Automatic Spatio-temporal Analysis of Construction Site Equipment Operations using GPS Data

Automation in ConstructionAutomation in Construction, Volume 29, January 2013, Pages 107–122

Nipesh Pradhananga and Jochen Teizer


  • Continuous data collection in construction equipment operation is lacking detail.
  • GPS data loggers can collect reliable location tracking data for further analysis.
  • A software user interface allows detection and visualization of active job site areas.
  • Information to equipment cycles, hours of operation, and proximity can be collected.
  • Data can be used in application, e.g., construction equipment simulation and safety.

“A literature review revealed several major shortcomings in the analysis of construction equipment operations data, for example, the lack of using realistic or real-time positioning data that can feed into an equipment operations analysis or simulation model. This paper presents technology and algorithms that have the potential in aiding the automated assessment of construction site equipment operations. Utilizing commercially available low-cost global positioning system (GPS) devices enables the continuous data logging of equipment location in addition to simultaneously recording timestamps. However, before any such spatio-temporal equipment data can be reliably collected on construction sites, the error rate of the GPS devices had to be evaluated. Data analysis methods and rules for monitoring construction site equipment operations and activity were then defined. A detailed software interface was finally created that allows a user to set, analyze, and visualize several important equipment parameters towards achieving the goal of creating more realistic equipment operation analysis and potential for inclusion in simulation models. Results from field experiments show that the developed technology is able to identify and track equipment activity- and safety-related information automatically for job site performance and layout decision making, respectively. The presented work will aid construction project managers in making better decisions to plan, manage, and control equipment-related work tasks on construction sites.”

Uncovering Repeated Spatio-temporal Behavioral Patterns Embedded in GPS-based Taxi Tracking Data

GIScience 2012

Yang Xu, Shih-Lung Shaw, Jiaoli Chen, Qingquan Li, Zhixiang Fang, and Yuguang Li

“Global Positioning System (GPS) based vehicle tracking data have been used to derive useful traffic data such as computing travel speed or congestion level (e.g., Herrera et al., 2010; Mohan, 2008) or measuring urban dynamics (e.g., Calabrese et al., 2011; Reades et al., 2007). Vehicle tracking data also have been used to analyze travel activities (e.g., Li et al., 2011; Liu et al., 2010). This study, on the other hand, focuses on identifying repeated spatio-temporal patterns embedded in a GPS-based taxi tracking dataset collected in Wuhan, China. Although taxi trajectories may appear to be chaotic at first glance, there could be important repeated spatio-temporal patterns embedded in taxi tracking data. For example, certain taxi drivers may have preferences of waiting for passengers at particular locations such as airports, train stations, etc. In many developing countries, it also is common to have two work shifts of drivers for one taxi. Identifying repeated spatio-temporal patterns embedded in vehicle tracking data thus can shed light on important travel and activity behavioral patterns.

Spatial distribution of repeatedly visited road segments by frequency.

Spatial distribution of repeatedly visited road segments by frequency.

“This study uses a taxi tracking dataset collected in Wuhan, China as a case study to identify repeated spatio-temporal behavioral patterns among the taxi drivers. The main objective of this study is to develop a systematic method that can facilitate uncovering repeated behavioral patterns in a large tracking dataset. This method can be adapted for studies using other types of tracking data such as cell phone tracking data of individual trajectories or online tracking data of individual web browsing histories.”

Esri Joins the Consortium for Ocean Leadership

Esri logoChief Scientist Dawn Wright Brings Spatial Thinking to Ocean Policy Leaders

The Consortium for Ocean Leadership (Ocean Leadership) has elected Esri, the world leader in GIS, as a member. Chief scientist Dawn Wright will represent Esri by bringing her extensive knowledge of ocean geospatial technology to the consortium. Esri will be participating in an elite group of over 100 world premier oceanographic research and educational institutions, aquariums, and industry partners.

“The Consortium for Ocean Leadership brings together research, education, and industry entities to advance a shared ocean agenda,” said Robert B. Gagosian, president and CEO of Ocean Leadership. “We are very pleased to have Esri join our membership, as its commitment to our oceans and dedication to the development of the frameworks for scientific collaboration will aid us in shaping the future of ocean science through discovery, understanding, and action.”

