“A Framework for GeoDesign” – Carl Steinitz’s Preconference Seminar at the 2012 Esri UC

Dr. Carl Steinitz

Dr. Carl Steinitz

Dr. Carl Steinitz will be offering a preconference seminar on geodesign at the 2012 Esri User Conference:

“Dr. Steinitz provides an in-depth overview of GeoDesign processes, looking at both rural and urban environments. He describes a framework for doing GeoDesign (design in geographic space) using six model types for assessing the geographic context, for proposing changes and for evaluating the consequences of those changes. He then shows how this framework and these models can be used to understand, plan and manage a variety of landuse/management projects. He presents nine different strategies for proposing change and shows how they can be applied to different problem types at different scales, discussing the pros and cons of each in different situations. The seminar provides the participants with the equivalent of a graduate level seminar on GeoDesign.”

This seminar will be held from 1:30 p.m. to 5:00 p.m. on Sunday, 21 July 2012 in San Diego, California

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

Remote Sensing, 2012, 4(4), 830-848

Paul Treitz, Kevin Lim, Murray Woods, Doug Pitt, Dave Nesbitt and Dave Etheridge

“Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada.

Sample model predicted surface generated from LiDAR height and density metrics (i.e., Gross Merchantable Volume (GMV)) for the Romeo Malette Forest (RMF).

Sample model predicted surface generated from LiDAR height and density metrics (i.e., Gross Merchantable Volume (GMV)) for the Romeo Malette Forest (RMF).

“Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m−2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m−2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types to estimate the following forest inventory variables: (1) average height (R2(adj) = 0.75–0.95); (2) top height (R2(adj) = 0.74–0.98); (3) quadratic mean diameter (R2(adj) = 0.55–0.85); (4) basal area (R2(adj) = 0.22–0.93); (5) gross total volume (R2(adj) = 0.42–0.94); (6) gross merchantable volume (R2(adj) = 0.35–0.93); (7) total aboveground biomass (R2(adj) = 0.23–0.93); and (8) stem density (R2(adj) = 0.17–0.86). Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m−2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.”

A Spatial and Temporal Analysis of Notifiable Gastrointestinal Illness in the Northwest Terrorities, Canada, 1991-2008

International Journal of Health GeographicsInternational Journal of Health Geographics, Published 29 May 2012

Aliya Pardhan-Ali, Olaf Berke, Jeff Wilson, Victoria L Edge, Chris Furgal, Richard Reid-Smith, Maria Santos, and Scott A McEwen

“Background: This is the first study to describe the geographical and temporal distribution of notifiable gastrointestinal illness (NGI) in the Northwest Territories (NWT), Canada. Understanding the distribution of NGI in space and time is important for identifying communities at high risk. Using data derived from the Northwest Territories Communicable Disease Registry (NWT CDR), a number of spatial and temporal techniques were used to explore and analyze NGI incidence from the years 1991 to 2008. Relative risk mapping was used to investigate the variation of disease risk. Scan test statistics were applied to conduct cluster identification in space, time and space-time. Seasonal decomposition of the time series was used to assess seasonal variation and trends in the data.

Spatial relative risk function for NGI.

Spatial relative risk function for NGI.

“Results: There was geographic variability in the rates of NGI with higher notifications in the south compared to the north. Incidence of NGI exhibited seasonality with peaks in the fall months for most years. Two possible outbreaks were detected in the fall of 1995 and 2001, one of which coincided with a previously recognized outbreak. Overall, incidence of NGI fluctuated from 1991 to 2001 followed by a tendency for rates to decrease from 2002 to 2008.

“Conclusions: The distribution of NGI notifications varied widely according to geographic region, season and year. While the analyses highlighted a possible bias in the surveillance data, this information is beneficial for generating hypotheses about risk factors for infection.”

Esri Supports USAID Crowdsourcing Event

Esri logoEsri will closely support the US Agency for International Development (USAID) at the agency’s first-ever crowdsourcing initiative to make international development data accessible and transparent. The initiative will kick off at the USAID Innovation Lab in Washington, DC, at noon on Friday, June 1, 2012, and continue virtually until Sunday, June 3. Esri will participate in the event and provide a platform via ArcGIS Online that USAID can use to openly map the data after the event.

During the event, interested individuals, including volunteers from the online technical communities Standby Task Force and GIS Corps, will structure data on certain USAID economic growth activities and then geocode the data. After the event, USAID will release the complete geocoded dataset in line with the agency’s commitment to make development assistance information more available. As part of this commitment, USAID will map this data on ArcGIS Online so that anyone can explore and analyze the data.

