Dasymetric Mapping Research Articles

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

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-censusdefined geographical areas using publically available data. 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. 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. 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.

An analysis on transit pick-up points for vulnerable people during hurricane evacuation: A case study of New Orleans

In emergency evacuation there is always a need for transit for those who do not have access to private transportation or cannot walk to safety. Particularly affected among this section of society are the poor, the disabled, and the elderly. Several cities have established prepositioned pick-up points to serve this section of the population but the number and location of the pick-up points have usually been decided using professional judgment or based on experience from past storms. As the vulnerable section of the population typically have low mobility, getting transit pick-up points close to areas of greatest need is important in order to provide better service, improve the efficiency of evacuation, and even decrease the loss of life. In this study, the spatial distribution of the population is estimated using a modified dasymetric mapping method, which estimates population distribution in terms of 30m × 30m land parcels. In each parcel, the vulnerable population is estimated from the proportion of disabled, aged, and zero car-owning persons in the area as determined from census data. Using Orleans parish in New Orleans as a case study and assuming a catchment area of ½ mile around each of the 17 existing pick-up points, the number of vulnerable persons served by the existing system was estimated. To determine whether the 17 pick-up points could be better located to serve the vulnerable optimally, the set of all eligible pick-up sites were first identified and then the pick-up points serving the greatest need were identified using integer linear programming. The results show that the 17 optimally located pick-up points triple the vulnerable population that could be served. On the question of whether 17 is the right number of pick-up points to employ, analysis shows there is a steadily decreasing average value served per pick-up point as the number of pick-up point increases. At some stage it would probably no longer be efficient to create additional pick-up points but provide a feeder service from small demand to larger demand pick-up points or link smaller demand pick-up points on a route to serve several pick-up points with a single service.

Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation

ABSTRACTNovel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.

Vulnerability Analysis of the Urban Environments to Different Seismic Scenarios: Residential Buildings and Associated Population Distribution Modelling through Integrating Dasymetric Mapping Method and GIS

Residential buildings and associated population distribution modeling, is an integral part of analysis of the direct and indirect risks related to natural disasters, including earthquakes in urban environments. In this study, the distribution of residential population along with the distribution of residential buildings is estimated. The study offers a more comprehensive and accurate model for Iran in accordance with local conditions and with higher degree of details as available and accessible databases.Dasymetric mapping method as an alternative to a choropleth map is an efficient method for the spatial distribution of data benefitting from spatial information and statistical modeling. This research intends to integrate Dasymetric mapping method and GIS (Geographical Information System) to bring spatial, locational and statistical dataset together in order to analyze vulnerability of the exposed elements at risk in an urban context considering different seismic scenarios.In this study, statistical data at urban, block and parcel level as well as digital aerial and satellite images were compiled together. Building related features were extracted from remotely sensed images and integrated with the statistical information in order to generate spatially distributed dataset. This study develops a scenario-based seismic loss estimation model for residential building in city of Sari as a case study. The data was processed according to the following steps. At first, object extraction from satellite imagery was accomplished to acquire building boundaries within urban blocks. Then, the structural and demographic information were spatially distributed using Dasymetric mapping method. Next, ground motions map for different scenarios were generated for the entire metropolitan area. In the final step, the estimation of structural damage and loss of life were calculated for different scenarios. The results from this study indicate that, in comparison with Gridding method, Dasymetric mapping method would allow for a more realistic and careful assessment of seismic vulnerability of buildings at an urban scale due to more accurate classification and distribution of underlying data.

Spatially explicit urban green indicators for characterizing vegetation cover and public green space proximity: a case study on Brussels, Belgium

ABSTRACTCities often have a substantial green infrastructure, which provides local ecosystem services that improve the quality of life of urban residents. These services should be explicitly addressed in urban development policies, and areas with insufficient vegetation and limited access to public green spaces should be identified. This paper presents two spatially explicit urban green indicators that are derived using remote sensing imagery, freely available map data and spatial analysis tools from open source geospatial libraries and commercial software. The first indicator represents proportional green cover (public as well as private) in the vicinity of each building within a city. The second indicator quantifies the proximity of public green spaces as walking distances from buildings to actual park entrances. A dasymetric mapping approach was used to take spatial variations in population density into account. This allows representing the indicators from the perspective of citizens instead of buildings, which may be more meaningful for deriving statistics at city level or at the level of neighbourhoods or administrative zones. The potential use of these indicators in a planning context is discussed on a case study carried out for the city of Brussels, Belgium.

