2016
DOI: 10.1038/sdata.2016.34
|View full text |Cite
|
Sign up to set email alerts
|

Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000

Abstract: How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000,… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
79
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
7
2
1

Relationship

0
10

Authors

Journals

citations
Cited by 97 publications
(79 citation statements)
references
References 26 publications
0
79
0
Order By: Relevance
“…Also the coordinates associated with the extracted locations by Nominatim were manually checked, since the geographical information associated with trajectories is at the core of our migration model and possible errors must be minimized. These are then collapsed to the nearest great city, where we adopted as a definition of great cities the list proposed in [25]. In their work, Reba et al [25] collected also precious historical demographic data for most of these cities, that we used to test our baseline for the migration model.…”
Section: Dataset Compositionmentioning
confidence: 99%
“…Also the coordinates associated with the extracted locations by Nominatim were manually checked, since the geographical information associated with trajectories is at the core of our migration model and possible errors must be minimized. These are then collapsed to the nearest great city, where we adopted as a definition of great cities the list proposed in [25]. In their work, Reba et al [25] collected also precious historical demographic data for most of these cities, that we used to test our baseline for the migration model.…”
Section: Dataset Compositionmentioning
confidence: 99%
“…One useful track that was made possible by the availability of data and computational capability to handle them is a quantitative historical analysis, wherein the current state of the city may be understood by studying the spatial patterns left by urbanization [16]. And among these spatial features, the road networks are the ones that outlast the other physical elements [17,18], and, sometimes, even the very civilizations that built them [19].…”
Section: Introductionmentioning
confidence: 99%
“…These models were generalized to handle initial attractiveness [12] and latecomer nodes with a higher degree of fitness [11,13]. It is important to note that these models generate powerlaw (degree) distributions that are similar to the distribution of socioeconomic variables of settlements indicating that preferential attachment is a process that can be used to 2 Complexity describe city grow [14][15][16][17][18]. In the case of geographically distributed networks, the likelihood of link formation is dependent on distance due to the cost of establishing connections and spatial constraints [19].…”
Section: Introductionmentioning
confidence: 99%