New Zealand Police’s Policing Excellence and Prevention First Strategy: A New Approach to Police Service Delivery

2021 ◽  
pp. 207-236
Author(s):  
Garth den Heyer
Geology ◽  
2021 ◽  
Author(s):  
Steven Kidder ◽  
David J. Prior ◽  
James M. Scott ◽  
Hamid Soleymani ◽  
Yilun Shao

Peridotite xenoliths entrained in magmas near the Alpine fault (New Zealand) provide the first direct evidence of deformation associated with the propagation of the Australian-Pacific plate boundary through the region at ca. 25–20 Ma. Two of 11 sampled xenolith localities contain fine-grained (40–150 mm) rocks, indicating that deformation in the upper mantle was focused in highly sheared zones. To constrain the nature and conditions of deformation, we combine a flow law with a model linking recrystallized fraction to strain. Temperatures calculated from this new approach (625–970 °C) indicate that the observed deformation occurred at depths of 25–50 km. Calculated shear strains were between 1 and 100, which, given known plate offset rates (10–20 mm/yr) and an estimated interval during which deformation likely occurred (<1.8 m.y.), translate to a total shear zone width in the range 0.2–32 km. This narrow width and the position of mylonite-bearing localities amid mylonite-free sites suggest that early plate boundary deformation was distributed across at least ~60 km but localized in multiple fault strands. Such upper mantle deformation is best described by relatively rigid, plate-like domains separated by rapidly formed, narrow mylonite zones.


2021 ◽  
Author(s):  
Benjamin Schumacher ◽  
Katharine Melnik ◽  
Marwan Katurji ◽  
Veronica Clifford ◽  
Jiawei Zhang ◽  
...  

<p>The rate of spread (ROS) of wildfires is an important parameter for understanding fire-atmospheric interactions and developing fire-spread models, but it is also vital for firefighting operations to ensure the safety of firefighters (Plucinski 2017, Stow 2019). Spatial ROS observations are usually carried out by using visible and thermal satellite imagery of wildfires estimating the ROS on a time scale of hours to days for large fires (>100 ha) or repeated passing with an airborne thermal infrared imager for higher spatial and temporal resolution (Viedma et al. 2015, Stow 2014). For fire experiments in highly controlled conditions like laboratory fires or during light fuel prescribed burns, ROS estimation usually involves lag-correlation of temperature point measurements (Finney 2010, Johnston 2018). However, these methodologies are not applicable to fast-spreading grass or bush fires because of their temporal and spatial limitations. Instantaneous spatial ROS of these fires is needed to understand rapid changes in connection with the three major drivers of the fire: fuel, topography and atmospheric forcings.</p><p>We are presenting a new approach towards a spatial ROS product which includes newly developed image tracking methods based on thermal and visible imagery collected from unmanned aerial vehicles to estimate instantaneous, spatial ROS of fast spreading grass or bush fires. These techniques were developed using imagery from prescribed wheat-stubble burns carried out in Darfield, New Zealand in March 2018 (Finney 2018). Results show that both the visible and thermal tracking techniques produce similar mean ROS; however they differ in limitations and advantages. The visible-spectrum tracking method clearly identifies the flaming zone and provides accurate ROS measurements especially at the fire front. The thermal tracking technique is superior when resolving dynamics and ROS within the flaming zone because it resolves smaller scale structures within the imagery.</p><p> </p><p>References:</p><p>Finney, M. et al. 2010: An Examination of Fire Spread Thresholds in Discontinuous Fuel Beds.” International Journal of Wildland Fire, 163–170.</p><p>Finney, M. et al. 2018: New Zealand prescribed fire experiments to test convective heat transfer in wildland fires. In Advances in Forest Fire Research, Imprensa da Universidade de Coimbra: Coimbra, 2018.</p><p>Johnston, J. M., et al. 2018:  Flame-Front Rate of Spread Estimates for Moderate Scale Experimental Fires are Strongly Influenced by Measurement Approach. Fire 1: 16–17</p><p>Plucinski M., et al. 2017: Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation. Environmental Modelling & Software 91, 1-12.</p><p>Stow, D., et al. 2014: Measuring Fire Spread Rates from Repeat PassAirborne Thermal Infrared Imagery. Remote Sensing Letters 5: 803–881.</p><p>Stow, D., et al. 2019: Assessing uncertainty and demonstrating potentialfor estimating fire rate of spread at landscape scales based on time sequential airbornethermal infrared imaging, International Journal of Remote Sensing, 40:13, 4876-4897</p><p>Viedma, O., et al. 2015:  Fire Severity in a Large Fire in a Pinus Pinaster Forest Is Highly Predictable from Burning Conditions, Stand Structure, and Topography. Ecosystems18: 237–250.</p>


2016 ◽  
Vol 32 (4) ◽  
pp. 947-962 ◽  
Author(s):  
Kim Dunstan ◽  
Christopher Ball

Abstract Statistics New Zealand is one of the few national statistical agencies to have applied a stochastic (probabilistic) approach to official demographic projections. This article discusses the experience and benefits of adopting this new approach, including the perspective of a key user of projections, the New Zealand Treasury. Our experience is that the change is less difficult to make than might be expected. Uncertainty in the different projection inputs (components) can be modelled simply or with more complexity, and progressively applied to different projection types. This means that not all the different demographic projections an agency produces need to adopt a stochastic approach simultaneously. At the same time, users of the projections are keen to better understand the relative certainty and uncertainty of projected outcomes, given the important uses of projections.


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