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.

Probability of receiving a blast noise complaint based on population density

This study extends previous work done by Nykaza et al. (2012) on the probability of receiving a blast noise complaint to include population density in the analysis. Nykaza et al. specifically asked, “what is the probability of receiving a blast noise complaint on a given day within 5 km of a noise monitor?” The original study examined 102,116 blast events and 148 complaints recorded over 641 study days around one military installation. Results from the original study found that while the overall probability of receiving a blast noise complaint was very low, blast noise complaint tolerances around individual noise monitors varied significantly. The current study combines dasymetric mapping techniques—an overlay method for redistributing arbitrarily organized data based on real world circumstances—and regression analysis to examine the different complaint tolerances, and explores what role, if any, characteristics of surrounding population may play in determining the probability of receiving a blast noise c...

Measuring the vulnerability of disadvantaged populations during hurricane evacuation

In this paper, the extent to which the potentially transit-dependent portion of the population is vulnerable to the effects of a hurricane is estimated. The vulnerability of an area is defined as a composite measure of the proportion of disadvantaged persons, distance to transit, and flooding potential of people within an area. Unlike past studies which have focused on the vulnerability of the population in relatively large geographic areas, this study estimates the vulnerability of the population in 30 m × 30 m areas as defined in the National Land Cover Database. Population estimates from the national census at block level are disaggregated to the 30 m × 30 m units using a modified dasymetric mapping method in ArcGIS. The modified mapping method assigns population to each small areal unit using weights estimated by regressing the area of each land use in a census block against the population in that block. The coefficients in the regression analysis are “weights” associating population with each land use, and are used to distribute the population in each census block to the small geographic units based on their land use. In a case study of New Orleans, the results show that some areas are not well served by the existing transit pickup locations, as evidenced by their high vulnerability scores. Reassignment of pickup point locations to cover higher vulnerability score areas was investigated using integer linear programming. The results show that the optimally located pickup points serve areas with a larger average vulnerability score than the current pickup points in the study area. The method appears to be helpful in identifying vulnerable areas that, subsequently, could receive improved hurricane evacuation service in the future.

SUSTAINABLE AND SMART CITY PLANNING USING SPATIAL DATA IN WALLONIA

Abstract. Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that “Smart” city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale.

Can Dasymetric Mapping Significantly Improve Population Data Reallocation in a Dense Urban Area?

The issue of reallocating population figures from a set of geographical units onto another set of units has received a great deal of attention in the literature. Every other day, a new algorithm is proposed, claiming that it outperforms competitor procedures. Unfortunately, when the new (usually more complex) methods are applied to a new data set, the improvements attained are sometimes just marginal. The relationship cost‐effectiveness of the solutions is case‐dependent. The majority of studies have focused on large areas with heterogeneous population density distributions. The general conclusion is that as a rule more sophisticated methods are worth the effort. It could be argued, however, that when we work with a variable that varies gradually in relatively homogeneous small units, simple areal weighting methods could be sufficient and that ancillary variables would produce marginal improvements. For the case of reallocating census data, our study shows that, even under the above conditions, the most sophisticated approaches clearly yield the better results. After testing fourteen methods in Barcelona (Spain), the best results are attained using as ancillary variable the total dwelling area in each residential building. Our study shows the 3‐D methods as generating the better outcomes followed by multiclass 2‐D procedures, binary 2‐D approaches and areal weighting and 1‐D algorithms. The point‐based interpolation procedures are by far the ones producing the worst estimates.

SUSTAINABLE AND SMART CITY PLANNING USING SPATIAL DATA IN WALLONIA

Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that “Smart” city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale.

Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods.

Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice.

