What Else Do We Want to Know about Social Networks?

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.”

Continuous Mapping of Topsoil Calcium Carbonate using Geostatistical Techniques in a Semi-arid Region

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.”

New White Paper: Lidar Analysis in ArcGIS 10 for Forestry Applications

January 2011

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.”

Variations in Development of Exurban Residential Landscapes: Timing, Location, and Driving Forces

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.”

The Effects of Climate Change on Wildlife and Terrestrial Ecosystems

Taprobanica, April, 2010. Vol. 02, No. 01: pp. 30-47.

Thilina Surasinghe

“Climate change and biodiversity are interconnected, where climate change is reshaping global biodiversity. Unsustainable human activities that increase accumulation of greenhouse gases and hinder the natural balance of atmospheric greenhouse gases aggravate the effects of climate change on biodiversity. Rising seas-levels could inundate coastal habitats and stem the flow of nutrients from the ocean to the terrestrial ecosystems. Altered climate regimes directly affect wildlife, their behavior, migration, foraging, growth and reproduction. Climate change could disturb the dynamic equilibrium of terrestrial ecosystems by affecting ecosystem productivity, biomass production, hydrological balance, and trophic interactions. Further, climate change intensifies natural disasters and shifts in natural disturbance regimes. Such processes impose physiological and environmental stress on terrestrial ecosystems which adversely affect the ecosystem resistance and resilience. Moreover, warming atmosphere causes thermal optima to shift towards high latitudes and high altitudes. Terrestrial biota readily responds to temperature, where both flora and fauna alter distributions toward more favorable climatic conditions. Some climatic parameters that drive life history events, such as photoperiod, are fixed, while others, such as the timing of spring weather, are changing because of greenhouse gasses. The resulting mismatch between fixed and variable drivers of phenology, such as in mating, breeding, migration, hibernation, and post-hibernation activities, will disadvantage some species and benefit others. This will result in new ecosystems. Warming temperature favors biological activities of wildlife pathogens, since high temperature increases breeding rate, survival, hatching success and transmission of wildlife parasites and disease-causing agents. Climate change dissociates species interactions, mutual associations and a multitude of ecosystem functions. Ultimately, climate change predisposes native terrestrial wildlife to extinction and alters the functions and structure of terrestrial ecosystems. Biodiversity provides ecosystem services including the regulation and mitigation of the adverse impacts of climate change. Therefore, biodiversity conservation and terrestrial ecosystem management is critical to address climate change. Robust climate-oriented models with the use of GIS and remote sensing technology are needed to make effective predictions about the spatial and temporal effects of climate change.”

Harvesting Geospatial Knowledge from Online Social Networks

Spatio-Temporal Constraints on Social Networks Workshop, University of California, Santa Barbara, Center for Spatial Studies, 13-14 December 2010

Kristina Lerman

“Social Web has moved knowledge production from the hands of the experts and professionals to the masses. Today online social networking sites, such as Twitter, Facebook, YouTube, and Flickr, allow ordinary people not only to create massive quantities of new data, but also organize it, use it, and share it with others. Unlike earlier information technologies, the Social Web exposes social activity, allowing each person to observe and be influenced by the actions of others in real time. How will such real-time, many-to-many communication change how we discover, use, and manage information? And how will it transform society and how we solve problems? My research addresses these questions by developing methods to harvest social knowledge.”

Spatial Analysis of Metal Concentrations in the Brown Shrimp from the Southern North Sea

Scientia Marina 73(1), March 2009, pp. 105-115

Kristine Jung, Vanessa Stelzenmüller,  and Gerd-Peter ZauKe

“Spatial distributions of Cu, Pb, Cd, Ni and Zn concentrations in brown shrimps Crangon crangon (linnaeus, 1758) collected on a cruise of FrV Walther Herwig III to the southern north sea in January 2004, were investigated on a scale of 18 x 18 km to evaluate the range of spatial autocorrelations for the different variables under study. semivariogram models obtained by geostatistical procedures indicated a distinct increase in variability for most variables with sampling distance. Only if samples are taken at distances above the estimated values for the practical range of the semivariogram can stochastic independence of the data be assumed. these are 6.6 km for Cd, 3.0 km for ni and 5.2 km for Pb. Contour plots revealed a clear coincidence of high values for Cd, ni and Pb with low shrimp mean body wet weight. nevertheless, spatial autocorrelations were rather weak, since classical and geostatistical population estimates for the means and the 95% confidence intervals were in good agreement. the low detected concentrations of Pb in C. crangon were in good agreement with reported data for decapod crustaceans from other regions. For Zn reported values were distinctly below our 95% confidence intervals, while for Cu they were slightly above and for Cd distinctly above concentrations in C. crangon from this study. For Ni no comparative values exist. We conclude that with this integrated biomonitoring approach metal concentrations could be assessed more precisely and relations between biotic and abiotic variables could be evaluated.”