End-user informed demographic projections for Hamilton up to 2041

This report provides a set of projections of the population of Hamilton City and the larger Hamilton Zone. The projections have been calculated by means of the cohort component model. The projections can be considered alongside official Statistics New Zealand projections, but differ from the latter in terms of assumptions made about net migration. These assumptions constitute a number of scenarios that were informed by the Hamilton City Council and local consultations. These scenarios are linked to the potential impact of a number of economic development activities. The report also contains projections of the number of households, the labour force and two ethnic groups: Māori and New Zealand Europeans. In addition, a dwellings-based methodology is used to produce small area (Census Area Unit) projections. Across the scenarios, Hamilton City’s projected population growth over the next two decades ranges from 13.8 percent to 36.0 percent. This is between 1.5 to 12.2 percentage points higher than the corresponding projected national growth

LA2050

LA2050 is an initiative to create a shared vision for the future of Los Angeles, and to drive and track progress toward that vision. Spearheaded by the Goldhirsh Foundation, the LA2050 Report has looked at the health of the region along well-defined indicators (Arts & Cultural Vitality, Education, Environmental Quality, Health, Housing, Income & Employment, Public Safety, and Social Connectedness), and made informed projections about where we'll be in the year 2050 if we continue on this current path

The Future of California Transportation Revenue

Stable, predictable, and adequate transportation revenues are needed if California is to plan and deliver an excellent transportation system. This report provides a brief history of transportation revenue policies and potential futures in California. It then presents projections of transportation revenue under the recently enacted Senate Bill 1, the Road Repair and Accountability Act of 2017. Those revenue projections are compared with projections of revenue should SB 1 be repealed by voters in the November 2018 election. State-generated transportation revenues will be higher under SB1 than if the act is repealed. For 2020, the mean projection is that the state will collect $10.4 billion with SB1 in place and$6.6 billion without it, a difference of $3.8 billion. Over time, changes in fuel economy and other factors will change annual revenue By 2040, the mean projection is that the state will collect$8.6 billion with SB1 and $3.4 billion without it, a$5.2 billion difference. The total of all state transportation revenue collected between 2018 and 2040, assuming no other revisions to transportation revenue programs during these years, will be about $100 billion less if SB 1 is repealed than if the law is retained. The final section of the report addresses public attitudes toward transportation tax and fee policies, since future any policy changes must be informed by public willingness to consider revenue increases and opinions about which taxes or fees would be most appropriate

Probabilistic projections of HIV prevalence using Bayesian melding

The Joint United Nations Programme on HIV/AIDS (UNAIDS) has developed the Estimation and Projection Package (EPP) for making national estimates and short-term projections of HIV prevalence based on observed prevalence trends at antenatal clinics. Assessing the uncertainty about its estimates and projections is important for informed policy decision making, and we propose the use of Bayesian melding for this purpose. Prevalence data and other information about the EPP model's input parameters are used to derive a probabilistic HIV prevalence projection, namely a probability distribution over a set of future prevalence trajectories. We relate antenatal clinic prevalence to population prevalence and account for variability between clinics using a random effects model. Predictive intervals for clinic prevalence are derived for checking the model. We discuss predictions given by the EPP model and the results of the Bayesian melding procedure for Uganda, where prevalence peaked at around 28% in 1990; the 95% prediction interval for 2010 ranges from 2% to 7%.Comment: Published at http://dx.doi.org/10.1214/07-AOAS111 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Probabilistic projections of HIV prevalence using Bayesian melding

The Joint United Nations Programme on HIV/AIDS (UNAIDS) has developed the Estimation and Projection Package (EPP) for making national estimates and short-term projections of HIV prevalence based on observed prevalence trends at antenatal clinics. Assessing the uncertainty about its estimates and projections is important for informed policy decision making, and we propose the use of Bayesian melding for this purpose. Prevalence data and other information about the EPP model's input parameters are used to derive a probabilistic HIV prevalence projection, namely a probability distribution over a set of future prevalence trajectories. We relate antenatal clinic prevalence to population prevalence and account for variability between clinics using a random effects model. Predictive intervals for clinic prevalence are derived for checking the model. We discuss predictions given by the EPP model and the results of the Bayesian melding procedure for Uganda, where prevalence peaked at around 28% in 1990; the 95% prediction interval for 2010 ranges from 2% to 7%.Comment: Published at http://dx.doi.org/10.1214/07-AOAS111 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Flow of New Doctorates

