="graphic" target="#tab_001">Table 1</ref>. In the
special case of London which had several markets, I added the name of the market
(Smithfield) in the <hi rend="italic">alternate_name</hi> field.</p>
<table xml:id="tab_001">
<row>
<cell>
<hi rend="bold">Name in </hi>Owen</cell>
<cell>
<hi rend="bold">Modern
name</hi>
</cell>
</row>
<row>
<cell>Beaminster, Dorsetshire</cell>
<cell>Blandford Forum</cell>
</row>
<row>
<cell>Brighthelmstone, Sussex</cell>
<cell>Brighton</cell>
</row>
<row>
<cell>Adwalton, Yorkshire</cell>
<cell>Drighlington</cell>
</row>
<row>
<cell>Marketjew, Cornwall</cell>
<cell>Marazion</cell>
</row>
<row>
<cell>Croesoswallt, Shropshire</cell>
<cell>Oswestry</cell>
</row>
<row>
<cell>Oakingham, Berkshire</cell>
<cell>Wokingham</cell>
</row>
<row>
<cell>Ambresoury, Wiltshire</cell>
<cell>Amesbury</cell>
</row>
<trailer>
<ref type="intern" target="#tab1">Tab. 1</ref>: Place names in <title type="desc">Owen</title>
which are non-trivially different from modern names. [Philip Allfrey 2025]</trailer>
</table>
<p>For the few cases where <hi rend="italic">Owen</hi> did not provide
a distance from London for the town, I used the distance given in W. C. Oulton, <bibl>
<title type="desc">Traveller's Guide, or English Itinerary</title>
</bibl>, rounded to
the nearest mile.<note type="footnote">
<ref type="bibliography" target="#oulton_guide_1805a">Oulton 1805</ref>.</note> If this also did
not give a distance, I determined a value by finding the nearest town on a route
radially outward from London for which <bibl>
<title type="desc">Owen</title>
</bibl> provided
a distance, then adding the modern driving distance between these two towns as
suggested by Google Maps, rounded to the nearest mile.</p></div>
<div type="subchapter">
<head>2.2 Locating the market</head>
<p>Initially I had accepted the location of the town provided by the
›Add to dataset‹ button from the Mapbox Editor search results, with the intention of
moving this point to the location of the market in the second pass over the data.
However, at the time of initial data entry (2018) using this button had the side
effect of adding information from Wikidata to the properties for that marker. For
consistency with the markers I placed manually (for ambiguous or non-existent search
results in the Mapbox Editor) I chose to remove the extra Wikidata information by
opening the GeoJSON panel in the Mapbox Datset Editor and deleting the relevant
lines from the properties object. As the first pass progressed it became apparent
that it was more efficient for me to place all markers manually, rather than
clicking ›Add to Dataset‹ then deleting Wikidata information. Consequently, I
decided to change my process to locate the market before placing the marker, as
shown in <ref type="graphic" target="#english_markets_002">Figure 2</ref>.</p>
<figure>
<graphic xml:id="english_markets_002" url="Medien/english_markets_002.png">
<desc>
<ref type="intern" target="#english_markets_002">Fig. 2</ref>: Algorithm for identifying the location of the market
within the town. [Philip Allfrey 2025]</desc>
</graphic>
</figure>
<p>In the ideal case I used the ›Standard Satellite‹ background style
in the Mapbox Editor to locate the geographic centre of the town by eye, and find a
large open space adjacent to the main street, or a wide or boat-shaped street
nearby. If such as place was named ›Market Place‹, ›Market Square‹ or ›Market Hill‹,
I took this as sufficient evidence of the market location. Roads named ›Market St‹
or (in Devon and Cornwall) ›Fore St‹ were in general not the site of the market, but
the road leading to it, so further information was required to locate the market
(<ref type="intern" target="#hd5">see below</ref>). If the aerial photograph showed
areas with a highly uniform street pattern, I excluded these as being likely
mid-19<hi rend="super">th</hi> century or later developments, and
attempted to find the historic core of the town at the centre of the remaining urban
area. If I could not find the historic centre by eye I used the road names, looking
first for ›High St‹, and failing that, for road names referring to compass
directions (e. g. ›North St‹), neighbouring towns (e. g. ›York Rd‹), or older words
for road (e. g. ›Hungate‹). If this was not sufficient, particularly for towns which
have significantly expanded since 1813, I applied the above algorithm to the 19<hi rend="super">th</hi> century Ordnance Survey maps as digitised by the
