Day: July 7, 2014

OGC Seeks Comment on Charter for New Urban Planning Domain Working Group

/ Matt Artz / 1 Comment

OGC_Logo_Border_Blue_3DAn OGC Urban Planning Domain Working Group (SWG) is being chartered to define the role for OGC standards and related activities within the Urban Planning Discipline and to provide an open forum for the discussion and presentation of interoperability requirements, use cases, pilots, and implementations of OGC standards in this domain. Initiators of the new DWG seek comments from the public on the draft new charter. The comment period closes on  6 August 2014.

A “Smart City” invests in human and social capital, physical infrastructure and information communications technology (ICT) infrastructure to sustain quality of life in the urban environment. Technologies and trends such as Augmented Reality (AR), Smart Cities, Smart Grids, Sensor Webs, the Internet of Things (IoT), LBS (Location Based Services), Facilities Management, navigation (indoor and outdoor) and “Big Data” Analytics all can play important roles in informing urban planners. Also, these technologies are permanent and rapidly evolving elements of life in modern cities, which makes them subjects for urban studies and urban planning.

In all of those technology domains, open standards can facilitate the development, publication, discovery, assessment, analysis, portrayal and use of information.The OGC Urban Planning Domain Working Group intends to discover requirements for open geospatial standards in information systems involved in the planning, design, use, maintenance and governance of publicly accessible spaces. Requirements presented and discussed in OGC Domain Working Groups are typically addressed in existing or yet-to-be chartered OGC Standards Working Groups and in the OGC’s collaborative activities with other standards development organizations.

The OGC® is an international consortium of more than 475 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. Visit the OGC website at http://www.opengeospatial.org/.

[Source: OGC press release]

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

/ Matt Artz / 1 Comment

rs-remotesensing-logoRemote Sensing, Volume 6, Issue 7, Published Online 07 July 2014

Akpona Okujeni, Sebastian van der Linden, Benjamin Jakimow, Andreas Rabe, Jochem Verrelst, and Patrick Hostert

“Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN) or limited (RFR and PLSR) performances when comprehensively mapping urban land cover.

Generation of binary and ternary synthetic mixtures.

Generation of binary and ternary synthetic mixtures.

“Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.”

Socio-environmental Drivers and Suicide in Australia: Bayesian Spatial Analysis

/ Matt Artz / 1 Comment

BMC Public HealthBMC Public Health 2014, 14:681, Published 4 July 2014

By Xin Qi, Wenbiao Hu, Kerrie Mengersen, and Shilu Tong

“Background
The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods.

Methods
Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE)) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology (BoM). A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs.

Suicide rates across LGAs and unincorporated SLAs, 1996-2000.

Suicide rates across LGAs and unincorporated SLAs, 1996-2000.

Results
In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1[degree sign]C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996-2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001-2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III.

Conclusions
Proportion of Aboriginal and Torres Strait Islanders, unemployment and temperature appeared to be statistically associated with of suicide incidence across LGAs among all selected variables, especially in recent years. The results indicated that socio-demographic factors played more important roles than meteorological factors in the spatial pattern of suicide incidence.”

The Gravity of Pollination: Integrating At-site Features into Spatial Analysis of Contemporary Pollen Movement

/ Matt Artz / 1 Comment

meMolecular Ecology, Accepted July 2014

By Michelle F. DiLeo, Jenna C. Siu, Matthew K. Rhodes, Adriana López-Villalobos, Angela Redwine, Kelly Ksiazek, and Rodney J. Dyer

“Pollen-mediated gene flow is a major driver of spatial genetic structure in plant populations. Both individual plant characteristics and site-specific features of the landscape can modify the perceived attractiveness of plants to their pollinators and thus play an important role in shaping spatial genetic variation. Most studies of landscape-level genetic connectivity in plants have focused on the effects of inter-individual distance using spatial and increasingly ecological separation; yet have not incorporated individual plant characteristics or other at-site ecological variables. Using spatially explicit simulations, we first tested the extent to which the inclusion of at-site variables influencing local pollination success improved the statistical characterization of genetic connectivity based upon examination of pollen pool genetic structure. The addition of at-site characteristics provided better models than those that only considered inter-individual spatial distance (e.g., IBD). Models parameterized using conditional genetic covariance (e.g., Population Graphs) also outperformed those assuming panmixia. In a natural population of Cornus florida L. (Cornaceae), we showed that the addition of at-site characteristics (clumping of primary canopy opening above each maternal tree and maternal tree floral output) provided significantly better models describing gene flow than models including only between-site spatial (IBD) and ecological (Isolation By Resistance) variables. Overall, our results show that including inter-individual and local ecological variation greatly aids in characterizing landscape-level measures of contemporary gene flow.”