[Excerpt] As noted by Bowen and Sosa, their projections of the supply side of the academic labor market, which are typical of those used in other studies, are based on a number of simplifying assumptions. Similarly, their proposed policy remedies to increase the flow of new doctorates, such as increasing financial support for graduate students and shortening the time it takes students to receive degrees, are made presenting only scanty evidence on the likely magnitude of supply responses to these changes. This essay, which draws heavily from my study (Ehrenberg 1991), reviews the academic literature and available data (from a wide range of sources) to summarize what we know about new doctorate supply and what we need to know to make informed policy decisions

Causally-informed deep learning to improve climate models and projections

Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds and convection. Deep learning can learn these subgrid-scale processes from computationally expensive storm-resolving models. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non-physical correlations learned by the neural networks due to the complexity of the physical dynamical system. We apply a causal discovery method to unveil key physical drivers in the set of input predictors of atmospheric subgrid-scale processes of a superparameterized climate model. We show that the climate simulations with causally-informed neural network parameterizations clearly outperform the non-causal approach. These results demonstrate that the combination of causal discovery and deep learning helps removing spurious correlations and optimizing the neural network algorithm

Using Model-Data Fusion to Interpret Past Trends, and Quantify Uncertainties in Future Projections, of Terrestrial Ecosystem Carbon Cycling

Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model-data fusion framework benchmarked with an artificial neural network (ANN). We used 18 years of eddy-covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process-based carbon cycle model, both against the measured decadal trend and under future long-term climate change. The use of high-frequency eddy-covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long-term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data-informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long-term trend. Furthermore, we use the model-data fusion framework, and the ANN, to show that the long-term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model-data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data-informed process-based model.Organismic and Evolutionary Biolog

'First Portal in a Storm': A Virtual Space for Transition Students

The lives of millennial students are epitomised by ubiquitous information, merged technologies, blurred social-study-work boundaries, multitasking and hyperlinked online interactions (Oblinger & Oblinger, 2005). These characteristics have implications for the design of online spaces that aim to provide virtual access to course materials, administrative processes and support information, all of which is required by students to steer a course through the storm of their transition university experience. Previously we summarised the challenges facing first year students (Kift & Nelson, 2005) and investigated their current online engagement patterns, which revealed three issues for consideration when designing virtual spaces (Nelson, Kift & Harper, 2005). In this paper we continue our examination of students’ interactions with online spaces by considering the perceptions and use of technology by millennial students as well as projections for managing the virtual learning environments of the future. The findings from this analysis are informed by our previous work to conceptualise and describe the architecture of a transition portal

Probabilistic 21st and 22nd Century Sea-Level Projections at a Global Network of Tide-Gauge Sites

Sea-level rise due to both climate change and non-climatic factors threatens coastal settlements, infrastructure, and ecosystems. Projections of mean global sea-level (GSL) rise provide insufficient information to plan adaptive responses; local decisions require local projections that accommodate different risk tolerances and time frames and that can be linked to storm surge projections. Here we present a global set of local sea-level (LSL) projections to inform decisions on timescales ranging from the coming decades through the 22nd century. We provide complete probability distributions, informed by a combination of expert community assessment, expert elicitation, and process modeling. Between the years 2000 and 2100, we project a very likely (90% probability) GSL rise of 0.51.2m under representative concentration pathway (RCP) 8.5, 0.40.9m under RCP 4.5, and 0.30.8m under RCP 2.6. Site-to-site differences in LSL projections are due to varying non-climatic background uplift or subsidence, oceanographic effects, and spatially variable responses of the geoid and the lithosphere to shrinking land ice. The Antarctic ice sheet (AIS) constitutes a growing share of variance in GSL and LSL projections. In the global average and at many locations, it is the dominant source of variance in late 21st century projections, though at some sites oceanographic processes contribute the largest share throughout the century. LSL rise dramatically reshapes flood risk, greatly increasing the expected number of 1-in-10 and 1-in-100 year events