National Library of Scotland.<note type="footnote">
<ref type="bibliography" target="#nlos_hg_side_2024">National Library of Scotland
2024</ref>.</note>
</p>
<p>For the 353 towns where I could not definitively identify the market
from cartographic evidence, I consulted other sources to find the location. For 80&#160;%
of these towns I was able to find a map or verbal description of the market location
in reports produced for the relevant county councils by professional archaeologists
and historians, or in published academic studies such as the Victoria County History
series.<note type="footnote">See e. g. <ref type="bibliography" target="#historicengland_hg_2013">Historic England 2013</ref> and <ref type="bibliography" target="#ihr_hg_history_2024">Institute of Historical Research 2024</ref>.</note>
For a further 9&#160;% I was able to find the market location from local history or
museum websites, historical texts digitised by Google Books, or in four cases, a
Wikipedia article. For the remaining 11&#160;% I was unable to find, or find confirmation
of, the location of the market; for these towns I added a <term type="dh">location_uncertain</term> field with a value of 1 to the
marker properties, and placed the marker in the centre of the High St (or
equivalent), near a crossroad, if any. When searching these texts if the location of
the general market was different to that of the livestock market, I chose the
general market location. In the small number of cases where the town had two general
market locations, I chose the larger or more easily identified.</p>
<p>At the end of this pass, I exported my dataset as a GeoJSON file
from the Datasets page within Mapbox Studio.</p></div>
<div type="subchapter">
<head>2.3 Second pass</head>
<p>For greater accuracy in placing the marker at the market location, I
wanted to visualise the position obtained from the first pass on the 25-inch
Ordnance Survey Maps from the late
19<hi rend="super">th </hi>/ early 20<hi rend="super">th</hi> century. These often provided a clearer picture of
the extent of the market place, as they predate more than a century of subsequent
urban development and demolition of features such as market crosses. The National
Library of Scotland ›Side by Side viewer‹ website in which I consulted the OS maps
does not offer the functionality of importing a list of points, nor of marking a
point of interest, so I wrote a browser extension to facilitate this
comparison.<note type="footnote"> See <ref type="bibliography" target="#allfrey_duty_2019">Allfrey 2018</ref>. A browser extension is
JavaScript code which, when activated in a web browser, modifies the
contents of a web page while it is being viewed in that browser, either on
demand, or when the website meets certain criteria. The extension I wrote
only works on the ›Side by Side viewer‹ page.</note>
</p>
<p>My extension loads the GeoJSON file from the first pass and inserts
a panel at the top of the ›Side by Side viewer‹ page with a county selector
dropdown, and ›Previous feature‹ and ›Next feature‹ buttons to allow navigating
through the points in this file (see <ref type="graphic" target="#english_markets_003">Figure
3</ref>). When a new town is selected via this navigation, the maps zoom to that
location, a marker is shown at the coordinates entered during the first pass, and
the data from <bibl>
<title type="desc">Owen</title>
</bibl> entered as properties of the
marker appear in the top panel. At the start of each session I needed to manually
select the maps shown in the ›Side by Side viewer‹. I usually chose ›OS 25 Inch,
1892–1914‹ for the left-hand pane as it had the highest resolution, though sometimes
the earlier ›OS Six inch, 1830s–1880s‹ had useful information. For the right-hand
pane I used ›MapTiler Satellite Hybrid‹. During the course of this project the
National Library of Scotland released a new version of the ›Side by Side viewer‹,
which is incompatible with my browser extension, so that as of the time of writing
(December 2024) it is necessary to click the link to use the former ›Side by Side
viewer‹.</p>
<figure>
<graphic xml:id="english_markets_003" url="Medien/english_markets_003.png">
<desc>
<ref type="intern" target="#english_markets_003">Fig. 3</ref>: Screenshot of the National Library of Scotland’s Side
by Side map viewer, with my browser extension enabled. [Left panel: CC-BY (NLS).
