Seth Spielman
Biographic Data
| ID | 5232974 |
|---|---|
| NAME | Seth Spielman |
| GIVEN NAMES | Seth |
| FAMILY NAME | Spielman |
| SIGNATURE | SPIELMAN S |
| AFFILIATIONS | Seth Spielman is with the Columbia University Graduate School of Architecture, Planning, and Preservation, New York, NY, and is a National Science Foundation IGERT Fellow in Geographic Information Science at the National Center for Geographic Information and Analysis, University of Buffalo, Buffalo, NY. |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 25 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2006 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 3 |
The Impact of Covariance on American Community Survey Margins of Error: Computational Alternatives
Migration in the 1930s: Beyond the Dust Bowl
This paper analyzes in detail the role of environmental and economic shocks in the migration of the 1930s. The 1940 US Census of Population asked every inhabitant where they lived five years earlier, a unique source for understanding migration flows and networks. Earlier research documented migrant origins and destinations, but we will show how short-term and annual weather conditions at sending locations in the 1930s explain those flows, and how…
Dasymetric Modeling and Uncertainty
Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, and/or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology - the Penalized Maxi…
Identifying and Bounding Ethnic Neighborhoods
This study presents three novel approaches to the question of how best to identify ethnic neighborhoods (or more generally, neighborhoods defined any aspect of their population composition) and to define their boundaries. It takes advantage of unusual data on the residential locations of all residents of Newark, NJ, in 1880 to avoid having to accept arbitrary administrative units (like census tracts) as the building blocks of neighborhoods. For t…
Appropriate Use of the K Function in Urban Environments
There is clearly a need to understand neighborhood food environments and how they contribute to behavior. “Clustering of Fast-Food Restaurants Around Schools: A Novel Application of Spatial Statistics to the Study of Food Environments”1 is an interesting approach to understanding this important need. However, the article raises a number of methodological concerns about the use of spatial statistics in urban environments. First and foremost is tha…
Identifying and Bounding Ethnic Neighborhoods
This study presents three novel approaches to the question of how best to identify ethnic neighborhoods (or more generally, neighborhoods defined any aspect of their population composition) and to define their boundaries. It takes advantage of unusual data on the residential locations of all residents of Newark, NJ, in 1880 to avoid having to accept arbitrary administrative units (like census tracts) as the building blocks of neighborhoods. For t…
Migration in the 1930s: Beyond the Dust Bowl
This paper analyzes in detail the role of environmental and economic shocks in the migration of the 1930s. The 1940 US Census of Population asked every inhabitant where they lived five years earlier, a unique source for understanding migration flows and networks. Earlier research documented migrant origins and destinations, but we will show how short-term and annual weather conditions at sending locations in the 1930s explain those flows, and how…
Dasymetric Modeling and Uncertainty
Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, and/or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology - the Penalized Maxi…
The Impact of Covariance on American Community Survey Margins of Error: Computational Alternatives
Appropriate Use of the K Function in Urban Environments
There is clearly a need to understand neighborhood food environments and how they contribute to behavior. “Clustering of Fast-Food Restaurants Around Schools: A Novel Application of Spatial Statistics to the Study of Food Environments”1 is an interesting approach to understanding this important need. However, the article raises a number of methodological concerns about the use of spatial statistics in urban environments. First and foremost is tha…
Identifying and Bounding Ethnic Neighborhoods
This study presents three novel approaches to the question of how best to identify ethnic neighborhoods (or more generally, neighborhoods defined any aspect of their population composition) and to define their boundaries. It takes advantage of unusual data on the residential locations of all residents of Newark, NJ, in 1880 to avoid having to accept arbitrary administrative units (like census tracts) as the building blocks of neighborhoods. For t…
Dasymetric Modeling and Uncertainty
Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, and/or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology - the Penalized Maxi…
Migration in the 1930s: Beyond the Dust Bowl
This paper analyzes in detail the role of environmental and economic shocks in the migration of the 1930s. The 1940 US Census of Population asked every inhabitant where they lived five years earlier, a unique source for understanding migration flows and networks. Earlier research documented migrant origins and destinations, but we will show how short-term and annual weather conditions at sending locations in the 1930s explain those flows, and how…
The Impact of Covariance on American Community Survey Margins of Error: Computational Alternatives
Computer Science (4 works) · Geography (4 works) · Population (4 works) · Census (3 works) · Demography (3 works) · Demography (3 works) · Mathematics (3 works) · Sociology (3 works) · Urban, Neighborhood, and Segregation Studies (3 works) · Data mining (2 works)