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Adding a Spatial Dimension to Research With Historical Census Data John R. Logan Brown University With the support of funding from NIH and NSF. Adding a spatial dimension: Technical challenges of mapping a 19 th century city
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Adding a Spatial Dimension to Research With Historical Census Data John R. Logan Brown University With the support of funding from NIH and NSF.
Adding a spatial dimension: Technical challenges of mapping a 19th century city • Identifying enumeration districts when the descriptions have been lost • Geocoding address data from the 1880 census manuscripts (5 million records)
District 76 Baltimore City: 8th Ward: South of Monument Street, then North-West of Hillen Street, East of Jones Falls to Center Street, South-West of Centre to Front Street, East and South-East of Front Street, Then North-East of Forest Street District 77 Baltimore City: 8th Ward: South of Monument Street, Then South-West of Forest Street, North and North-West of Front Street, North-East of Centre Street to Jones Falls, East of Jones Falls District 78 Baltimore City: 8th Ward: South of Madison Street, to Harford Avenue, East of Harford Avenue, Then South of Chew Street, West of Ensor Street, Then North of Monument Street, and East of Jones Falls District 79 Baltimore City: 8th Ward: South of Eager Street, West of Greenmount Avenue, North of Madison Street, East of Jones Falls District 80 Baltimore City: 8th Ward: South of Eager Street, West of Ensor Street, & North of Chew Street, West of Harford Avenue, Then North of Madison Street, East of Greenmount Avenue District 81 Baltimore City: 8th Ward: South of Chase Street, West and North-West of Harford Avenue, North of Eager Street, East of the Falls District 82 Baltimore City: 8th Ward: South of John Street, West of Harford Avenue, North of Chase Street, East of Greenmount Avenue District 83 Baltimore City: 8th Ward: South of North Avenue, West of Harford Avenue & North of John St., West of Greenmount Avenue, Then North of Chase Street, East of Jones Falls
Inferring the boundaries
Adapting the geocoding address file
EDs filled in by geocoded addreses
Estimating a discrete choice model • Taking every adult male on every street segment in Newark – • What are the significant associations between the person’s characteristics and characteristics of the street segment’s population (e.g., the neighbors) that account for why the person lives there vs. somewhere else? • How are these associations similar or different for Germans, Irish, or British 1st and 2nd generation persons?
American Communities Project website – see also Social Explorer
We are familiar with research questions that require microdata samples. The minimal steps toward spatial analysis also require data for census tracts. Creating linked files of microdata and tract data provides the possibility for multilevel analysis: who lives where and with what consequences? The cutting edge questions and methodologies of spatial analysis require flexible creation of spatial data and geographies – built from high density microdata that can be geocoded. What is a neighborhood and at what scale to people form communities? Why do people live where they do? What are the processes of residential mobility, assimilation or separation? How are these related to occupation, education, nativity, marital choice, family formation?
This can be achieved. For 1940 we already have some tract-level data as well as the tract boundaries. • It will be natural to create a data file for multi-level analysis: data on individuals, households, and census tracts. A high-density microdata sample is required to create new and flexibly defined tract variables. • The street grid in 1940 is much more similar to the present time than was true for 1880. • If addresses are carefully transcribed, geocoding will enable spatial analysis at the level of points, allowing us to define neighborhoods based on population patterns.
For materials presented here, see: www.s4.brown.edu/utp www.s4.brown.edu/hstrcensus/query40/1940index.htm (in development)