Right panel: <ref target="http://www.openstreetmap.org/copyright">OpenStreetMap</ref>]</desc>
</graphic>
</figure>
<p>My algorithm for the second pass is shown in <ref type="graphic" target="#english_markets_004">Figure 4</ref>. I worked alphabetically by county
and town and compared the data from <bibl>
<title type="desc">Owen</title>
</bibl> with that
entered in the first pass, as displayed by my browser extension. If there were any
errors or omissions, I located that town within the Mapbox Editor, and corrected the
data there. I did not re-export the data from Mapbox until the end of this pass. If
the town did not have sufficient cartographic evidence for the market location, I
searched for and added a reference as described in <ref type="intern" target="#hd4">section 2.2</ref>.</p>
<figure>
<graphic xml:id="english_markets_004" url="Medien/english_markets_004.png">
<desc>
<ref type="intern" target="#abb4">Fig. 4</ref>: Algorithm for the second pass through the data. [Philip Allfrey 2025]</desc>
</graphic>
</figure>
<p>For consistency I decided to place the marker at the focal point of
the market place, if there was one (e. g. market cross or market hall), otherwise in
the centre of the market place. For towns where I could not confirm the location of
the market, I placed the marker in the centre of the main street, near a crossroads
if any. If the marker from the first pass was not in the correct location according
to these criteria, I moved the cursor to the correct place in the ›Side by Side
viewer‹, and noted the latitude and longitude at the bottom of the screen. I
transferred the decimal version of these coordinates to the GeoJSON panel in the
Mapbox Editor.</p>
<p>During the first pass I noticed that there was frequently a road
named ›Silver Street‹ near the market. To tag these for further study I searched by
eye for Silver Street after confirming the market location. I added a numeric <term type="dh">silver_street</term> property to the town in the Mapbox
Editor, with a value of 1 if Silver Street led into the market or 0.75 if Silver
Street connected to a road leading to the market. If I could not find Silver Street
near the market location, I searched Google Maps for ›Silver St‹ plus the name of
the town. If this returned a result elsewhere in the town, I entered a value of 0.5
for <term type="dh">silver_street</term>. Approximately 10&#160;% of towns had
a Silver Street.</p>
<p>Finally, to allow this dataset to be more easily connected to other
datasets, I changed the <term type="dh">ID</term> field for each marker
from the default alphanumeric string to a standard identifier. Since these towns are
all in the UK I chose the Ordnance Survey Open Names identifiers.<note type="footnote">
<ref type="bibliography" target="#ordnancesurvey_hg_names_2023">Ordnance Survey 2023</ref>.</note> At the
time of initial data entry (2018) the Ordnance Survey provided a Linked Data
reconciliation endpoint. When queried with the name of a town, this service would
return a list of possible matches. I clicked through for each result and consulted
the map and data on the Open Names entry until I determined the correct identifier,
making sure to check the place type to avoid selecting the identifier for a railway
station with the same name as the town. When I had located the correct identifier, I
copied and pasted it into the <term type="dh">ID</term> field at the
bottom of the Mapbox Editor.</p>
<p>By 2023 this reconciliation endpoint had been removed, and replaced
with a download of the dataset in various formats. I downloaded the July 2023
version as a ZIP file containing one CSV for each square on the British National
Grid. This was unwieldy to work with so I joined all 819 files into a single CSV
file by using the Linux <term type="dh">cat</term> command within the Git
Bash Shell on my Windows computer. Because this concatenated file contained over 3
million rows it could not be opened in Microsoft Excel. Instead, I opened the file
with Modern CSV, sorted it by the LOCAL_TYPE column and deleted all rows which
corresponded to names for non-populated places based on their value for LOCAL_TYPE
(e. g. Railway Stations, Postcodes, Sections of Named Roads), and all rows where the
