Filters
Selected Filters
None
Available Filters
Modern Countries and Continents
File Formats
Network Tags
Network Topics
Node Topics
Edge Topics
Directionality
Weighted
General

Search Fields

The following fields can be used for targeting a specific field as described in the query syntax below.

canonicalCitation
collectionTags
description
fileFormats
id
name
tags.txt
authors.firstName
authors.lastName
authors.orcidId
construction.additionalComments
construction.description
construction.sources.sourceTypes.txt
license.licenseId
relatedPublications.canonicalCitation
structure.directionality
structure.weighted
topic.edgeAttributes.txt
topic.edgeTopics.txt
topic.modernCountriesAndContinents.name
topic.networkTopics.txt
topic.nodeAttributes.txt
topic.nodeTopics.txt

Query Syntax

TitleOperatorExampleDescription
Phrase / Exact match
""
"Roman"
"Roman Road Network"
The term or phrase must be matched exactly (case insensitive) to get a match.
Field Search
FIELD_NAME:()
name:(Roman Road Network)
name:("Roman Road Network")
Field searches makes it possible to narrow the search to a specific field instead of searching all fields. The same operators as used in a normal search can be applied to field searches.
Wildcard
?, *
Roma?
Ro*
Search words including or ending with and an unknown set of characters. The wildcard
?
matces a single character and
*
matches 0-n characters.
Fuzzy
~
Roma~
Squire~
Find words which are similar (spelling wise) to the given word. Good for finding misspelled words. The examples could e.g. result in "Roma, Roman, Rome" or "Squire, Super, Squibb".
Given the length of the word different rules apply *:
[0-2]:
No fuzzyfication is applied - the word must match exactly
[3-5]:
One edit** is allowed
[6-*]:
Two edits** are allowed
* The default rules for edits can be overwritten by applying one of [0, 1, 2] after the "~", where the number specifies the number of edits allowed.
** An edit is an insertion, deletion or substitution of a character.
Must
+
+Roman Road +Network
+name:(Roman Road)
+"Roman Road" Network
Express which terms must be present to get a match:
+Roman Road +Network
Both "Roman" and "Network" must be present, "Road" is not required but would make a better result if present
+name:(Roman Road)
One of the terms must be present in the title field (If all terms must be present prefix each term with a "+")
+"Roman Road"
The exact phrase must be present
Must Not
-
-Roman Road
-name:(Roman Road)
-"Roman Road" Network
Express which terms must not be present to get a match:
-Roman Road
"Roman" must not present
-name:(Roman Road)
One of the terms must not be present in the title field (If all terms must not be present prefix each term with a "-")
-"Roman Road"
The exact phrase must not be present
Grouping
( )
(+Roman +Road) (+Ancient +Network)
Group expressions together to form sub-queries. The Example reads: match ("Roman" and "Road") or ("Ancient" and "Network").
time
1 - 1 / 1
Authors
|
Jack Hanson
|
Maintainers
Formats
csv, other
Nodes
-
Edges
-
Years
-20001800
Access
|
Added
2025-12-01
24

"Cities and towns have often developed infrastructure that enabled a variety of socio-economic interactions. Street networks within these urban settings provide key access to resources, neighborhoods, and cultural facilities. Studies on settlement scaling have also demonstrated that a variety of urban infrastructure and resources indicate clear population scaling relationships in both modern and ancient settings. This article presents an approach that investigates past street network centrality and its relationship to population scaling in urban contexts. Centrality results are compared statistically among different urban settings, which are categorized as orthogonal (i.e., planned) or self-organizing (i.e., organic) urban settings, with places having both characteristics classified as hybrid. Results demonstrate that street nodes have a power law relationship to urban area, where the number of nodes increases and node density decreases in a sub-linear manner for larger sites. Most median centrality values decrease in a negative sub-linear manner as sites are larger, with organic and hybrid urban sites’ centrality being generally less and diminishing more rapidly than orthogonal settings. Diminishing centrality shows comparability to modern urban systems, where larger urban districts may restrict overall interaction due to increasing transport costs over wider areas. Centrality results indicate that scaling results have multiples of approximately ⅙ or ⅓ that are comparable to other urban and road infrastructure, suggesting a potential relationship between different infrastructure features and population in urban centers. The results have implications for archaeological settlements where urban street plans are incomplete or undetermined, as it allows forecasts to be made on past urban sites’ street network centrality. Additionally, a tool to enable analysis of street networks and centrality is provided as part of the contribution."

From https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259680

Tags
cities
roads
street
Modern Countries and Continents
Africa
Asia
Europe
Structure
Directionality
undirected
Weighted
no
Hypergraph
no
Longitudinal
no
Multigraph
no
Multilayer
yes
Multipartile
-
Probabilistic
no
Self Loops
no
Signed
no
Spatial
yes
Canonical Citation
Altaweel, Mark; Hanson, John; Squitieri, Andrea (2021). The structure, centrality, and scale of urban street networks: Cases from Pre-Industrial Afro-Eurasia: Supplementary Material. University College London. Dataset. https://doi.org/10.5522/04/15191601.v1
Funding
The Center of Advanced Studies-Schwerpunkt (Siedlungen zwischen Diversität und Homogenität) from the University of Munich (LMU) provided funding for this effort.
Network Topics
city
roads
streets
Node Topics
junction
site
Edge Topics
centrality
Node Attributes
latitude
longitude
Edge Attributes
-
Uncertainties
Nodes
-
Edges
-
Node Attributes
-
Edge Attributes
-
Statistics
Avg. Clustering Coefficient
-
Avg. Degree
-
Construction

Data: The subfolders contain the shapefile data for site street networks. Each subfolder represents a site analysed or where data were gathered. The subfolders also contain network centrality output and other network data; the nodeCentrality.csv file in the output subfolder is the file that contains centrality outputs used in the analysis for each site.

centrality_results_median.csv: The file contains centrality results for different urban places collected. Data indicate: the urban name, area, number of nodes (No. Nodes), node density (No. Nodes/Area), different median centrality measures (e.g., betweenness, closeness, etc.), period streets date to, the street period, which is a simplified indication for the main period the street likely dates to, Gini coefficient values that show disparity within centrality values, type of cities (orthogonal, organic, or hybrid (i.e. mixed), region where the data are from, and how complete the data are (complete, mostly complete, or partially complete). References are provided for sources where data are provided. Additionally, a Global Efficiency, which measures how well nodes communicate or connect in a network (Porta et al. 2006), metric is given as a measure for overall centrality, although the values are not used in the research paper’s analysis.

Locations: This folder provides the shapefile locations of the urban data used.

StreetCentrality.zip: This is the Python (3.6) project used to conduct the network analysis. The code is provided along with information on running the code and analysis conducted.

Sources

Yale University

Source Types
map
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