School of Geographical Sciences and Urban Planning

Cavernous rock decay processes represent a global phenomenon, ubiquitous to all environments, with the viewable-in-landscape form usually being the final descriptor (e.g. ‘‘alveoli’’), sometimes alluding to the specific decay process (e.g. ‘‘pitting’’), other times not (e.g. ‘‘honeycombing’’). Yet, definitive terminology remains inconsistent, usually owing to variability in dimension, morphometry, distribution, and/or academic lineage. This lack of an established lexicon limits scientific collaboration and can generate scientific bias. With no official consensus on appropriate distinctions, researchers and scientists must either be familiar with all the possible terminology, or know the apparent distinction between ‘‘forms’’—which can seem arbitrary and, even more frustrating, often differs from researcher to researcher, scientist to scientist. This article reviews the historical and contemporary progression of scientific inquiry into this decay—and, arguably, erosional— feature to identify lexical inconsistencies and promote a singular unifying term for future scholars. Ultimately, the authors support using ‘‘tafoni’’ (singular: ‘‘tafone’’) as the non-scalar universal term—the form created by numerous processes involved in cavernous decay features—and strongly suggest researchers adopt the same vernacular in order to promote collaboration.

A recent paper expands the well-known geographically weighted regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model---a framework referred to as multiscale GWR (MGWR). However, one limitation of the MGWR framework is that, until now, no inference about the local parameter estimates was possible. Formally, the so-called ``hat matrix,'' which projects the observed response vector into the predicted response vector, was available in GWR but not in MGWR. This paper addresses this limitation by reframing GWR as a Generalized Additive Model, extending this framework to MGWR and then deriving standard errors for the local parameters in MGWR. In addition, we also demonstrate how the effective number of parameters can be obtained for the overall fit of an MGWR model and for each of the covariates within the model. This statistic is essential for comparing model fit between MGWR, GWR, and traditional global models, as well as for adjusting multiple hypothesis tests. We demonstrate these advances to the MGWR framework with both a simulated data set and a real-world data set and provide a link to new software for MGWR (MGWR1.0) which includes the novel inferential framework for MGWR described here.

Discrete Markov chain models (DMCs) have been widely applied to the study of regional income distribution dynamics and convergence. This popularity reflects the rich body of DMC theory on the one hand and the ability of this framework to provide insights on the internal and external properties of regional income distribution dynamics on the other. In this paper we examine the properties of tests for spatial effects in DMC models of regional distribution dynamics. We do so through a series of Monte Carlo simulations designed to examine the size, power and robustness of tests for spatial heterogeneity and spatial dependence in transitional dynamics. This requires that we specify a data generating process for not only the null, but also alternatives when spatial heterogeneity or spatial dependence is present in the transitional dynamics. We are not aware of any work which has examined these types of data generating processes in the spatial distribution dynamics literature. Results indicate that tests for spatial heterogeneity and spatial dependence display good power for the presence of spatial effects. However, tests for spatial heterogeneity are not robust to the presence of strong spatial dependence, while tests for spatial dependence are sensitive to the spatial configuration of heterogeneity. When the spatial configuration can be considered random, dependence tests are robust to the dynamic spatial heterogeneity, but not so to the process mean heterogeneity when the difference in process means is large relative to the variance of the time series.

Abstract Stochastic dominance tests are used to measure whether distributions are directionally-distinct. Using stochastic dominance measures, an income distribution can be measured to be more favorable for its members at all income levels than another distribution. This contrasts with conventional σ and β-convergence approaches that compare distributions at a set of predetermined income levels. Given this comprehensiveness, stochastic dominance measures can yield novel insight into the relationship between income distributions, and thus to the relative economic welfare of inhabitants of different societies. However, stochastic dominance measures used in the analysis of regional income convergence have ignored the complications of geography. In this paper we examine the properties of common tests for stochastic dominance using various descriptive models of geographically-realistic income growth processes. We find that the false positive rate of stochastic dominance tests increases significantly when comparing spatially-informed income distributions growing over time. Further, this over-eager rejection rate requires only that one distribution be spatially-dependent, unlike some other bivariate statistics. We demonstrate that both the parametric and non-parametric spatial filtering procedures are effective at resolving these issues in restricted circumstances for higher-order dominance tests. Overall, while providing a novel and useful perspective on income distribution dynamics, stochastic dominance measures are demonstrated to have undesirable properties when used in geographically-realistic scenarios and they resist one common method to address these issues. Solutions to the concerns expressed here remain open.