place lay outside England. To reduce the need to scroll along the row when checking
for the right identifier, I also removed extraneous columns.<note type="footnote"> See <ref type="bibliography" target="#galliumdigital_hg_csv_2024">Gallium Digital 2024</ref>. The remaining
columns are ID, NAMES_URI, NAME1, NAME1_LANG, NAME2, NAME2_LANG, TYPE,
LOCAL_TYPE, POPULATED_PLACE, POPULATED_PLACE_URI, POPULATED_PLACE_TYPE,
COUNTY_UNITARY, COUNTY_UNITARY_URI, COUNTY_UNITARY_TYPE, COUNTRY,
RELATED_SPATIAL_OBJECT, SAME_AS_DBPEDIA, and SAME_AS_GEONAMES.</note>
The final spreadsheet contained 33382 rows and 18 columns, which I sorted
alphabetically by the NAME1 column. To find the identifiers for the remaining market
towns I used the Ctrl+F search feature within this spreadsheet. This turned out to
be faster than the reconciliation endpoint because I could see the county without
having to click through to another screen, and the false positives (e. g. railway
stations) had already been removed.</p>
<p>Once I had completed the second pass, I re-exported the data from
the Mapbox Editor as a GeoJSON file.</p></div>
<div type="subchapter">
<head>2.4 Third pass</head>
<p>Because I had entered data in multiple stages over several years, I
wanted to do a sanity check to ensure there were no errors or missing fields. My
first check was to zoom out the map in the Mapbox Editor to confirm that all the
markers were within the boundaries of England. I carried out the remaining checks
programmatically to avoid human error when scanning large amounts of data. Because
the dataset was already in GeoJSON format, it was easiest for me to perform these
checks in an interactive JavaScript session in a web browser. The abbreviated
transcript of a JavaScript session below shows the commands I executed in the
Console tab of the Developer Tools in Google Chrome. It was used to programmatically
check for data errors. Lines starting with &gt; denote input, lines starting with
&lt; denote output. Large sections of data have been replaced by an ellipsis ›(…)‹.
The data structure of the GeoJSON file is described in <ref type="intern" target="#hd7">section 3</ref>. </p>
<list type="ordered">
<item><code>&gt; const geojson = {...} // Copy and paste entire GeoJSON dataset</code></item>
<item><code>&lt; {features: Array(698), type: 'FeatureCollection'}</code></item>
<item><code>&gt; const features = geojson.features</code></item>
<item><code>&gt; features.length</code></item>
<item><code>&lt; 698</code></item>
<item><code>&gt; const ids = features.filter(x =&gt; x.id.startsWith('http://data.ordnancesurvey.co.uk/id/')).length</code></item>
<item><code>&lt; 698</code></item>
<item><code>&gt; const properties = features.map(x =&gt; x.properties)</code></item>
<item><code>&gt; const placenames = properties.filter(x =&gt; !!x.place_name).length</code></item>
<item><code>&lt; 698</code></item>
<item><code>&gt; const counties = properties.filter(x =&gt; !!x.county).length</code></item>
<item><code>&lt; 698</code></item>
<item><code>&gt; const distances = properties.filter(x =&gt; !!x.distance_to_london).length</code></item>
<item><code>&lt; 697</code></item>
<item><code>&gt; properties.filter(x =&gt; !x.distance_to_london)</code></item>
<item><code>&lt; 0: {alternate_name: 'Smithfield', county: 'Middlesex', distance_to_london: 0, number_mps: 4, place_name: 'London'}</code></item>
<item><code>&gt; const openNames = {...} // Copy and paste reduced OS Open Names in JSON format</code></item>
<item><code>&lt; (33381) [...]</code></item>
<item><code>&gt; let openNamesLookup = {}</code></item>
<item><code>&gt; for(const place of openNames) {</code></item>
<item><code> openNamesLookup[place.NAMES_URI] = place;</code></item>
<item><code> }</code></item>
<item><code>&gt; const names = features.filter(x =&gt; {</code></item>
<item><code> const id = x.id;</code></item>
<item><code> const openName = openNamesLookup[id];</code></item>
<item><code> return openName.NAME1 !== x.properties.place_name</code></item>
<item><code> &amp;&amp; openName.NAME1 !== x.properties.alternate_name;</code></item>
<item><code> })</code></item>
<item><code>&gt; names.length</code></item>
<item><code>&lt; 3</code></item>
<item><code>&gt; const mismatchedNames = names.map(x =&gt; {</code></item>
