Dasymetric Estimation of Census Population Density: A Geostatistical Approach

17th International Conference on Geoinformatics, 12-14 August 2009

Zongyi He, Liwei Liu, Aihua Hu, Lu Xu

“The population density in choropleth maps is the average density calculated based on the polygon statistical units. It does not show the difference inside the polygon area and cannot reflect the actual population distribution. In order to obtain a real population distribution, it is necessary to spatially refine the statistical areal population data. Using population distribution model or surface interpolation can realize the spatialization of urban population. But these two methods are isolated in research, not closely related to each other. This paper studied the dasymetric mapping of urban population based on geostatistics with the combined use of these two methods. First, the variation function of geostatistics was introduced into the analysis of population spatial distribution model. The population distribution in a space presented a definite structure feature. The variation function curves were fitted through the population density at the sampling points with different intervals h in space, so as to quantitatively describe the change of the population spatial distribution. With some cases, the paper showed how the parameters of variation function could be utilized to analyze the spatial mode of population distribution. Then, a method for dasymetric mapping of urban population was put forward based on indicator Kriging’s interpolation. The theoretical model of the variation function could reflect the degree of spatial relativity of urban population distribution, and the Indicator Kriging can be used to carry out interpolation with sample weight coefficients derived by the theoretical model of the variation function. This was an overall modeling with partial interpolation approach, which could effectively control the influence of particular values, so as to improve the accuracy of urban population estimation. Population statistic data used in the case was acquired from the fifth census in Zhengzhou, China. Considering the large volume of the data, statistic uni- t in the study is confined to the street office level. The study area is the metropolitan area in City of Zhengzhou. Spatial database was built using ArcGIS.The case studied here indicated that the Indicator Kriging performs well in the interpolation of population data.”

Critical Zone Observatories and Sensor Repositories

UK e-Science AHM 2009: Past, Present and Future, 07–09 December 2009, Oxford, UK

Stephen Wilson and Jeremy Frey

“The Shale Hills critical zone laboratory in Pennsylvania was one of three critical zones funded by the U.S National Science Foundation. The goal of these areas was to study the complex processes occurring on the Earth’s surface, including research into hydrology, geomorphology and biogeochemical systems. To allow sharing of this data between researchers a standardised approach of storing the data is required. The volume of data and need for automation means that the data must also be accessible through machine to machine interactions as well as a human to machine interface. In this work an implementation of the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS) has been developed to capture, store and view the Shale Hill critical zone observatory sensor data.”

First Comprehensive Study on GIS and Facility Management Published and Available for Download

Manhattan Software, global leaders in computer-aided facility management (CAFM) and integrated workplace management systems (IWMS) software, along with Esri and the IFMA Foundation, have released a white paper entitled “Geographic Information Systems (GIS) for Facility Management.” This report, written by Stuart Rich and Kevin Davis of PenBay Media, presents the value of GIS as a complimentary technology to other real estate and facility management tools, such as CAD, CAFM and IWMS which can help in managing assets both inside and outside the building. The publication supports the unique collaboration between Manhattan Software and PenBay by leveraging their expertise in using Esri GIS tools for real estate and facility management.

Available free of charge, the report provides an in-depth analysis of this critical issue including:

* A description of the retrofitting process for existing buildings;
* The perspective of sustainability from all players involved in commercial real estate;
* An economic model that supports analysis of capital improvements;
* How to make a business case for investment in energy efficiency improvements; and
* A case study of the renovation of the Empire State Building, the latest poster child for efficiency improvements in existing buildings.

“Manhattan’s work in integrating our CAFM and IWMS tools with Esri’s GIS suite of products not only enabled us to extend our application suite to the outside environment, but allowed us to heighten the knowledge of that environment with analytical and visualization capabilities not known before in our field,” said Craig Gillespie, Manhattan Software’s CEO for North America. “When approached by the IFMA Foundation to jointly support PenBay’s publication, we jumped at the opportunity to bring our understanding of the importance of GIS, and particularly the work of PenBay and Esri, to the IFMA world. We applaud the Foundation for understanding the need and filling a gap in the FM arena with the clear benefits of GIS.”