“The US government is committed to opening data and increasing aid transparency; this pilot is an example of this commitment,” said Eric Postel, assistant administrator for Economic Growth, Education and Environment at USAID. “By enabling the crowd to help us sort through and clean nonconfidential data, we are able to release information that we never previously thought was possible.”

Providing public access to this information increases the possible use and value that the data will provide to USAID’s many stakeholders. With an appropriate basemap and the addition of other content found on ArcGIS Online, such as world demographic information, organizations and citizens can create, save, and share maps and web applications. These will enable further discussion, analysis, and action about important development strategies.

“I am excited to continue partnering with my colleagues at USAID to improve communication and collaboration for development activities,” said Jack Dangermond, president of Esri. “Using ArcGIS Online, anyone can leverage this data to better understand the important work that is being done around the world to address social, economic, business, and environmental concerns.”

Join Esri in supporting USAID in this crowdsourcing event by signing up at http://tinyurl.com/USAIDCrowdSource. For more information on ArcGIS Online, visit arcgis.com.

[Source: Esri press release]

Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

Sensors 2012, 12(5), 6307-6330, published online 11 May 2012

Álvaro Sigüenza, David Díaz-Pardo, Jesús Bernat, Vasile Vancea, José Luis Blanco, David Conejero, and Luis Hernández Gómez

“Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively.

Experimental setup for publishing driver-generated observations in the Semantic Sensor Web.

Experimental setup for publishing driver-generated observations in the Semantic Sensor Web.

“Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is sound.”

Jack Dangermond Selected as 2012 UCGIS Fellow

Jack Dangermond

Jack Dangermond

The University Consortium for Geographic Information Science (UCGIS) has selected Esri founder and president Jack Dangermond as one of its 2012 fellows. Dangermond, who is being recognized for his contributions to the advancement of geographic information systems (GIS), UCGIS, and conservation, joins Dr. Luc Anselin, Dr. David Cowen, Dr. Max Egenhofer, Dr. Gerard Rushton, and Dr. Waldo Tobler in the 2012 UCGIS Fellow class. The group will be formally presented at the upcoming UCGIS 2012 Symposium May 30–June 1, 2012, in Washington, DC.

The grade of fellow is bestowed on those who have had an extraordinary record of accomplishments in any of the spatial disciplines and communities of practice that use spatial information to complement and support their business operations or personal activities.

A landscape architect by training, Dangermond founded Esri in 1969 with a vision that a mapping and analysis framework provides a deeper understanding of our world, enabling people to design a better future. Dangermond’s leadership and vision have accelerated the ongoing innovation of GIS technologies that enable people to make insightful decisions and improve the quality of life everywhere. Esri supports a wide variety of global communities using GIS to increase spatial literacy, protect the environment, and assist with disaster response.

[Source: Esri press release]

A Distributed System for Supporting Spatio-temporal Analysis on Large-scale Camera Networks

SCS Technical Report (GT-CS-12-04), 2012

Kirak Hong, Marco Voelz, Venu Govindaraju, Bharat Jayaraman, and Umakishore Ramachandran

“Cameras are becoming ubiquitous. Technological advances and the low cost of such sensors enable deployment of large-scale camera networks in metropolises such as London and New York. Applications such as video-base surveillance and emergency response that exploit such camera networks are continuous, data intensive, and dynamic in terms of resource requirements. Common anomalies in such application spaces include authorized personnel moving into unauthorized spaces and checking the movement of suspicious individuals as they move through the spaces. High level goal in such applications include catching such anomalies in real time and reducing collateral damage.

An example of how a spatio-temporal filter may be designed to prune the search space of signatures to be compared using the time and space attributes of the generated signature

An example of how a spatio-temporal filter may be designed to prune the search space of signatures to be compared using the time and space attributes of the generated signature

“A well-known technique for meeting this high level goal is spatio-temporal analysis. This is an inferencing technique employed by domain experts (e.g., vision researchers) to answer queries such as show the track of person A in the last 30 minutes. Performing spatio-temporal analysis in real-time for a large-scale camera network is challenging. It involves continuously capturing images from distributed cameras, analyzing the images to detect and track objects of interest in the field of view of the cameras, generating an event by comparing the signature of a detected object against a database of known signatures, and maintaining a state transition table indexed by time that shows the spatio-temporal evolution of people movement through the distributed spaces. In this paper, we propose a distributed system architecture to address these challenges. We make the following contributions: (a) present the design choices for real-time spatio-temporal analysis with a view to supporting scalability (in terms of number of cameras, event rate, and known targets), (b) develop heuristics for pruning the event generation phase of spatio-temporal analysis, and (c) implement and evaluate the different design choices in a distributed system to show the scalability of our distributed system architecture.”