<item><code> return {</code></item>
<item><code> place_name: x.properties.place_name,</code></item>
<item><code> alternate_name: x.properties.alternate_name,</code></item>
<item><code> open_name: openNamesLookup[x.id].NAME1</code></item>
<item><code> }</code></item>
<item><code>})</code></item>
<item><code>&gt; console.table(mismatchedNames)</code></item>
<item><code>(index) place_name alternate_name open_name</code></item>
<item><code>0 'Sutton' 'Sutton Coldfield' 'Royal Sutton Coldfield'</code></item>
<item><code>1 'Barnet' 'High Barnet' 'Chipping Barnet'</code></item>
<item><code>2 'Sherburne' 'Sherburn in Elmet' 'Sherburn'</code></item>
</list>
<p>I assigned the GeoJSON data to a variable, and extracted the
features array, which contains an ID and set of properties for each market town. For
every attribute I wanted to check I used the <term type="dh">Array.filter</term> function to return a new array satisfying a logical test
(e. g. the attribute is a non-empty value). By comparing the length of the filtered
array to the length of the features array I could determine whether there were any
towns which were missing properties. Where the counts did not match, I inverted the
<term type="dh">Array.filter</term> condition to display the affected
entries, then added the missing information in the Mapbox Editor.</p>
<p>In order to programmatically check that I had copied and pasted the
correct OS Open Names identifier as the ID for each market town, I converted the
reduced spreadsheet into JSON format, then constructed a lookup table from OS Open
Names identifier to name.<note type="footnote"> I used ConvertCSV (<ref type="bibliography" target="#datadesigngroup_convertcsv_2024">Data Design Group 2024</ref>) to perform the
conversion.</note> Iterating over the features array I compared the
<term type="dh">place_name</term> and <term type="dh">alternate_name</term> fields to the name from the lookup table. This
highlighted cases where I had not copied the whole identifier, pasted the identifier
from a previous town, or chosen the wrong identifier when using the reconciliation
endpoint. I corrected the affected identifiers in the Mapbox Editor, then exported
the data for a final time in GeoJSON format. After converting the dataset to CSV I
uploaded both formats to GitHub and the Knowledge Commons Works repository.<note type="footnote">
<ref type="bibliography" target="#allfrey_location_2024a">Allfrey 2024a</ref> and <ref type="bibliography" target="#allfrey_market-towns_2024b">2024b</ref>.</note>
</p></div></div>
<div type="chapter">
<head>3. Data structure</head>
<p>The GeoJSON version of the dataset has the format shown below&#160;– a
FeatureCollection with an array of 698 features, one for each market town. Each
feature consists of an ID, a Point geometry, and a list of properties.</p>
<list type="ordered">
<item>
<code>{</code>
</item>
<item>
<code> "type": "FeatureCollection"</code>
</item>
<item>
<code> "features": [</code>
</item>
<item>
<code> {</code>
</item>
<item>
<code> "id": "http://data.ordnancesurvey.co.uk/id/4000000074542156",</code>
</item>
<item>
<code> "geometry": {</code>
</item>
<item>
<code> "coordinates": [</code>
</item>
<item>
<code> -0.333735,</code>
</item>
<item>
<code> 53.740772</code>
</item>
<item>
<code> ],</code>
</item>
<item>
<code> "type": "Point"</code>
</item>
<item>
<code> },</code>
</item>
<item>
<code> "properties": {</code>
</item>
<item>
<code> "alternate_name": "Kingston upon Hull",</code>
</item>
<item>
<code> "county": "Yorkshire",</code>
</item>
<item>
<code> "distance_to_london": 173,</code>
</item>
<item>
<code> "number_mps": 2,</code>
</item>
<item>
<code> "place_name": "Hull",</code>
</item>
<item>
<code> "reference": "http://www.british-history.ac.uk/vch/yorks/east/vol1/pp407-412#p3",</code>
</item>
<item>
<code> "silver_street": 1</code>
</item>
<item>
<code> },</code>
</item>
<item>
<code> "type": "Feature"</code>
</item>
<item>
<code> },</code>
</item>
<item>
<code> ...</code>
</item>
<item>
<code> ]</code>
</item>
<item>
<code>}</code>
</item></list>
<p>The <term type="dh">ID</term> is taken from the July 2023
version of the Ordnance Survey Open Names dataset. The <term type="dh">coordinates</term> for the <term type="dh">geometry</term> are given in