A Comparison of Small-Area Population Estimation Techniques Using Built-Area and Height Data, Riyadh, Saudi Arabia

Small-area population estimation is important for many applications. This paper explores the usefulness of Landsat ${\bf ETM} + $ data, remotely sensed height data, census population, and dwelling unit data to provide small-area population estimates. Riyadh, Saudi Arabia was selected as a suitable area to test a set of methods for population downscaling. Two broad approaches were applied: 1) statistical modeling and 2) areal interpolation. With regard to statistical modeling, regression through the origin was used to model the relationship between density of dwelling units and built area proportion at the block level and the coefficients were used to downscale the density of dwelling units to the parcel level. Areal interpolation with ancillary data (dasymetric mapping) used the block and parcel levels as the source and target zones, respectively. The population distribution was then estimated based on the average population per dwelling unit. Eight models were developed and tested. A conventional regression model, using only built area as a covariate, was used as a benchmark and compared with the more sophisticated models. Remotely sensed height data were used to: 1) create number of floors; 2) classify the built area into different categories; and 3) increase the user’s accuracy of the built area. It was found that remotely sensed height data were useful to explain the variation in the dependent variable across the selected study area. Dasymetric mapping was applied in order to provide a comparison, while acknowledging that the method uses population data not available in the regression approach.

A quick earthquake disaster loss assessment method supported by dasymetric data for emergency response in China

Abstract. Improving earthquake disaster loss estimation speed and accuracy is one of the key factors in effective earthquake response and rescue. The presentation of exposure data by applying a dasymetric map approach has good potential for addressing this issue. With the support of 30'' × 30'' areal exposure data (population and building data in China), this paper presents a new earthquake disaster loss estimation method for emergency response situations. This method has two phases: a pre-earthquake phase and a co-earthquake phase. In the pre-earthquake phase, we pre-calculate the earthquake loss related to different seismic intensities and store them in a 30'' × 30'' grid format, which has several stages: determining the earthquake loss calculation factor, gridding damage probability matrices, calculating building damage and calculating human losses. Then, in the co-earthquake phase, there are two stages of estimating loss: generating a theoretical isoseismal map to depict the spatial distribution of the seismic intensity field; then, using the seismic intensity field to extract statistics of losses from the pre-calculated estimation data. Thus, the final loss estimation results are obtained. The method is validated by four actual earthquakes that occurred in China. The method not only significantly improves the speed and accuracy of loss estimation but also provides the spatial distribution of the losses, which will be effective in aiding earthquake emergency response and rescue. Additionally, related pre-calculated earthquake loss estimation data in China could serve to provide disaster risk analysis before earthquakes occur. Currently, the pre-calculated loss estimation data and the two-phase estimation method are used by the China Earthquake Administration.

A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables

GIScience 2016 Short Paper Proceedings A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables April Morton 1 , Jesse Piburn 1 , Ryan McManamay 1 , Nicholas Nagle 2 , Robert N. Stewart 1 Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831 Email: {mortonam; piburnjo; mcmanamayra; stewartrn}@ornl.gov University of Tennessee, Knoxville, Department of Geography, 1000 Phillip Fulmer Way, Knoxville, TN 37916 Email: nnagle@utk.edu Abstract Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknown random output variables. Though Monte Carlo simulation is a powerful technique for the probabilistic understanding of many processes, it can only be applied if it is possible to infer the probability distributions describing the required input variables. This is particularly challenging when the input probability distributions are related to population counts unknown at desired spatial resolutions. To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations dependent on population counts. 1. Introduction Monte Carlo simulation is a numerical experimentation technique that has been widely used in a variety of scientific domains to obtain the statistics of unknown random output variables by repeatedly sampling values from a set of known input random variables and then feeding them through a computational model (Mahadevan 1997). Dasymetric mapping, on the other hand, has been widely used in the field of areal interpolation to disaggregate coarse resolution population data to a finer resolution through the use of ancillary data (Eicher and Brewer 2001). Though Monte Carlo simulation is a powerful technique for the probabilistic understanding of many processes, it can only be applied if the probability distributions describing the required input variables can be inferred. Unfortunately, conventional inference methods cannot often be used to infer the probability distributions of population counts (i.e. counts of populations with specific characteristics) that are unknown at desired spatial resolutions. Fortunately, recent advancements in dasymetric mapping, which may not be well known to researchers utilizing Monte Carlo simulation in fields other than areal interpolation, provide novel methods for estimating the probability distributions of population counts. To highlight the potential link between dasymetric mapping and Monte Carlo simulation, we propose a framework that uses the penalized maximum entropy dasymetric model (PMEDM) proposed by Nagle et. al (2014) to learn the parameters of multinomial distributions describing population counts needed to complete a specific class of Monte Carlo simulations. 2. Methodology Suppose we’d like to calculate, through Monte Carlo simulation, the sample mean � � and sample standard deviation � � � of an output variable � � = � � ( � � , � � ) for a set of non-overlapping regions � ∈ {1, … , �} where � � = [� �1,…, � �� ] and � � = [� �1,…, � �� ] are vectors of random variables with

