Information Network’s (CIESIN) Socioeconomic Data and Applications Center
(SEDAC). SEDAC has created a gridded population dataset called Gridded Population
of the World (Deichmann et al., 2000). SEDAC utilized census data at the most basic
administrative units available, and converted them to a grid of 2.5’ by 2.5’ latitudelongitude cells. A comparable method was developed for a global urban-rural dataset -
Landscan, created by Oak Ridge National Laboratory. This method uses gridded
census data with additional algorithms to categorize population in relation to data on
lights at night, land cover classification, elevation, slope, and transportation infrastructure
(Dobson et al., 2000). Once the socioeconomic data have been gridded in either way, they
can more easily be combined with remote sensing and GIS data (CIESIN, 2004).
A second approach to social and physical science data integration is to convert data
in the opposite direction, i.e. translating physical science data within gridded formats
and converting them to tabular data formats more useful and familiar to social scientists
(CIESIN, 2004). The SEDAC Population, Landscape and Climate Estimates (PLACE)
data set is one of the first efforts to do this. It can be found at the following website:
http://sedac.ciesin.columbia.edu/plue/nagd/place.html. The methodology in this case
is to take remotely sensed data, or data originally derived from remote sensing
instruments, including coastlines, elevation, slope, climate zones and biomes, and
aggregate human populations within those categories. These datasets can subsequently
be joined with additional tabular data (economic, environmental, or trade statistics, for
example), and aggregated at different spatial scales to identify patterns via statistical
analyses (CIESIN, 2004). It is critical that such efforts increase aimed at the integration
of social and physical science data in order to identify coastal change and coastal urban
expansion, and to make physical science data more accessible to social scientists for
more inclusive and extensive analyses of coastal, global changes.
5. Conclusions
Remote sensing observations and spatial data of coastal ecosystem dynamics have
historically been separate from in situ monitoring of those same dynamic systems.
Increasingly, however, the two data types have been utilized in tandem to determine the
nature of the landscape, the change over time, and the implementation of management
strategies. The combination of historical data archives with current data sets has
strengthened many studies of ecosystem change and variability. There are many
sources of free and at-cost archival and contemporary data that can support analyses
of change on a global, regional, or local scale. Integrating multiple datasets as well as
in situ and remotely sensed datasets and products allows researchers to analyze an
ecosystem and include all the influences upon and within it. Environmental impacts
affecting every ecosystem due to anthropogenic actions have become more evident
as technology is better able to link ecosystem changes to their causal source.
As ecosystem vulnerability increases worldwide, it is imperative that scientists lend
their understanding of technologies to better identify the links between environment,
weather, global climate change, and human activities. The principle interest of
sustainable development needs to incorporate integrated research including urban
growth, environmental education, marine park development, tourism revenue, and
marine resource management to avoid a global “Tragedy of the Commons” (Ehler and
Basta, 1993; van da Weide, 1993; Christie and White, 1997; Courtney and White,
2000).
194
Gebelein
(SEDAC). SEDAC has created a gridded population dataset called Gridded Population
of the World (Deichmann et al., 2000). SEDAC utilized census data at the most basic
administrative units available, and converted them to a grid of 2.5’ by 2.5’ latitudelongitude cells. A comparable method was developed for a global urban-rural dataset -
Landscan, created by Oak Ridge National Laboratory. This method uses gridded
census data with additional algorithms to categorize population in relation to data on
lights at night, land cover classification, elevation, slope, and transportation infrastructure
(Dobson et al., 2000). Once the socioeconomic data have been gridded in either way, they
can more easily be combined with remote sensing and GIS data (CIESIN, 2004).
A second approach to social and physical science data integration is to convert data
in the opposite direction, i.e. translating physical science data within gridded formats
and converting them to tabular data formats more useful and familiar to social scientists
(CIESIN, 2004). The SEDAC Population, Landscape and Climate Estimates (PLACE)
data set is one of the first efforts to do this. It can be found at the following website:
http://sedac.ciesin.columbia.edu/plue/nagd/place.html. The methodology in this case
is to take remotely sensed data, or data originally derived from remote sensing
instruments, including coastlines, elevation, slope, climate zones and biomes, and
aggregate human populations within those categories. These datasets can subsequently
be joined with additional tabular data (economic, environmental, or trade statistics, for
example), and aggregated at different spatial scales to identify patterns via statistical
analyses (CIESIN, 2004). It is critical that such efforts increase aimed at the integration
of social and physical science data in order to identify coastal change and coastal urban
expansion, and to make physical science data more accessible to social scientists for
more inclusive and extensive analyses of coastal, global changes.
5. Conclusions
Remote sensing observations and spatial data of coastal ecosystem dynamics have
historically been separate from in situ monitoring of those same dynamic systems.
Increasingly, however, the two data types have been utilized in tandem to determine the
nature of the landscape, the change over time, and the implementation of management
strategies. The combination of historical data archives with current data sets has
strengthened many studies of ecosystem change and variability. There are many
sources of free and at-cost archival and contemporary data that can support analyses
of change on a global, regional, or local scale. Integrating multiple datasets as well as
in situ and remotely sensed datasets and products allows researchers to analyze an
ecosystem and include all the influences upon and within it. Environmental impacts
affecting every ecosystem due to anthropogenic actions have become more evident
as technology is better able to link ecosystem changes to their causal source.
As ecosystem vulnerability increases worldwide, it is imperative that scientists lend
their understanding of technologies to better identify the links between environment,
weather, global climate change, and human activities. The principle interest of
sustainable development needs to incorporate integrated research including urban
growth, environmental education, marine park development, tourism revenue, and
marine resource management to avoid a global “Tragedy of the Commons” (Ehler and
Basta, 1993; van da Weide, 1993; Christie and White, 1997; Courtney and White,
2000).
194
Gebelein