1 / 18

Analysing long-term spatio-temporal change using Historical GIS

Analysing long-term spatio-temporal change using Historical GIS. Ian Gregory Associate Director Centre for Data Digitisation and Analysis Queen’s University Belfast. National Historical GISs. Contain: Census, vital registration, and similar data Many created/in progress:

alair
Download Presentation

Analysing long-term spatio-temporal change using Historical GIS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Analysing long-term spatio-temporal change using Historical GIS Ian Gregory Associate Director Centre for Data Digitisation and Analysis Queen’s University Belfast

  2. National Historical GISs • Contain: • Census, vital registration, and similar data • Many created/in progress: • Great Britain (£1,000,000 plus) • US National Historical GIS ($5 million) • China Historical GIS • Quantitative Databank of Belgian Territorial Units • Holland • Russia • South Korea • Ireland (planned at CDDA) • Europe? • Components: • Attribute • Spatial – many boundary changes • Temporal depth (typically two centuries)

  3. What is GIS? • A Geographical Information System? • A Spatial Database Management System • 1. Co-ordinates: Absolute (Euclidean) space • 2. Topology: Relative space

  4. The Geography of Infant Mortality in England & Wales • From the 1850s to the 1900s: • Was there a rural-urban divide? • Was there a north-south divide? • How did they change over time? • How did they inter-relate? • Looking at infant mortality only • Infant mortality: Deaths before 1st birthday per 1,000 live births. • Data published decennially using c. 635 Registration Districts. • Pilot project • 1911-1973: data are available annually for 1,500 Local Govt. Districts • 1974 on: data available annually at ward level

  5. Infant Mortality, 1851-1911 • Conventional explanations: • Britain was already a highly urban country and was becoming more so. • Lower rates were driven by improvements in sanitary conditions in towns and by better health care (encouraging breast feeding, safe milk banks, etc.). • Improvements started simultaneously across the whole country in the 1880s. • 1890s was a blip caused by a series of hot summers increasing infectious diseases in urban areas. After Woods et al (1988 & ’89); Lee (1991); Williams and Galley (1995); Woods (1997)

  6. Maps of Infant Mortality, 1851-1911 1850s 1860s 1870s 1880s 1890s 1900s Legend uses worst three deciles (red), best three (green), and those in between (yellow)

  7. The Urban-Rural divide • 1. Most rural • 8. Most urban

  8. A Core-Periphery Divide? 1911

  9. The Core-Periphery divide, 1900s Infant mortality

  10. The Core-Periphery divide, 1850s-1900s

  11. The Core-Periphery divide, 1850s-1900s 1850s 1860s 1870s 1900s 1880s 1890s Legend uses worst three deciles, best three, and those in between. Buffers are 25km wide.

  12. Change in Infant Mortality 1850s-1900s Overall Soton Cardiff L’pool N’castle London B’ham M’chester Proportional to national rate National rate dropped from 153.25 to 127.59 – an improvement of 16.74% Shading indicates population deciles

  13. Changing Infant Mortality Matrices, 1851-1911 Legend uses deciles (worst 3 red, best 3 green, middle 4 yellow). Moran’s I measures edge to edge joins.

  14. Change in core and peripheral areas Core (buffer 1) % change, 1850s-1900s Periphery (Buffer 8unless stated)

  15. Areal Interpolation approaches • Leighton Buzzard (urbanity 2, peripherality 2) Total lives saved: 1009 As % of expected deaths: 27.75%

  16. Lives saved by IM decline 1860s-1900s Total lives saved % of expected deaths Legend uses deciles of total population (1911)

  17. Conclusions for Infant Mortality • Falling infant mortality • Increasing core-periphery divide • Increasing rural-urban divide • Rural areas in south and midlands seem to be driving the improvement. • Lack of improvement in northern rural and urban areas seems to be increasing the divides • Traditional explanations over-emphasise urban areas

  18. Implications for HGIS • This analysis: • Tells us: what, where and when BUT not why • It is wide but shallow • Case studies: • Single “typical place” studied in depth • Narrow but deep

More Related