A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery

We propose a neural network component, the regional aggregation layer, that makes it possible to train a pixel-level density estimator using only coarse-grained density aggregates, which reflect the number of objects in an image region. Our approach is simple to use and does not require domain-specific assumptions about the nature of the density function. We evaluate our approach on several synthetic datasets. In addition, we use this approach to learn to estimate high-resolution population and housing density from satellite imagery. In all cases, we find that our approach results in better density estimates than a commonly used baseline. We also show how our housing density estimator can be used to classify buildings as residential or non-residential.Comment: 10 pages, 8 figures. ACM SIGSPATIAL 2018, Seattle, US

Addressing Inequities in Urban Health: Do Decision-Makers Have the Data They Need? Report from the Urban Health Data Special Session at International Conference on Urban Health Dhaka 2015

Rapid and uncontrolled urbanisation across low and middle-income countries is leading to ever expanding numbers of urban poor, defined here as slum dwellers and the homeless. It is estimated that 828 million people are currently living in slum conditions. If governments, donors and NGOs are to respond to these growing inequities they need data that adequately represents the needs of the urban poorest as well as others across the socio-economic spectrum. We report on the findings of a special session held at the International Conference on Urban Health, Dhaka 2015. We present an overview of the need for data on urban health for planning and allocating resources to address urban inequities. Such data needs to provide information on differences between urban and rural areas nationally, between and within urban communities. We discuss the limitations of data most commonly available to national and municipality level government, donor and NGO staff. In particular we assess, with reference to the WHO’s Urban HEART tool, the challenges in the design of household surveys in understanding urban health inequities. We then present two novel approaches aimed at improving the information on the health of the urban poorest. The first uses gridded population sampling techniques within the design and implementation of household surveys and the second adapts Urban HEART into a participatory approach which enables slum residents to assess indicators whilst simultaneously planning the response. We argue that if progress is to be made towards inclusive, safe, resilient and sustainable cities, as articulated in Sustainable Development Goal 11, then understanding urban health inequities is a vital pre-requisite to an effective response by governments, donors, NGOs and communities

Spatiotemporal patterns of population in mainland China, 1990 to 2010

According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from ~18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (~100 m) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository

HIV-associated bladder cancer: a case series evaluating difficulties in diagnosis and management

<p>Abstract</p> <p>Background</p> <p>Chronic human immunodeficiency virus (HIV) infection is associated with an increased incidence of Non-Acquired Immunodeficiency Syndrome (non-AIDS) defining cancers. To date, only a limited number of cases of bladder cancer have been linked with HIV infection. We sought to describe the clinical characteristics of HIV-associated bladder cancer.</p> <p>Methods</p> <p>A retrospective study was performed involving HIV-positive patients with bladder cancer, combining cases from multiple institutions with published case reports. Data regarding patient demographics, HIV status, clinical presentation, pathology, cancer treatment, and outcome were analyzed using descriptive statistics.</p> <p>Results</p> <p>Eleven patients were identified with a median age of 55 years (range, 33 - 67). The median CD4+ count at cancer diagnosis was 280 cells/mm³(range, 106 - 572 cells/mm³). Six patients (55%) had a known risk factor for bladder cancer, and nine (82%) presented with hematuria. Ten patients had transitional cell carcinoma, and most had superficial disease at presentation. Treatment included mainly transurethral resection of bladder tumor followed by a combination of local and systemic therapies. One patient received intravesical bacillus Calmette-Guèrin (BCG) without complication. Several patients (55%) were alive following therapy, although many (64%) suffered from local relapse and metastatic disease.</p> <p>Conclusion</p> <p>Bladder cancer is part of the growing list of cancers that may be encountered in patients living longer with chronic HIV-infection. Our patients presented at a younger age and with only mild immunosuppression, however, they experienced an expected course for their bladder cancer. Hematuria in an HIV-infected patient warrants a complete evaluation.</p

Latency Associated Peptide Has In Vitro and In Vivo Immune Effects Independent of TGF-β1

Latency Associated Peptide (LAP) binds TGF-β1, forming a latent complex. Currently, LAP is presumed to function only as a sequestering agent for active TGF-β1. Previous work shows that LAP can induce epithelial cell migration, but effects on leukocytes have not been reported. Because of the multiplicity of immunologic processes in which TGF-β1 plays a role, we hypothesized that LAP could function independently to modulate immune responses. In separate experiments we found that LAP promoted chemotaxis of human monocytes and blocked inflammation in vivo in a murine model of the delayed-type hypersensitivity response (DTHR). These effects did not involve TGF-β1 activity. Further studies revealed that disruption of specific LAP-thrombospondin-1 (TSP-1) interactions prevented LAP-induced responses. The effect of LAP on DTH inhibition depended on IL-10. These data support a novel role for LAP in regulating monocyte trafficking and immune modulation

Modelling the changing population distribution: example with the Kenyan Coast, 1979-2009

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time invariant or temporally-explicit. Here we make use of recently released multi-temporal high resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields

Modelling the changing population distribution: example with the Kenyan Coast, 1979-2009

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time invariant or temporally-explicit. Here we make use of recently released multi-temporal high resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields