ArcGIS for AutoCAD Offers Advanced CAD and GIS Interoperability

Use ArcGIS for AutoCAD to easily add, create, and edit GIS information in your AutoCAD drawings.

Use ArcGIS for AutoCAD to easily add, create, and edit GIS information in your AutoCAD drawings.

New Version Introduces Geodatabase Editing

The latest release of ArcGIS for AutoCAD, Esri’s free AutoCAD plug-in, improves the ability to exchange data and information between the ArcGIS and AutoCAD platforms. ArcGIS for AutoCAD users with read/write access to ArcGIS for Server feature services can now edit geodatabases through AutoCAD. This enables easier data dissemination between CAD and GIS users across the enterprise, reduces the duplication of work, and increases efficiency.

CAD professionals can use the free downloadable application to add, create, and edit GIS data within AutoCAD drawings. For example, users can add maps and map services from enterprise or cloud servers, such as ArcGIS Online, to their drawings, giving the design a geographic context and a common operating picture for the organization.

“ArcGIS for AutoCAD is the interface to the ArcGIS system and all of its rich data content, sharing, and data management,” states Esri CAD product manager Don Kuehne. “The possibilities presented by the combination of AutoCAD and ArcGIS services to automate editing and data maintenance workflows are going to result in an exponential leap in value for those who take advantage of them.”

The new release also includes access to image services and a geolocation service for navigating within an AutoCAD drawing. AutoCAD 2010/2011/2012 (32-bit and 64-bit) systems are supported. To learn more about or to download the new release of ArcGIS for AutoCAD, visit esri.com/autocadapp.

[Source: Esri press release]

Clinton Climate Initiative Receives 2012 Special Achievement in GIS Award

Clinton FoundationLast month at the Esri International User Conference, Clinton Climate Initiative (CCI) received the 2012 Special Achievements in GIS Award. The award recognized CCI for using geographic information system (GIS) technology to help countries monitor their carbon levels.

CCI’s Forestry Program is developing forestry projects and carbon measurement systems that help governments and local communities receive compensation for preserving and re-growing forests. As global warming is caused by increased carbon dioxide in the atmosphere from burning fossil fuels – and deforestation accounts for about 15% of total carbon dioxide emissions in the world – scientists predict that if governments and communities don’t take action to reduce carbon dioxide emissions, our world will face increasingly drastic consequences ranging from stronger heat waves to more droughts and floods to increasing sea level. All of these affect agriculture, food security, viability of coastal cities, and water availability around the world.

In order to reduce emissions, governments and economies must use less fossil fuels and increase use of energy efficient technologies and renewable technologies. CCI’s Forestry Program focuses on helping developing countries reverse deforestation and plant new trees. If countries are able to show that they can monitor and verify that they are reducing their carbon dioxide emissions, countries become eligible for funding to manage their forest programs and other low-carbon economic activities.

CCI was recognized with the 2012 Special Achievement in GIS Award for helping the country of Guyana become eligible for $70 million in forest-based payments from the government of Norway. Guyana is now using this funding to facilitate specific elements of a Low Carbon Development Plan envisioned and put in place by former Guyana President Bharrat Jagdeo. This project is part of CCI’s Forestry Program and has been supported by the Rockefeller Foundation and the governments of Norway and Australia.

CCI uses GIS technology as a centerpiece of forest carbon measurement, reporting, and verification (MRV) systems for developing countries. GIS is one of three legs of the platform–Data, Models, and GIS–that allows countries to determine how much carbon they have, how it is changing, and how the drivers of deforestation and forest degradation can be monitored and adjusted as required.

With GIS systems in place for forestry, developing countries can be eligible for direct payments through international agreements based on the effectiveness of their MRV systems. Once in place, the GIS systems can be used more broadly by the countries for other resource development, land surveys, and determination of land tenure.

Read more about CCI’s forestry projects.

[Source: Clinton Foundation press release]

Obesogenic Neighborhood Environments, Child and Parent Obesity: The Neighborhood Impact on Kids Study

American Journal of Preventive MedicineAmerican Journal of Preventive Medicine, May 2012, Vol. 42, No. 5

“Background: Identifying neighborhood environment attributes related to childhood obesity can inform environmental changes for obesity prevention.

“Purpose: To evaluate child and parent weight status across neighborhoods in King County (Seattle metropolitan area) and San Diego County differing in GIS-defıned physical activity environment (PAE) and nutrition environment (NE) characteristics.

“Methods: Neighborhoods were selected to represent high (favorable) versus low (unfavorable) on the two measures, forming four neighborhood types (low on both measures, low PAE/high NE, high PAE/low NE, and high on both measures). Weight and height of children aged 6–11 yearsandone parent (n730) from selected neighborhoods were assessed in 2007–2009. Differences in child and parent overweight and obesity by neighborhood type were examined, adjusting for neighborhood-, family-, and individual-level demographics.

“Results: Children from neighborhoods high on both environment measures were less likely to be obese (7.7% vs 15.9%,OR0.44, p0.02) and marginally less likely to be overweight (23.7% vs 31.7%,OR0.67, p0.08) than children from neighborhoods low on both measures. In models adjusted for parent weight status and demographic factors, neighborhood environment type remained related to child obesity (high vs low on both measures, OR0.41, p0.03). Parents in neighborhoods high on both measures (versus low on both) were marginally less likely to be obese (20.1% vs 27.7%,OR0.66, p0.08), although parent overweight did not differ by neighborhood environment. The lower odds of parent obesity in neighborhoods with environments supportive of physical activity and healthy eating remained in models adjusted for demographics (high vs low on the environment measures, OR0.57, p0.053).

“Conclusions: Findings support the proposed GIS-based defınitions of obesogenic neighborhoods for children and parents that consider both physical activity and nutrition environment features.”