Dr. Dawn Wright, Esri's chief scientist.

Dr. Dawn Wright, Esri’s chief scientist.

Ocean Leadership shapes the future of ocean science, technology, and education by managing and coordinating programs that involve marine life inventories, ocean observatories, and natural resources management. Centered in Washington, DC, Ocean Leadership is the program office for the National Ocean Sciences Bowl, National Oceanographic Partnership Program, the Interagency Working Group on Ocean Observations, and the Ocean Research and Resources Advisory Panel.

“Ocean Leadership’s invitation to Esri to join its oceans efforts is extremely important,” said Wright. “Esri was founded on the belief that comprehending the relationships of the earth’s systems is integral to the health of the planet. We share the values of Ocean Leadership and look forward to participating in its efforts to advance ocean science and to shape policy for sustainable ocean management.”

Consortium members represent some of the nation’s most prestigious ocean organizations. The consortium provides community-based scientific advice and recommendations and is widely respected on Capitol Hill as well as in the entire US ocean research and education community. Wright attended the meeting in which Esri was ratified as an Ocean Leadership member on October 25, 2012, in Washington, DC. She will soon be called on to serve on Ocean Leadership committees.

“Successful decision making and effective policy building is based on spatial thinking, spatial data, and spatial methods,” said Wright. “Esri’s affiliation with Ocean Leadership adds a spatial component that will help drive our nation’s initiatives to improve the management of the oceans.

“Membership in Ocean Leadership will advance Esri’s Ocean GIS Initiative, which is the company’s commitment to developing products and strategic plans for ocean science, resources management, and conservation.”

[Source: Esri press release]

Spatial and Temporal Analysis of Deforestation and Forest Degradation in Selangor: Implication to Carbon Stock Above Ground

4th Conference on Data Mining and Optimization (DMO)4th Conference on Data Mining and Optimization (DMO), 02-04 September 2012

Syed Abdullah and Sharifah Mastura

“This paper aims to develop an operational methodology for monitoring spatial and temporal changes due to deforestation in Selangor over a 22 year period. The driving forces determining the changes were also analysed. Overall, the results show that the causes of deforestation were the economic factors, namely agriculture intensification, and population dynamics, related to the process of urbanization. However, deforestation statistics shows only a total of 10 percent decrease; it is the degradation of the remaining forest that is the major concern. Knowledge on deforestation and its driving forces in Selangor is very important as it provides the basis for the calculation of the total amount of carbon stock above ground. It also gives insight into the appropriate intervention measures that can be taken to increase carbon stock, thus reducing the release of carbon dioxide emission to the atmosphere.”

The Influence of DEM Quality on Mapping Accuracy of Coniferous- and Deciduous-Dominated Forest Using TerraSAR‑X Images

Remote Sensing, 2012, 4(3), 661-681

Sonia M. Ortiz, Johannes Breidenbach, Ralf Knuth and Gerald Kändler

“Climate change is a factor that largely contributes to the increase of forest areas affected by natural damages. Therefore, the development of methodologies for forest monitoring and rapid assessment of affected areas is required. Space-borne synthetic aperture radar (SAR) imagery with high resolution is now available for large-scale forest mapping and forest monitoring applications. However, a correct interpretation of SAR images requires an adequate preprocessing of the data consisting of orthorectification and radiometric calibration. The resolution and quality of the digital elevation model (DEM) used as reference is crucial for this purpose. Therefore, the primary aim of this study was to analyze the influence of the DEM quality used in the preprocessing of the SAR data on the mapping accuracy of forest types.

Application of the classification model in Biberach.

Application of the classification model in Biberach. (a) Map of deciduous- and
coniferous-dominated forest based on combined leaf-on and leaf-off TerraSAR-X images (acquired in 2008 and 2009) preprocessed with the ALS DTM; (b) official forest stand map (established 2007); (c) ortho-photographs (acquired on 2007).

