Occupancy

About: Occupancy is a research topic. Over the lifetime, 2757 publications have been published within this topic receiving 68288 citations.

Identifying regions for conservation of sloth bears through occupancy modelling in north-eastern Karnataka, India

Abstract: In the absence of information on species in decline with contracting ranges, management should emphasize remaining populations and protection of their habitats. Threatened by anthropogenic pressure including habitat degradation and loss, sloth bears (Melursus ursinus) in India have become limited in range, habitat, and population size. We identified ecological and anthropogenic determinants of occurrence within an occupancy framework to evaluate habitat suitability of non-protected regions (with sloth bears) in northeastern Karnataka, India. We employed a systematic sampling methodology to yield presence–absence data to examine a priori hypotheses of determinants that affected occupancy. These covariates were broadly classified as habitat or anthropogenic factors. Mean number of termite mounds and trees positively influenced sloth bear occupancy, and grazing pressure expounded by mean number of livestock dung affected it negatively. Also, mean percentage of shrub coverage had no impact on bear in...

The distribution of species: occupancy, scale, and rarity

Abstract: Introduction Species occupancy is typically measured as the number of cells occupied by the species in a study area. Because it is easy to document and interpret and it correlates with species abundance, occupancy is widely used for measuring species rarity and for assessing extinction risk on which conservation decisions are made (Gaston, 1994; Fagan et al ., 2002; Hartley & Kunin, 2003; Wilson et al ., 2004). Ecologists and conservation practitioners, however, have long realized that occupancy often fails to capture significant spatial features of distribution. It is possible that two species having the same occupancy can exhibit very different patterns (Fig. 3.1). Most species in nature are discretely distributed due to the patchiness of landscapes, or due to intrinsic reproductive or dispersal behavior of the species. An outstanding problem concerning species distribution in space is how to describe the patchiness of a species and to measure the effect of changing spatial scale (cell size) on the patchiness for the purpose of predicting distribution at fine scales from coarse scales. There are two primary approaches to addressing this question. The first one is to use existing measures and methods to describe patchiness and scale effect. Many fragmentation indices in landscape ecology can be used for this purpose (Turner, Gardner & O'Neill, 2001; Wu et al ., 2003). These include edge length (perimeter), the number of patches, perimeter/area ratio and many other indices to capture the spatial features of species distribution.

Systems and methods for predictive building energy monitoring

Abstract: A system and method for predictive modeling of building energy consumption provides predicted building energy load values which are determined using kernel smoothing of historical building energy load values for a building using defined scaling factors for scaling predictor variables associated with building energy consumption. Predictor variables may include temperature, humidity, windspeed or direction, occupancy, time, day, date, and solar radiation. Scaling factor values may be defined by optimization training using historical building energy load values and measured predictor variable values for a building. Predicted and measured building energy load values are compared to determine if a preset difference threshold has been exceeded, in which case an alert signal or message is generated and transmitted to electronically and/or physically signal a user. The building energy monitoring system may be integrated with a building automation system, or may be operated as a separate system receiving building energy and predictor variable values.

Video-based estimation of building occupancy during emergency egress

Abstract: Providing real-time estimates of building occupancy to first responders during emergency events can help in search and rescue, and egress management. This paper addresses the estimation of occupancy in each zone of a building, where the building is spatially divided into non-overlapping zones that cover all areas of the building. Each zone contains video cameras located at each portal of the zone, where each camera has a signal processing algorithm that detects number of people passing through the portal in each direction. The technical approach of this paper is to develop a non-linear stochastic state-space model of people traffic during emergency egress, and apply the extended Kalman filter which uses the video signal processing outputs and the people traffic model. The approach is demonstrated on a 16,000 square-foot building that has typical occupancy of 100 people. The estimator is tested on data from an agent-based simulation, and on data from an actual fire alarm. The results show that better estimation accuracy is achieved compared to an estimation approach that uses only the video sensors.