Dasymetric Mapping as a Tool to Assess the Spatial Distribution of Population in Jeddah City (Kingdom of Saudi Arabia)

It is well-known that, when dealing with density of population, most of the proposed maps choose the easiest and probably the most understandable cartographic method, i.e. the choropleth method. Nevertheless, for heterogonous spaces and those observing intense spatial dynamic, it is proven that this method has many lacks and deficiencies. This is the case of Jeddah city (the second largest city in Saudi Arabia), which is a very contrasted urban place with regards to its social structure, spatial organization and land use besides the fact that it witnesses a profound and continuous urban growth. Yet, most of the planning decisions are often taken on these types of maps and may mislead the urban planners. In this context, the dasymetric maps reveal very useful because they may give the real distribution of the population. Therefore, we think that establishing a dasymetric map at a convenient scale with regards to the results of satellite image processing may help the planners and the geographers as well as the common users. Indeed, this method may be an interesting alternative to the classic choropleth map. First it may improve our estimations towards the density within the various areas of the districts. Second it may refine the original enumeration units often using the administrative apportionment and therefore help the planning and agricultural agencies when establishing their base maps. The satellite image processing and GIS were used as tools in this study.

Calculation of a nighttime-population map based on free data

When the first authoritative data was released, free access was given to registries like the building registry (BBR). However, statistical data like the nighttime-population is still not free. This analysis shows how we can use dasymetric mapping to dis-aggregate municipality population data into BBR housing units, producing an estimated nighttime-population map. The results produced show that the proposed model has a general overrepresentation of the population, but despite that, this free dataset calculated here, should be usable in many cases where an estimate is adequate.

Spatializing Global Urban Extent: A Source Driven Approach

GIScience 2016 Short Paper Proceedings Spatializing Global Urban Extent: A Source Driven Approach J. J. Moehl 1 , A. N. Rose 1 , E. A. Bright 1 Oak Ridge National Laboratory, Oak Ridge, TN 37831 Email: moehljj;rosean;brightea@ornl.gov Abstract “Urban” is something that intuitively feels very well defined; however, when it comes time to express this idea on a map, things get complicated. Definitions of “urban” vary globally, and as such there is not universal understanding of what makes a given place “urban”. The common approach to spatially defining urban extents is through remotely sensed imagery. The alternative approach presented in this paper uses the percent of population in urban areas, which is a common macroeconomic (country-level) variable with the definition for urban generally defined by each country’s statistical office, along with temporally-aligned high- resolution population data to spatially define urban extents for each country. Because the percent urban number is defined by the same producer as other urban/rural defined statistical data, such as household characteristics or birth rate, understanding the spatial aspect of urban from the same perspective is ideal for high resolution spatial modeling of these other phenomena. 1. Introduction At Oak Ridge National Laboratory, we’ve been modeling population for nearly two decades. Whether using a top down approach such as the LandScan Global model (Dobson et al. 2000), or a hybrid top down/bottom up approach as is used to develop LandScan USA (Bhaduri et al. 2007), spatial data that aligns with tabular datasets is essential. Ensuring that national and subnational boundaries match the data from statistical organizations is extremely important for dasymetric mapping, as these boundaries play a large role in appropriately disaggregating zonal summaries. Many summary data values, such as population counts, birth rates, average household size, and age distributions are listed not only by administrative zone, but often times they are further listed by whether they occur in urban or rural areas. Spatial data for administrative boundaries are widely available, although of varying quality and level of detail. However, very rarely are spatial data available with urban area delineation from the perspective of the statistical organization. Summary data designated as urban or rural for each administrative zone within the country is common. The idea of what is “urban” is defined and implemented by each country’s statistical office. The UN’s World Urbanization Prospects: The 2014 Revision provides an exhaustive comparison of these definitions which vary widely in their considerations (UN 2014). This may be an extremely detailed formal definition like the one provided by the US Census Bureau (US Census 2011) complete with spatial data freely available in multiple formats, or very generally defined with no publicly accessible spatial data. Administrative data and population density are the most common criteria used by countries to fit the urban definition, with 125 and 137 countries respectively using each criterion in part or whole (UN 2014). The HYDE datasets, which provide historical population and land use estimates, combine historical urban population estimates with population density in their models; however, these datasets are historical and at a coarser ~10 sq km resolution (Klein Goldewijk 2001, 2005).

