Computational Modeling of Spatio-temporal Social Networks: A Time-Aggregated Graph Approach

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

Shashi Shekhar and Dev Oliver

“Social computing is transforming on-line spaces with popular applications such as social networking (e.g., Facebook), collaborative authoring (e.g., Wikipedia), social bargain hunting (e.g., Groupon), etc. Spatio-temporal constraints are becoming a critical issue in social computing with the emergence of location-based social-networking, Volunteered
Geographic Information (Goo 07, Elw 08), Participative Planning (Elw 08, Fis 01), etc. Location-based social networks (e.g., foursquare.com and the “Places Check-in” feature on Facebook) facilitate socialization with nearby friends at restaurants, bars, museums, and concerts. Volunteered Geographic Information (e.g., Wikimapia, OpenStreetMap, Google MyMaps) allows Internet users to participate in generation of geographic information. Traditional computational models for social networks are based on graphs [Fre 06, Was 94, Nrc 03, Cro 09], where nodes represent individual actors (e.g., persons, organizations) and edges represent relationship ties (e.g., communication, financial aid, contracts) between actors. Such graph models are used to assess centrality and the influence of actors (e.g., measures such as degree, reach, “between-ness,” bridge), as well as community structure (e.g., measures such as cohesion, clustering, etc.). Statistical properties such as skewed degree distribution are modeled by random graphs [New 02, Nrc 03], where each node-pair has a connecting edge with independent probability p, which may depend on factors such as geographic distance [Won 05].

“However, traditional graph and random graph models are limited in addressing spatio-temporal questions such as change (e.g., how is trust or leadership changing over time? who are the emerging leaders in a group? what are the recurring changes in a group?), trends (e.g., what are the long-term and short-term trends in network size or structure? what are the exceptions to the long-term trend?), duration (e.g., how long is the tenure of a leader in a group? how long does it take to elevate the level of trust such as a relationship changing from visitor to friend?), migration, mobility and travel (e.g., interplay between travel behavior and size/structure of social networks [Tim 06]). This position paper explores time-aggregated graph models to support computational tools to address such questions.”

Gold Resources Potential Assessment in Eastern Kunlun Mountains of China Combining Weights-of-evidence Model with GIS Spatial Analysis Technique

Chinese Geographical Science, Volume 20, Number 5, 461-470

Binbin He, Cuihua Chen, and Yue Liu

“Resources potential assessment is one of the fields in geosciences, which is able to take great advantage of GIS technology as a substitution of traditional working methods. The gold resources potential in the eastern Kunlun Mountains, Qinghai Province, China was assessed by combining weights-of-evidence model with GIS spatial analysis technique. All the data sets used in this paper were derived from an established multi-source geological spatial database, which contains geological, geophysical, geochemical and remote sensing data. Three multi-class variables, i.e., structural intersection, Indosinian k-feldspar granite and regional fault, were used in proximity analysis to examine their spatial association with known gold deposits. A prospectivity map was produced by weights-of-evidence model based on seven binary evidential maps, all of which had passed a conditional independence test. The study area was divided into three target zones of high potential, moderate potential and low potential areas, among which high potential areas and moderate potential areas accounted for 20% of the total area and contained 32 of the 43 gold deposits. The results show that the gold resources potential assessment in the eastern Kunlun Mountains has a higher precision. ”

Spatial Analysis of Main Urban Land-Use Patterns in Guangzhou

2010 International Conference on Multimedia Technology (ICMT), 29-31 Oct. 2010, pp. 1 – 4

Ying Li Chi Haishan

“Based on the information of land-uses which was achieved by the satellite remote sensing TM image in 1990 and 2006, this paper took the main urban area of Guangzhou as a study area to analyze its structure and dynamics of land-use changes, as well as the spatial variation of the land-use by dynamic degree of land-use, land-use composite index, and the compactedness coefficient and relative entropy in line with the land-use structure features. The results showed that land-use composite indexes of the main urban area in Guangzhou were in an increased trend, particularly in construction land-use. A large number of farmland, woodland, gardens and waters turned into construction land-uses, so construction land-use areas made a significant growth; trends of regional construction land-use and urban development were scattered; old area’s development and utilization was the most compact form, followed by Huadu, and Panyu and Nansha area were minimum, where is the least relatively compact; entropies of the three regions were higher, which showed that land use and development were in a more scattering and average situation, among the three, entropies of Panyu and Nansha area growing fastest; it also showed that land development and utilization of this area would have a dispersive trend.”