Spatial Analysis of Social Media Content (Tweets) during the 2012 U.S. Republican Presidential Primaries

GIScience 2012, Columbus, Ohio, 18-21 September 2012

Ming-Hsiang Tsou and Jiue-An Yang

“Social media (such as Twitter and Facebook) are powerful communication platforms for idea exchange, breaking news (Sankaranarayanan et al. 2009), personal networking (Lerman and Ghosh 2010), political opinions (An et al. 2011), and collective actions (Earl 2010). By using smart phones, personal computers, and mobile devices, people can communicate and coordinate their activities geospatially, and to a significant degree, to accomplish these social communication functions in near-real time.

The tweet spatial search output file by using the keyword, "Mitt Romney", within the radius of 84 miles from the city center of Miami on February 02, 2012.

The tweet spatial search output file by using the keyword, “Mitt Romney”, within
the radius of 84 miles from the city center of Miami on February 02, 2012.

“The impacts of these tools were so vividly demonstrated in the most recent anti-government protests, including the Arab Spring, Occupy Wall Street, and the London Riots. The rich information available in social media can now be monitored, traced, and analyzed in ways that may assist researchers understanding of various diffusion processes, human behaviors, and the collective moods around the world (Golder and Macy 2011).”

A GIS-supported Impact Assessment of the Hierarchical Flood-defense Systems on the Plain Areas of the Taihu Basin, China

International Journal of Geographical Information ScienceInternational Journal of Geographical Information Science, Volume 26, Issue 4, 2012

Chaoqing Yu, Xiaotao Cheng, Jim Hall, Edward P. Evans, Yanyan Wang, Changwei Hu, Haoyun Wu, Jon Wicks, Mathew Scott, Haitao Sun, Jing Wang, Minglei Ren & Zongxue Xu

“The Taihu Basin is located in the east coast of China, with a total area of 36,895 km2. Low-lying floodplain areas occupy about 83% of the basin. The threat of frequent floods to this economically important area has stimulated construction of enormous flood-defense projects along the complex system of rivers and lakes. Digital modeling of flooding processes and quantitative assessment of flood damages in this basin remain challenging due to the complexity. This article reports on an approach to simulate the flooding processes, which integrates hydrological and hydraulic modeling with dike-reliability analysis and socioeconomic information within a GIS platform. A new algorithm is introduced to calculate the influence of the flood-defense systems on spatial distributions of floodwater and consequential damages. Scenario analysis indicates that the modeling is particularly sensitive to the assumed rainfall, dike reliability, and the pump capacities within local polders. The model is validated by comparison with observations from historical flood records. The analysis reveals that the defense systems have significantly reduced the basin-wide flood risk and changed the spatial distributions of floodwater. Such a GIS-based approach can be potentially used to assess the benefit from construction of flood defenses and to avoid unintended spatial redistribution of flooding.”

Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data

Remote Sensing, 2012, 4(4), 810-829

Sandra Eckert

“Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression.

Overview and zoom map of the study area.

Overview and zoom map of the study area.

“Pearson’s correlation coefficients revealed that (a) texture measures correlated more with biomass and carbon than spectral parameters, and (b) correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%). For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%). These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a) developing and applying forest stratum–specific models, and (b) including textural information in addition to spectral information.”

High Resolution Climate Spatial Analysis of European Winegrowing Regions

IXe International Terroirs Congress 2012

Benjamin BOIS, Aurélie BLAIS, Marco MORIONDO, and Gregory V. JONES

“Climate strongly affects the geographical distribution of grape varieties, grapevine cultivation techniques and wine organoleptic properties. The current study aims at comparing the climatic features of European winegrowing regions. A geodatabase of 260 wine producing areas within 18 countries of the European Community was first established by means of maps collected from various sources (e.g. atlases and national wine and vine services). Within the 247 of the 260 initially delimited regions, areas actually planted with vine were identified by means of the Corine Land Cover database, for a total of 6 million of hectares. Each of the 1 km resolution pixels of the WorldClim 1950-2000 monthly climatic database located within these planted areas were used to calculate agroclimatic indices. The Huglin index, the Cool night index and the Dryness index, as described by the Multicriteria Climatic Classification system, as well as a winter freeze risk index, a spring frost risk index and a heat stress index were calculated.

