Large-scale spatial network models: (Record no. 14528)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02798nab a2200181 4500 |
| 005 - DATE & TIME | |
| control field | 20230906210415.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230906b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Almquist, Zack W |
| 245 ## - TITLE STATEMENT | |
| Title | Large-scale spatial network models: |
| Sub Title | an application to modeling information diffusion through the homeless population of San Francisco/ |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Sage, |
| Date of publication, distribution, etc | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | Vol. 47, Issue 3, 2020, ( 523–540 p.) |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | To address the effects of increasing homeless populations, planners must understand the size and distribution of their homeless populations, as well as how information and resources are diffused throughout homeless communities. Currently, there is limited publicly available information on the homeless population, e.g. the estimates of the homeless, gathered annually by the US Housing & Urban Development point in time survey. While it is theorized in the literature that the networks of homeless individuals provide access to important information for planners in areas such as health (e.g. needle exchanges) or access (e.g. information diffusion about the location of new shelters), it is almost never measured, and if measured, only at a very small scale. This research addresses the question of how planners can leverage publicly available data on the homeless to better understand their own homeless networks (e.g. relations among the homeless themselves) in a cost-effective and reliable way. To this end, we provide a method for simulating realistic networks of a social relation among the homeless population and perform a diffusion analysis over the resultant homeless-to-homeless networks, and also over a simulated homeless youth Facebook network. We validate the former through novel use of historical data, while the latter is based on recent work that demonstrated that the homeless youth have similar size Facebook networks and usage. We see much stronger spatial hopping and quicker diffusion over the youth network, i.e. we expect information to pass among the youth network much faster than the homeless-to-homeless network. This finding implies that non-government organizations and public health efforts that seek to provide information, goods or services to the homeless should start with the homeless youth, given the potential for faster diffusion when homeless youth are the initial transmitters. Overall, these methods and analysis provide a unique opportunity for visualizing, characterizing and inferring information for large-scale and hard to measure social networks. |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 8876 |
| Host Itemnumber | 17104 |
| Place, publisher, and date of publication | London Pion Ltd. 2010 |
| Title | Environment and planning B: planning and design (Urban Analytics and City Science) |
| International Standard Serial Number | 1472-3417 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1177/2399808318785375 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | E-Journal |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| -- | 57632 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| -- | ddc |
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