the order [longitude, latitude]. The <term type="dh">alternate_name</term>
property is used when the modern name for the town differs from that used in <bibl>
<title type="desc">Owen</title>
</bibl>
<hi rend="italic">.</hi> The <term type="dh">county</term> property uses the name as given in <bibl>
<title type="desc">Owen</title>
</bibl>. The <term type="dh">distance_to_london</term> is the value in miles given in <bibl>
<title type="desc">Owen</title>
</bibl> for the distance by road from the market town to
London<hi rend="italic">.</hi> The <term type="dh">number_mps</term>
property is the number of Members of Parliament returned by the town, and can be 1,
2, or in the case of London 4, since this edition of <bibl>
<title type="desc">Owen</title>
</bibl> was published before the electoral reforms of the 1830s. The <term type="dh">place_name</term> is the town name given in <bibl>
<title type="desc">Owen</title>
</bibl>. The <term type="dh">reference</term> property
contains the URL of a source providing a visual or verbal description of the
location of the market, in cases where there is not sufficient cartographic
evidence, as explained above in <ref type="intern" target="#hd4">section 2.2</ref>. The
<term type="dh">silver_street</term> property records whether there is
a road named ›Silver Street‹ in proximity to the historic market place and can take
the values 1 (Silver Street leads into the market place), 0.75 (Silver Street is off
a road leading into the market place), or 0.5 (Silver Street is elsewhere in the
town). For towns where I was unable to confirm the location of the market, the
feature has a <term type="dh">location_uncertain</term> property with the
value 1.</p>
<p>The CSV version of the file contains a header row followed by 698
rows comprising the data for each market town. The column headers are <term type="dh">id, latitude, longitude, place_name, county,
alternate_name, distance_to_london, number_mps, silver_street,
location_uncertain</term>, and <term type="dh">reference</term>. The
reference column was placed last for ease of use as this column contained the most
text.</p></div>
<div type="chapter">
<head>4. Limitations and related work</head>
<p>The primary limitation of a dataset such as this is the reliability
of the underlying source, in this case <bibl>
<title type="desc">Owen’s New Book of
Fairs</title>
</bibl>. The title implies that market towns which did not hold a fair are
not included. I checked for potential lacunae in <bibl>
<title type="desc">Owen</title>
</bibl>
simply by plotting my geolocated dataset on a map and looking for areas with an
absence of markers (see <ref type="graphic" target="#english_markets_005">Figure 5</ref>). Chagford,
on the edge of Dartmoor falls in one such hole, and indeed is an example of a town
with a market but not a fair. In each of six other large lacunae I was able to find
at least one fair town which had an active market in 1813 when <bibl>
<title type="desc">Owen</title>
</bibl> was published, which suggests that the compiler
did not achieve perfect coverage: Burnham Market (Norfolk), Glossop (Derbyshire),
Market Lavington (Wiltshire), Stanhope (County Durham), Driffield and Whitby
(Yorkshire).</p>
<figure>
<graphic xml:id="english_markets_005" url="Medien/english_markets_005.png">
<desc>
<ref type="intern" target="#abb5">Fig. 5</ref>: Dataset points shown on a map (blue) with examples of
markets not in Owen (red). [Map data and imagery <ref target="https://www.mapbox.com/about/maps/">Mapbox</ref> and <ref target="http://www.openstreetmap.org/copyright">OpenStreetMap</ref>].</desc>
</graphic>
</figure>
<p>During the course of compiling this dataset, I became aware of
another list of market towns which is contemporary to <bibl>
<title type="desc">Owen</title>
</bibl>. In July 1822 a return was made to the House of Commons detailing the
<bibl>
<title type="desc">Population of all the Market Towns and Boroughs in
England, with the Population of the Principal Towns of Scotland and Wales</title>
</bibl>;
the following year it was published as an appendix to a history of Yorkshire.<note type="footnote">
<ref type="bibliography" target="#baines_history_1823">Baines 1823</ref>, Vol. 2, p.&#160;611–614.