“GIS can be used throughout the life cycle of a facility – from site selection, design and construction to use, maintenance and adaptation, and ultimately through closing, repurposing and reclamation. The challenge is to manage each step of the process in a way that maximizes the benefits of the facility to society while minimizing short- and long-term impacts on the natural environment. As an integrative platform for management and analysis of all spatial things, I believe, as the authors of this white paper have eloquently stated, GIS ‘is the only technology that has the ability to scale across any expanse, from the individual asset within a building to a virtually global context.'” (Jack Dangermond, President of Esri, page 3 in the foreword to the GIS for Facility Management White Paper)

To obtain your complimentary copy of the Geographic Information Systems (GIS) for Facility Management white paper, please visit Manhattan’s website, usa.manhattansoftware.com/2010pr-ifma-gis-wp.

[Source: Manhattan Software press release]

Under-five Mortality: Spatial-temporal Clusters in Ifakara HDSS in South-eastern Tanzania

Global Health Action 2010

Angelina M. Lutambi, Mathew Alexander, Jensen Charles, Chrisostom Mahutanga, and Rose Nathan

“Background: Childhood mortality remains an important subject, particularly in sub-Saharan Africa where levels are still unacceptably high. To achieve the set Millennium Development Goal 4, calls for comprehensive application of the proven cost-effective interventions. Understanding spatial clustering of childhood mortality can provide a guide in targeting the interventions in a more strategic approach to the population where mortality is highest and the interventions are most likely to make an impact. Methods: Annual child mortality rates were calculated for each village, using person-years observed as the denominator. Kulldorff ’s spatial scan statistic was used for the identification and testing of childhood mortality clusters. All under-five deaths that occurred within a 10-year period from 1997 to 2006 were included in the analysis. Villages were used as units of clusters; all 25 health and demographic surveillance sites (HDSS) villages in the Ifakara health and demographic surveillance area were included. Results: Of the 10 years of analysis, statistically significant spatial clustering was identified in only 2 years (1998 and 2001). In 1998, the statistically significant cluster (p<0.01) was composed of nine villages. A total of 106 childhood deaths were observed against an expected 77.3. The other statistically significant cluster (p<0.05) identified in 2001 was composed of only one village. In this cluster, 36 childhood deaths were observed compared to 20.3 expected. Purely temporal analysis indicated that the year 2003 was a significant cluster (p<0.05). Total deaths were 393 and expected were 335.8. Spatial-temporal analysis showed that nine villages were identified as statistically significant clusters (p<0.05) for the period covering January 1997- December 1998. Total observed deaths in this cluster were 205 while 150.7 were expected. Conclusion: There is evidence of spatial clustering in childhood mortality within the Ifakara HDSS. Further investigations are needed to explore the source of clustering and identify strategies of reaching the cluster population with the existing effective interventions. However, that should happen alongside delivery of interventions to the broader population.”

Severe Weather Event Detection and Tracking Sensor Web

17th International Conference on Geoinformatics, 12-14 August 2009

Genong Yu, Liping Di, Peichuan Li, Peisheng Zhao, Moses, J.F.

“Detecting and tracking of severe weather events requires the fast and no-delay processing of data from observing sensors to end users. This study adopted the sensor Web technology and framework in expediting the analytical process from sensor observation to climatic information. A set of algorithms for detecting and tracking severe weather events was adopted and supported with standard geospatial Web processing services. The study exemplified the procedure for designing a geospatial workflow, deploying a workflow, and executing a workflow. The workflow was described in business execution language (BPEL). The design and construction of a geospatial workflow involve a domain scientist, a developer, a workflow visual design tool, and a workflow engine. The standard interfaces are built on open scripting language, Web services, Web processing service (WPS), Web coverage service (WCS), Web feature service (WFS), and Web map service (WMS). The workflow construction and design built a virtual product/service that synergies raw data, algorithms, Web services to reach the post conditions: persistent data served in standard geospatial Web services, algorithms wrapped as standard WPS processes, composite service (or a workflow) executable automatically in standard workflow engines, and geospatial features described in geography markup language (GML) that can be shared across and supported widely by software platform.”