“In order to examine TerraSAR-X images to map forest dominated by deciduous and coniferous trees, High Resolution SpotLight images were acquired for two study sites in southern Germany. The SAR images were preprocessed with a Shuttle Radar Topography Mission (SRTM) DEM (resolution approximately 90 m), an airborne laser scanning (ALS) digital terrain model (DTM) (5 m resolution), and an ALS digital surface model (DSM) (5 m resolution). The orthorectification of the SAR images using high resolution ALS DEMs was found to be important for the reduction of errors in pixel location and to increase the classification accuracy of forest types. SAR images preprocessed with ALS DTMs resulted in the highest classification accuracies, with kappa coefficients of 0.49 and 0.41, respectively. SAR images preprocessed with ALS DTMs resulted in greater accuracy than those preprocessed with ALS DSMs in most cases. The classification accuracy of forest types using SAR images preprocessed with the SRTM DEM was fair, with kappa coefficients of 0.23 and 0.32, respectively.Analysis of the radar backscatter indicated that sample plots dominated by coniferous trees tended to have lower scattering coefficients than plots dominated by deciduous trees. Leaf-off images were only slightly better suited for the classification than leaf-on images. The combination of leaf-off and leaf-on improved the classification accuracy considerably since the backscatter changed between seasons, especially in deciduous-dominated forest.”

Read the paper [PDF]

Doing Fieldwork on the Seafloor: Photogrammetric Techniques to Yield 3D Visual Models from ROV Video

Computers & GeosciencesComputers & Geosciences, published online 22 October 2012

Tom Kwasnitschka, Thor H. Hansteen, Colin W. Devey, and Steffen Kutterolf


  • A new technology for deep-sea micro scale mapping is demonstrated.
  • Photogrammetry based on ROV video yields 3D models.
  • Quantitative data extraction yields geoscientific insights.
  • The workflow is readily replicable and based on industrial software.

“Remotely Operated Vehicles (ROVs) have proven to be highly effective in recovering well localized samples and observations from the seafloor. In the course of ROV deployments, however, huge amounts of video and photographic data are gathered which present tremendous potential for data mining. We present a new workflow based on industrial software to derive fundamental field geology information such as quantitative stratigraphy and tectonic structures from ROV-based photo and video material.

Warping effects due to missing lens distortion parameters (a) superimposed on the correct reconstruction (b).

Warping effects due to missing lens distortion parameters (a) superimposed on the correct reconstruction (b). Both models have been aligned at the first camera pose (c), where deviations in the model geometry and position are already apparent. The largest dislocation (gray arrow) in position and camera angle is found between the last images, (d) showing the warped path and (e) the correct path, deviating 29° in pitch, 8° in roll and 1.8° in heading. Crosses mark the location of a corresponding feature referenced in the text. Measurements of a corresponding bedding plane (white planes) indicate a strong deviation in strike (67°, lines) and dip (12°, arrows). The light transparent model (f) and camera planes (g) illustrate the model, which has been aligned to the track coordinates, resulting in positioning and also scaling errors. The white grid represents the true horizontal plane.

“We demonstrate proof of principle tests for this workflow on video data collected during dives with the ROV Kiel6000 on a new hot spot volcanic field that was recently identified southwest of the island of Santo Antão in the Cape Verdes. Our workflow allows us to derive three-dimensional models of outcrops facilitating quantitative measurements of joint orientation, bedding structure, grain size comparison and photo mosaicking within a georeferenced framework. The compiled data facilitate volcanological and tectonic interpretations from hand specimen to outcrop scales based on the quantified optical data. The demonstrated procedure is readily replicable and opens up possibilities for post-cruise “virtual fieldwork” on the seafloor.”

Out-of-School Suspensions by Home Neighborhood: A Spatial Analysis of Student Suspensions in the San Bernardino City Unified School District

Thesis Presented to the Faculty of the USC Graduate School, December 2012

Stephen O. Gervais

“Student out-of-school suspensions have been an ongoing problem in US schools for many years. Current methods of analysis have not yielded new insights into this problem. The purpose of this thesis is to consider student suspension incidents from a spatial perspective. Using student level data provided by SBCUSD, a large urban school district in southern California, suspension incidents were geocoded and mapped to student home neighborhoods within the district for the purpose of identifying whether or not suspensions incidents are clustered and, if so, to determine by neighborhood where the clusters are located.