</note> Comparing the two lists shows they have 628 market towns in common,
with 68 found only in <bibl>
<title type="desc">Owen</title>
</bibl>, and 102 found only in the
1822 list. Part of the discrepancy may lie in whether there was an active market in
these towns, or whether the market was of recent date. In both of these cases the
town was unlikely to be an attractor for armorial bearings taxpayers. In the first
instance I plan to compare my taxation data to the subset of market towns common to
both <bibl>
<title type="desc">Owen</title>
</bibl> and the 1822 list.</p>
<p>In the case of the remaining towns, the fact that the two lists
conflict means further research is required to determine whether there was a market
operating at that time. Nor can it be assumed that these two lists between them have
achieved complete coverage of all market towns at the time of compilation.
Independently identifying all active English market towns is a significant
undertaking, which is out of scope for my research. However, David Lawrenson has
recently completed a PhD which does precisely this. His thesis <bibl>
<title type="desc">Commerce and Place: markets in the English landscape,
1086-2000</title>
</bibl> builds on contemporary lists (including the two discussed above)
and prior studies (including Samantha Letters’ <bibl>
<title type="desc">Gazetteer
of Markets and Fairs in England and Wales to 1516</title>
</bibl>) with substantial
archival research, and use of non-documentary sources such as place names, market
crosses and halls, coin finds, and village morphology.<note type="footnote">
<ref type="bibliography" target="#lawrenson_commerce_2023">Lawrenson 2023</ref>. See also <ref type="bibliography" target="#letters_gazetters_2013">Letters 2013</ref>.</note> The resulting
dataset has 50 variables characterising each town, and records 861 markets which
were active around 1812, dropping to 779 in 1822. He does not appear to record the
location of the market, however, only of the town.</p>
<p>Another limitation of my dataset is that the identifier chosen for
the towns&#160;– their OS Open Names identifier. While still valid as a distinguishing
string, as a URL it no longer resolves to anything other than an end-of-life page.
This prevents easy matching with other datasets. The OS Open Names download does
provide SAME_AS_GEONAMES and SAME_AS_DBPEDIA fields to enable a crosswalk to these
identifiers; one or both of these values is present for 87&#160;% of towns in my dataset.
In a future iteration of my dataset these could perhaps be added as additional
properties on each feature. Lawrenson’s thesis does not use any third-party
identifiers, however Stephen Gadd has done some work on geolocating markets which
is, or will be, available as Linked Open Data. On the <title type="desc">Viae
Regiae</title> project, which aims to reconstruct the transport network of early
modern England and Wales, Gadd and collaborators used the open-source tool <hi rend="italic">Recogito</hi> to annotate Christopher Saxon’s 16<hi rend="super">th</hi> century maps of England, and georeference places
against the built-in <hi rend="italic">GeoNames</hi> gazetteer.<note type="footnote">
<ref type="bibliography" target="#pelagionscommons_hg_recogito_2024">Pelagios Commons 2024</ref>.</note> This
dataset is available for download.<note type="footnote">
<ref type="bibliography" target="#gadd_et_al_datacollection_2024a">Gadd 2024a</ref>.</note> Gadd is currently
extending Letters’ <bibl>
<title type="desc">Gazetteer of Markets and Fairs</title>
</bibl>
from its endpoint of 1516 up to the 19<hi rend="super">th</hi> century.<note type="footnote">
<ref type="bibliography" target="#gadd_correspndence_2024b">Gadd 2024b</ref>.</note> He has also
written a browser-based tool, <title type="desc">Locolligo</title> to
facilitate the linking of place data to gazetteers such as <title type="desc">Wikidata</title> and <title type="desc">GB1900</title>.<note type="footnote">
<ref type="bibliography" target="#gadd_locolligo_2022">Gadd 2022</ref>. <ref type="bibliography" target="#nlos_hg_gazetter_2018">National Library of Scotland 2018</ref>.</note> When complete this market dataset will be uploaded to the <bibl>
<title type="desc">World Historical Gazetteer</title>
</bibl>, where it can be queried
via an API.<note type="footnote">
<ref type="bibliography" target="#whg_version_2024">World Historical Gazetteer 2019</ref>.</note>
</p>
</div>
</body>
<back>
<div type="bibliography">
<head>Bibliography</head>
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<bibl xml:id="allfrey_location_2024a">Philip Allfrey (2024a): Location of Markets in English Market
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</bibl>
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</listBibl>
</div>
</back>
</text>
</TEI>
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