Exploring Uncertainty in Canine Cancer Data Sources Through Dasymetric Refinement

In spite of the potentially groundbreaking environmental sentinel applications, studies of canine cancer data sources are often limited due to undercounting of cancer cases. This source of uncertainty might be further amplified through the process of spatial data aggregation, manifested as part of the modifiable areal unit problem (MAUP). In this study, we explore potential explanatory factors for canine cancer incidence retrieved from the Swiss Canine Cancer Registry (SCCR) in a regression modeling framework. In doing so, we also evaluate differences in statistical performance and associations resulting from a dasymetric refinement of municipal units to their portion of residential land. Our findings document severe underascertainment of cancer cases in the SCCR, which we linked to specific demographic characteristics and reduced use of veterinary care. These explanatory factors result in improved statistical performance when computed using dasymetrically refined units. This suggests that dasymetric mapping should be further tested in geographic correlation studies of canine cancer incidence and in future comparative studies involving human cancers

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Need for an integrated deprived area "slum" mapping system (IDEAMAPS) in low-and middle-income countries (LMICS)

Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying "slum households" in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low-and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas

Participant Reactions to Two-Way Immersion (TWI) Programs

The purpose of this study was to elicit participant reactions to two-way immersion (TWI) programs in the United States of America. A large number of recent studies have focused on instructor views and perspectives of two-way immersion programs, so this study aimed to gain insight from students who are, or who have, participated in TWI programs throughout North America. One hundred fifty-one TWI schools throughout the United States were contacted and asked to participate in this study. Two similar surveys were developed, one for current TWI students, and another for former TWI students. Students from these two groups were asked to fill out a confidential online survey that addressed specific linguistic skills, abilities, and preferences, as well as connection to the cultures of the target language. Forty-eight percent of the survey respondents were native speakers of English, and the remaining 52% were non-native speakers of English. The number of respondents to the former student survey was so low that the data were inconclusive, and, therefore, will not be included in this study. Since the survey was conducted online, the data were stored in a comma-delimited format for further evaluation. The data were then tallied and analyzed for common themes

Binary classification of the Kinshasa and Bandundu provinces in the Democratic Republic of the Congo — settled versus non settled

This dataset was created based on a settlement layer produced by the Oak Ridge National Laboratory using feature extraction from high-resolution imagery for population modelling work undertaken in the Kinshasa and Kongo-Central provinces in the Democratic Republic of the Congo. The settlement layer consists of settlement polygons of approximately 7 meters resolution. The polygons were rasterized based on a reference grid with a resolution of 3 arc-seconds, approximately 90 meters. The presence of at least one settlement polygon designated a settled cell. We thank the Oak Ridge National Laboratory and the Bill and Melinda Gates Foundation for the support. We would also like to extend our gratitude to Eric M. Webber and Amy N. Rose at the Oak Ridge National Laboratory and Io Blair-Freese at the Bill and Melinda Gates Foundation.</span

Regional Models of Canine Cancer Incidences

<p>The dataset consists of the variables implemented in the regional models of canine cancer incidences (dogCancerModel.txt), the adjacency matrix determining the regions (kNearestNeighbour.txt), and the Swiss municipal boundaries (SwissMunicipalities_2015.shp).</p

Assessing effects of structural zeros on models of canine cancer incidence: a case study of the Swiss Canine Cancer Registry

Epidemiological research of canine cancers could inform comparative studies of environmental determinants for a number of human cancers. However, such an approach is currently limited because canine cancer data sources are still few in number and often incomplete. Incompleteness is typically due to under-ascertainment of canine cancers. A main reason for this is because dog owners commonly do not seek veterinary care for this diagnosis. Deeper knowledge on under-ascertainment is critical for modeling canine cancer incidence, as an indication of zero incidence might originate from the sole absence of diagnostic examinations within a given sample unit. In the present case study, we investigated effects of such structural zeros on models of canine cancer incidence. In doing so, we contrasted two scenarios for modeling incidence data retrieved from the Swiss Canine Cancer Registry. The first scenario was based on the complete enumeration of incidence data for all Swiss municipal units. The second scenario was based on a filtered sample that systematically discarded structural zeros in those municipal units where no diagnostic examination had been performed. By means of cross-validation, we assessed and contrasted statistical performance and predictive power of the two modelling scenarios. This analytical step allowed us to demonstrate that structural zeros impact on the generalizability of the model of canine cancer incidence, thus challenging future comparative studies of canine and human cancers. The results of this case study show that increased awareness about the effects of structural zeros is critical to epidemiological research

A bottom-up population modelling approach to complement the population and housing census

Population and housing censuses provide essential demographic information for local, national and international decision-making and response. However, census data in the most vulnerable countries are often outdated or partial because political instability, conflict and natural disasters prevent a nationwide enumeration. The bottom-up modelling approach complements outdated or incomplete census data by estimating population counts and age/sex structures in grid cells of about 100 m using required population data on a set of fully enumerated locations and auxiliary geospatial covariates. We present the modelling effort in the Democratic Republic of Congo - the last census was conducted in 1984 - and in Burkina Faso - the last census was conducted in 2020 but covered only 70% of the country. Both models showed good predictive performance, denoted by R2 values of 0.73 and 0.63 for the respective out-of-sample predictions of population counts. The resulting bottom-up and gridded population estimates are currently used for census support and humanitarian response in both countries. This work has highlighted the flexibility of the bottom-up modelling approach, in terms of input population data, model specification and aggregation of population estimates to support specific use cases