Spatio-Temporal Constraints on Social Networks Workshop, University of California, Santa Barbara, Center for Spatial Studies, 13-14 December 2010
Maureen O’Mara and Sally Schulz
“What is common to all networks and what is unique to particular network types? Are we looking at the sorts of networks that matter? Can we answer questions about membership dynamics in significant types of networks?
“Humans live in relation to each other. Sometimes they live in strong relationship, sometimes in weak relationship. Families, friendships, business partnerships, transnational organizations, political parties, clans and tribes, swim teams, charities, drug cartels, and organized crime are all forms of human relatedness, ensuring that the members of these networks are more strongly tied to each other in some time and place than to other networks at the same time and place.”
Australian Journal of Crop Science, 4(8):603-608 (2010)
Fereydoon Sarmadian, Ali Keshavarzi, and Arash Malekian
“Prediction and mapping of soil calcium carbonate are necessary for sustainable management of soil fertility. So, this research was done with the aims of (1) evaluation and analyzing spatial variability of topsoil calcium carbonate as an aspect of soil fertility and plant nutrition, (2) comparing geostatistical methods such as kriging and co-kriging and (3) continuous mapping of topsoil calcium carbonate. Sampling was done with stratified random method and 23 soil samples from 0 to 15 cm depth were collected. In co-kriging method, salinity data was also used as auxiliary variable. For comparing and evaluation of geostatistical methods, cross validation and statistical parameters such as correlation coefficient and RMSE were considered. The results showed that co-kriging method has the higher correlation coefficient (0.76) and less RMSE (4.1) which means its higher accuracy than kriging method to predict calcium carbonate content.”
Gordon Sumerling, ESRI Australia Pty. Ltd.
“Foresters use light detection and ranging (lidar) data to understand the forest canopy and terrain, which helps them with forest management and operational activities. Combining lidar data with Esri ArcGIS helps analysts assess forest health, calculate forest biomass, classify terrain, identify drainage patterns, and plan forest management activities such as fertilization, harvesting programs, development activities, and more.
“This paper will step through processes to convert lidar data into a format ArcGIS can process, explain methods to interpret the lidar data, and show how ArcGIS can disseminate the data to those who are not geospatial analysts. It will present methods for reading raw classified lidar data and demonstrate methods for
- Analyzing and validating lidar data with ArcGIS before any extensive processing occurs
- Storing and managing millions or billions of lidar points within the geodatabase in a seamless dataset, regardless of the number of original lidar files
- Processing to extract digital elevation models (DEMs) and digital surface models (DSMs) from the lidar data and store them as terrains in a geodatabase or as raster elevation files
- Extracting vegetation density estimates and tree height estimates from lidar, which aid in growth analysis, fertilization regimes, and logging operations
- Serving and analyzing large amounts of lidar data as a seamless dataset to geographic information system (GIS) clients
“In all areas, ArcGIS is a complete system for managing, storing, and analyzing lidar data. Coupling ArcGIS Desktop with ArcGIS Server, the forestry professional is able to access large amounts of lidar data quickly and efficiently without the need to produce additional resultant datasets.”
Journal of Land Use Science, final version received 24 March 2010
Li Ana, Dan Brown, Joan I. Nassauer, and Bobbi Low
“Residential land-use expansion, an important component of urban sprawl, has a variety of drivers and environmental implications. The goal of this article is to address the timing, location, and mechanisms of different types of residential development. Using land-parcel data and aerial imagery taken between 1950 and 2000 for eight townships 10 in southeastern Michigan, we sampled and classified polygons (854 in total) of four residential types. Socioeconomic characteristics were collected from US census data at the township level and assigned to sample polygons based on the township in which they fell. We then applied survival analysis to achieve the above goal. We found that (1) the development rates varied between residential types over time and (2) the evolution of 15 these types can be explained by different factors. Differentiating such residential types and their associated time-variant patterns usefully sheds light on environmental effects of residential land-use expansions in exurban areas.”