CLARA cluster group representations of the WorldCLim pixels located within winegrowing regions

CLARA cluster group representations of the WorldCLim pixels located within winegrowing regions (Axes correspond to the two first principal components of a PCA performed on the agroclimatic indices).

“The use of a clustering algorithm (CLARA) with each of these 1 km resolution gridded indices resulted in the identification of six climate types: (1) sub-humid temperate, (2) sub-humid cool with very cool nights and high spring frost risk, (3) moderately dry and temperate with cool nights, (4) dry and temperate warm with temperate nights, (5) sub-humid temperate with strong frost risks, and (6) very dry and hot, with cool nights climates. Each of the 247 winegrowing regions was classified according to the type of climate that covers the largest part of its territory. Despite the clustering, the type 4 climate still exhibits a large diversity of climatic characteristics. It is located mainly within winegrowing regions located close to the Mediterranean Sea. To our knowledge the current work is the largest spatial climate analysis of winegrowing regions that have been performed so far.”

Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments

Remote Sensing, 2012, 4(3), 598-621

Jose Raul Romo Leon, Willem J.D. van Leeuwen, and Grant M. Casady

“Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response.

Monsoon trend for each pixel within the fire and the reference areas were computed.

Monsoon trend for each pixel within the fire and the reference areas were computed. For Frijoles Canyon (A) the trend is calculated from 2008 to 2010, for Mid Elevation Mesas (B) from 2000 to 2010, and for Capulin (C) from 2006 to 2010.

“We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response.”

Detecting Irregular Clusters in Big Spatial Data

GIScience 2012, Columbus, Ohio, 18-21 September 2012

Jared Aldstadt, Michael Widener, and Neal Crago

“In the age of “Big Data” GIScientists are faced with the challenging task of processing and interpreting increasingly large spatial datasets. To fully exploit these large and high-resolution data, researchers must adapt existing algorithms and utilize new computational technologies, such as high performance computers (HPCs) (Armstrong 2000). This research concentrates on improving the performance of local irregular cluster detection algorithms, focusing on one flexible, but computationally expensive, algorithm: AMOEBA (Aldstadt and Getis 2006). The ability to locally detect clusters within a geographic region is a powerful tool that can be applied in many settings. For example, in a health context, it is important to explicitly locate the clustering of disease incidents so an appropriate medical intervention can occur. AMOEBA is one of a growing number of cluster detection techniques that do not restrict the search to regular geometric shapes (Duczmal and Assuncao 2004, Tango and Takahashi 2005, Assuncao et al. 2006, Yiannakoulias et al. 2007). These methods have the potential to more accurately delineate clusters, thereby improving the value of clustering as an exploratory data analysis technique.

“While the ability to detect irregularly shaped clusters is useful, the underlying process of AMOEBA involves organically growing the cluster from every spatial data point, known as seed locations, in a predefined study area. This research builds on previous work (Duque et al. 2011a, Widener et al. 2012) by developing a heuristic and decomposition strategies for parallel computing platforms to improve the runtime of the algorithm. The heuristic is designed to intelligently sample seed locations in sub-regions of a large dataset in order to eliminate redundant cluster discovery. Current AMOEBA algorithms spend valuable computation time rediscovering the same irregular cluster, as all seed locations apart of a particular cluster will grow approximately or exactly the same hotspot (or coldspot). With the heuristic reducing the number of computations necessary in some sub-regions but not in others, new decomposition strategies are necessary to equitably distribute the computational load of growing clusters from those seed locations that are tested. In addition to providing researchers with a faster tool for detecting irregularly shaped clusters in large or detailed datasets, this research also provides general insights into how efficient local spatial cluster detection can be achieved on parallel computing platforms.”