Building Consistent Multi-temporal Population Data at Fine Resolution through Spatially Refined Areal Interpolation

GIScience 2016 Short Paper Proceedings Building Consistent Multi-temporal Population Data at Fine Resolution through Spatially Refined Areal Interpolation H. Zoraghein 1 , S. Leyk 1 Department of Geography, University of Colorado Boulder, CO, 80309, USA Email: {hamidreza.zoraghein; stefan.leyk}@colorado.edu Abstract Demographic data are aggregated over areal units to protect privacy and are often inconsistent over time. Areal interpolation methods are used to estimate population in one census year within the units of another year to construct temporally consistent small census units. This research enhances these methods by using three advanced spatial refinement approaches, tested in Mecklenburg County, North Carolina to estimate population in 2000 within census tracts from the 2010 census. The results demonstrate the effectiveness of spatial refinement in reducing estimation errors, systematically. The proposed methods can be used to analyze micro-scale spatio-temporal demographic processes with minimum estimation error. 1. Introduction Spatial analysis on demographic data aggregated over incompatible boundaries represents a challenge, particularly when the data were collected over historical inconsistent units. To understand micro-scale spatio-temporal demographic processes, data need to be collected over temporally consistent fine-resolution census geographies such as census tracts. However, in reality, their boundaries change over time due to population fluctuations, especially in rapidly growing areas. Areal interpolation transfers the variable of interest from source zones to target zones and is used in temporal demographic applications (Gregory 2002; Schroeder 2007). In such applications, source populations in one census year (enumerated in source zones) are estimated within enumeration units from the target census completed in another year (target zones). If the underlying assumptions of areal interpolation methods are not met, accuracy can be very low. Therefore, recent studies have developed spatially refined interpolation techniques with the objective of decreasing population estimation errors (Buttenfield et al. 2015; Ruther et al. 2015). Areal interpolation methods are based on population density and area calculations. It can be expected that if these methods are constrained to spatially refined inhabited sub-areas of source and target zones, area and population density estimates will be more precise and realistic. Commonly, the spatially refined sub-areas are delineated using ancillary variables presumably related to population distribution in a dasymetric mapping approach (e.g., Mennis 2003). This research extends the previous efforts, leveraging three advanced spatial refinement strategies to estimate total population enumerated in census tracts in 2000 within census tract boundaries used in the 2010 census. 2. Study Area and Data The study area spans Mecklenburg County, North Carolina. It includes both urban areas of Charlotte at its center and large rural areas at its margins and has a history of rapid population growth.

Assessing the need for evacuation assistance in the 100 year floodplain of South Florida

The main objective of this study is to assess evacuation assistance need in the 100 year floodplain of South Florida (Palm Beach, Broward and Miami-Dade counties) by examining select population characteristics of the floodplain inhabitants. Dasymetric mapping is used to redistribute block group level census data to homogeneous inhabited zones of 30 m × 30 m. Because the 100 year floodplain does not correspond to block group boundaries, this data redistribution increases the resolution and accuracy of the floodplain population and their social characteristics. Data on poverty, age, vehicle ownership and mobile housing units are obtained for each 30 m × 30 m zone in the floodplain and is aggregated to the block group level. It is then used to assess evacuation assistance need (based on volume of need as well as concentration of need) for the floodplain in each block group. Results reveal variations in evacuation need across the floodplain. “Age” is the main driver of evacuation need along the coast. “Poverty” is a factor inland, in both urban and rural areas. “Lack of vehicle ownership” contributes to assistance need in coastal and inland urban areas, but not so much in rural areas. “Mobile housing” is a factor in rural areas. Miami-Dade County has higher volume and concentration of poor households lacking vehicular ownership. Palm Beach and Broward counties, on the other hand, have a more dominant presence of elderly and of mobile housing. These results have important implications for local and regional evacuation planning in the event of a 100 year flood.