2009-10 predicted suspension incidents, GWR Model

2009-10 predicted suspension incidents, GWR Model

“Spatial analysis indicated that suspension incident clustering does exist. Hotspot analysis showed variations in the suspension incident clustering pattern when disaggregating results by significant student subgroups and incident types. Neighborhoods were classified by these patterns and the results visualized in a choropleth map. As a final step in the analysis, a geographically weighted regression model predicting districtwide suspension incidents by census block group was developed. The model, based on the total number of days previously suspended and the number of students identified as having a low socioeconomic status, had an adjusted R2 greater than 0.90. Additional research needs to be conducted to verify that the patterns noted within this thesis hold steady. If so, discipline issues within SBCUSD may in part be influenced by local neighborhood factors. This becomes an opportunity for the school district to act at a local level and identify strategies to reduce suspensions and improve student outcomes.”

A Novel Satellite Mission Concept for Upper Air Water Vapour, Aerosol and Cloud Observations Using Integrated Path Differential Absorption LiDAR Limb Sounding

Remote Sensing, 2012, 4(4), published online 27 March 2012

Alex Hoffmann , Debbie Clifford , Josep Aulinas , James G. Carton , Florian Deconinck , Berivan Esen , Jakob Hüsing , Katharina Kern , Stephan Kox , David Krejci , Thomas Krings , Steffen Lohrey , Patrick Romano , Ricardo Topham, and Claudia Weitnauer

“We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively.

“This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010.”

The Development of a GIS Methodology to Assess the Potential for Water Resource Contamination due to New Development in the 2012 Olympic Park Site, London

Computers & GeosciencesComputers & Geosciences, Published online 26 September 2012

A.P. Marchant, V.J. Banks, K.R. Royse, and S.P. Quigley


  • We describe the Initial Screening Tool, a GIS for identifying pollutant linkages.
  • The tool aids the assessment of potential risk to groundwater and surface water.
  • The methodology is unique in its use of 3-D geological data.
  • Network Analysis is used to model the flow of surface water features.

“The Initial Screening Tool (IST) has been developed to enable Planners to assess the potential risk to ground and surface water due to remobilisation of contaminants by new developments. The IST is a custom built GIS application that improves upon previous screening tools developed by the British Geological Survey (BGS) through the inclusion of 3-D geological data and an enhanced scoring methodology. The key new feature of the IST is the ability to track individual pollutant linkages, from a source of contamination, along multiple possible Pathways to potentially susceptible Receptors. A rule based approach allows the methodology to be easily updated, and as a result the IST has a role in scenario planning. The application provides output in the form of an automatically generated report, in which details of the potential pollutant linkages identified are presented. The initial research area selected was the Olympic Park site, London.”

Estimating Net Primary Production of Turfgrass in an Urban-Suburban Landscape with QuickBird Imagery

Remote Sensing, 2012, 4(4), published online 27 March 2012

Jindong Wu and Marvin E. Bauer

“Vegetation is a basic component of urban-suburban environments with significant area coverage. As a major vegetation type in US cities, urban turfgrass provides a range of important ecological services. This study examined the biological carbon fixation of turfgrass in a typical residential neighborhood by linking ground-based measurements, high resolution satellite remote sensing, and ecological modeling. The spatial distribution of turfgrass and its vegetative conditions were mapped with QuickBird satellite imagery. The significant amount of shadows existing in the imagery were detected and removed by taking advantage of the high radiometric resolution of the data.

QuickBird false color images before shadows were removed (left) and after spectral information was restored for shadow pixels (right).

QuickBird false color images before shadows were removed (left) and after
spectral information was restored for shadow pixels (right).

“A remote sensing-driven production efficiency model was developed and parameterized with field biophysical measurements to estimate annual net primary production of turfgrass. The results indicated that turfgrass accounted for 38% of land cover in the study area. Turfgrass assimilated 0–1,301 g∙C∙m−2∙yr−1 depending on vegetative conditions and management intensity. The average annual net primary production per unit turfgrass cover by golf course grass (1,100.5 g∙C∙m−2) was much higher than that by regular lawn grass (771.2 g∙C∙m−2). However, lawn grass contributed more to the total net primary production than golf course grass due to its larger area coverage, although with higher spatial variability.”