A method for evaluating population and infrastructure exposed to natural hazards: tests and results for two recent Tonga tsunamis.

Background: Coastal communities are highly exposed to ocean- and -related hazards but often lack an accurate population and infrastructure database. On January 15, 2022 and for many days thereafter, the Kingdom of Tonga was cut off from the rest of the world by a destructive tsunami associated with the Hunga Tonga Hunga Ha'apai volcanic eruption. This situation was made worse by COVID-19-related lockdowns and no precise idea of the magnitude and pattern of destruction incurred, confirming Tonga's position as second out of 172 countries ranked by the World Risk Index 2018. The occurrence of such events in remote island communities highlights the need for (1) precisely knowing the distribution of buildings, and (2) evaluating what proportion of those would be vulnerable to a tsunami. Methods and Results: A GIS-based dasymetric mapping method, previously tested in New Caledonia for assessing and calibrating population distribution at high resolution, is improved and implemented in less than a day to jointly map population clusters and critical elevation contours based on runup scenarios, and is tested against destruction patterns independently recorded in Tonga after the two recent tsunamis of 2009 and 2022. Results show that ~ 62% of the population of Tonga lives in well-defined clusters between sea level and the 15 m elevation contour. The patterns of vulnerability thus obtained for each island of the archipelago allow exposure and potential for cumulative damage to be ranked as a function of tsunami magnitude and source area. Conclusions: By relying on low-cost tools and incomplete datasets for rapid implementation in the context of natural disasters, this approach works for all types of natural hazards, is easily transferable to other insular settings, can assist in guiding emergency rescue targets, and can help to elaborate future land-use planning priorities for disaster risk reduction purposes. Supplementary Information: The online version contains supplementary material available at 10.1186/s40677-023-00235-8.

Determination of the nighttime light imagery for urban city population using DMSP-OLS methods in Istanbul.

Demography researchers and scientists have been effectively utilizing advanced technologies and methods such as geographical information systems, spatial statistics, georeferenced data, and satellite images for the last 25 years. Areal interpolation methods have also been adopted for the development of population density maps which are essential for a variety of social and environmental studies. Still, a good number of social scientists are skeptical about such technologies due to the complexity of methods and analyses. In this regard, a practical intelligent dasymetric mapping (IDM) tool that facilitates the implementation of the statistical analyses was used in this study to develop the population distribution map for the Istanbul metropolitan area via night light data provided by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and the census records of the study area. A population density map was also produced using the choropleth mapping method to enable to make a comparison of the traditional and intelligent population density mapping implementations. According to the dasymetric population density map, 38.5% of the study area fell into sparse density category while low, moderate, high, and very high population density class percentages were found to be 9.4%, 5.5%, 2.9%, and 0.1% respectively. On the other hand, the percentages of the same population density classes ranking from sparse to very high in the choropleth map were determined to be 90.7%, 7.3%, 1.7%, 0.3%, and 0%. In the change analysis made as a result of the classification, the changes between the city area and the population were revealed. During this period, the city area and population grew. Spatial change has also been interpreted by comparing it with population changes. There appears to be a remarkable increase in both surface area and population. It is observed that the increase is especially in the south and northwest of the city. With the population increase, the number of new residential areas has increased. It is thought that behind this growth, there are different reasons besides the effect of the increase in residential areas. When the environmental awareness of people has increased more than in the past centuries, new solutions should be produced in order to be more controlled, smart, and sustainable while planning the cities of the future. Considering that the development of technology and remote sensing techniques is progressing in parallel with this technology, this study in which GIS technologies integrated with satellite images are used, it is thought that it will contribute positively to the studies in this area in terms of regular development of urban areas, increasing the opportunity to make fast and correct decisions, and creating infrastructure for studies such as monitoring and prevention of illegal housing.

Modeling spatial distribution of population for environmental epidemiological studies: Comparing the exposure estimates using choropleth versus dasymetric mapping.

Precise population information is critical for identifying more accurate environmental exposures for air pollution impacts analysis. Basically, there are two methods for estimating spatial distribution of population, choropleth and dasymetric mapping. While the choropleth approach accounts for linear distribution of population over area based on census tract units, the dasymetric model accounts for a more heterogeneous population density by quantifying the association between the area-class map data categories and values of the statistical surface as encoded in the census dataset. Environmental epidemiological studies have indicated the dasymetric mapping as a more accurate approach to estimate and characterize population densities in large urban areas. However, investigations that have attempted to compare the exposure estimates from choropleth versus dasymetric mapping in environmental health analysis are still missing. This paper addresses this gap and compares the impact of using choropleth and dasymetric mapping in different exposure metrics. We compare the impact of using choropleth and dasymetric mapping in three case studies, defined here as case study A (relationship between urban structure types and health), case study B (PM2.5 emissions and human exposure), and case study C (distance-decays of mortality risk related to PM2.5 emitted by traffic along major highways). These case studies represent previous investigations performed by our research group where spatial distribution of population was an essential input for analysis. Our findings indicate that the method used to estimate spatial distribution of population impacts significantly the exposure estimates. We observed that the choropleth mapping overestimated exposure for the case study A and B, while for the case study C the exposure was underestimated by the choropleth approach. Our findings show that the dasymetric model is a preferred method for creating spatially-explicit information about population distribution for health exposure studies. The results presented here can be useful for the environmental health community to more accurately assess the relationship between environmental factors and health risks.

A geographic information system-based method for estimating cancer rates in non-census defined geographical areas.

PURPOSE: To address locally relevant cancer-related health issues, health departments frequently need data beyond that contained in standard census area-based statistics. We describe a geographic information system-based method for calculating age-standardized cancer incidence rates in non-census defined geographical areas using publically available data. METHODS: Aggregated records of cancer cases diagnosed from 2009 through 2013 in each of Chicago's 77 census-defined community areas were obtained from the Illinois State Cancer Registry. Areal interpolation through dasymetric mapping of census blocks was used to redistribute populations and case counts from community areas to Chicago's 50 politically defined aldermanic wards, and ward-level age-standardized 5-year cumulative incidence rates were calculated. RESULTS: Potential errors in redistributing populations between geographies were limited to <1.5% of the total population, and agreement between our ward population estimates and those from a frequently cited reference set of estimates was high (Pearson correlation r = 0.99, mean difference = -4 persons). A map overlay of safety-net primary care clinic locations and ward-level incidence rates for advanced-staged cancers revealed potential pathways for prevention. CONCLUSIONS: Areal interpolation through dasymetric mapping can estimate cancer rates in non-census defined geographies. This can address gaps in local cancer-related health data, inform health resource advocacy, and guide community-centered cancer prevention and control.

Fine-Scale Population Estimation by 3D Reconstruction of Urban Residential Buildings.

Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable.

Simulação da quantidade máxima de domicílios permitida por quadras em Belo Horizonte / Simulation of the maximum number of households allowed by block in Belo Horizonte / /Simulación de la cantidad máxima de viviendas permitidas por cuadra en Belo Horizonte

O artigo propõe uma metodologia de simulação da quantidade máxima de domicílios comportada por cada quadra do município de Belo Horizonte. Os parâmetros utilizados na simulação foram extraídos das informações cadastrais da Prefeitura, associadas com os resultados dos Censos 2000 e 2010. A simulação é desenvolvida a partir de uma base georreferenciada das quadras do município, construída a partir do mapeamento dasimétrico da distribuição domiciliar na capital mineira em 2011. Em um segundo momento, é realizada a simulação do limite máximo de novos domicílios permitidos pelas leis municipais, caso todos os lotes vagos sejam ocupados em sua capacidade máxima e caso todas as edificações com potencial de demolição cedam lugar a novos edifícios residenciais também construídos em sua capacidade domiciliar máxima. Os resultados mostram qual seria o máximo de domicílios que poderia vir a se instalar em Belo Horizonte no futuro. Estas informações servem como insumo para os dados de projeções demográficas de pequenas áreas, ao apontar onde a expansão e o adensamento domiciliar podem ocorrer, bem como o limite máximo suportado em cada quadra. De forma complementar, são indicados caminhos para discutir os limites potenciais da ocupação urbana de Belo Horizonte, as áreas preferenciais de adensamento e suas implicações para o planejamento...

Abstract This paper proposes a methodology to simulate scenarios of maximum number of households for each city block in the municipality of Belo Horizonte. The parameters used in the simulation were obtained from municipal registration data as well as from the 2000 and 2010 Census results. The simulation is based on geo-referenced data for each municipal block combined with dasymetric mapping of the capital city's housing distribution in 2011. The study simulates the greatest amount of additional housing that would occur, under the limits of municipal law, if all vacant urban lots were occupied to their maximum capacity, and if buildings now at risk of demolition were substituted by new buildings constructed so as to yield their highest household capacity. The results show the maximum number of households that Belo Horizonte would have if these conditions were achieved. In addition to demonstrating the maximum number of households allowed in each city block, the paper identifies where urban expansion and consolidation is likely to occur. This exercise also provides a contribution to the field of small area population projections. The proposed scenarios additionally help to guide discussions about priority areas and potential alternatives for urban expansion and consolidation, as well as on their implications for urban planning...

Resumen El artículo propone una metodología de simulación de la cantidad máxima de viviendas que podría tener cada cuadra en el municipio de Belo Horizonte. Los parámetros utilizados en la simulación se extrajeron de la información del registro de la Municipalidad, asociada con los resultados de los censos de 2000 y 2010. La simulación se desarrolló a partir de una base de datos georreferenciada de las cuadras del municipio, construida mediante un mapeo dasimétrico de la distribución de las viviendas en la ciudad en 2011. En una segunda etapa se realizó la simulación del límite máximo de nuevas viviendas permitidas por las leyes municipales, en el caso que todos los lotes baldíos fueran ocupados a su máxima capacidad y que todas las edificaciones con potencial de demolición dieran lugar a nuevos edificios residenciales construidos también a su capacidad habitacional máxima. Los resultados muestran cuál sería el máximo de viviendas que podrían llegar a instalarse en Belo Horizonte en el futuro. Esta información sirve como insumo para las proyecciones demográficas de pequeñas áreas, pues muestra hacia dónde se puede producir la expansión y densificación de viviendas, además del límite máximo de viviendas soportado en cada cuadra. De forma complementaria, se sugieren caminos para discutir los límites potenciales de la ocupación urbana de Belo Horizonte, las zonas preferenciales de aglomeración y sus implicaciones para la planificación...