Spatial effects have been recognized to play an important role in transitional dynamics of regional incomes. Detection and evaluation of both spatial heterogeneity and spatial dependence in discrete Markov chain models, which have been widely applied to the study of regional income distribution dynamics and convergence, are vital, but under-explored issues. Indeed, in this spatiotemporal setting, spatial effects can take much more complex forms than that in a pure cross-sectional setting. In this paper, we address two test frameworks. The first is a conditional spatial Markov chains test framework, which can be used to detect spatial heterogeneity and temporally lagged spatial dependence; the second is a joint spatial Markov chains test framework, which tests for contemporaneous spatial dependence. A series of Monte Carlo experiments are designed to examine size, power and robustness properties of these tests for a range of sample sizes (spatial                                                                  {\$}{\$}{\backslash}times {\$}{\$}                                                      {\texttimes}                                                temporal dimensions), for different levels of discretization granularity and for different number of regimes. Results indicate that all tests display good size property except when sample size is fairly small. All tests for spatial dependence are similar in almost all aspects---size, power and robustness. Conditional spatial Markov tests for spatial heterogeneity have highest power for detecting spatial heterogeneity. Granularity of discretization has a major impact on the size properties of the tests when sample size is fairly small.

Across many cities in the early twentieth century, one in five children died before their fifth birthday. There is much we do not know about how infant and child mortality was reduced, nor why it declined at different rates across populations. This article investigates mortality using data from 13,247 families in Dublin City in the 1900s with a novel approach that incorporates geographic information systems, spatially-derived predictors and multilevel modelling. In the early twentieth century, Dublin had one of the highest early-age mortality rates in the British Empire. While experts attributed the death of young children to the unhygienic behaviors of indigenous Roman Catholics, others made claims of a social injustice rooted in economic inequality and the indifference of public authorities toward the health of the lower classes. This article finds that high Catholic mortality was mainly driven by poverty and the conditions engendered by residential segregation. Low mortality rates among Dublin’s small Jewish population are not easily explained by location or economic characteristics.

With over 30 million people moving to North America during the Age of Mass Migration (1850-1913), governments feared that Europe was losing its most talented workers. Using new data from Ireland in the early twentieth century, I provide evidence to the contrary, showing that the sons of farmers and illiterate men were more likely to emigrate than their literate and skilled counterparts. Emigration rates were highest in poorer farming communities with stronger migrant networks. I constructed these data using new name-based techniques to follow people over time and to measure chain migration from origin communities to the United States.

Proponents of restrictive immigration policies often claim that families arriving with fewer skills and resources will struggle economically. This claim is challenging to test as lower-skilled migrants also tend to face greater discrimination, exclusion, and obstacles in the United States. I use unique multigenerational data on Irish Americans in the early-twentieth century, before and after migration, to directly study how the economic origins of Irish families and the reception context they faced in the U.S. affected economic attainment in the second generation. This analysis finds weak associations between economic background in Ireland and second-generation earnings in the United States. The schooling context and ethnic communities of settlement locations, in contrast, have strong effects on second-generation outcomes. These findings indicate that the experiences of immigrant families in the United States may be more important for second-generation attainment than the skills and resources brought from the origin country in the immigrant generation.

Economic inequality is a major challenge the world is facing today. This situation is not different for Bangladesh. Unequal distribution of wealth and other facilities not only cause economic but also social discrepancy. Disparity among the regions can help to identify the privileged and deprived regions, so that guideline for developing the depressed regions can be formulated. This study aims to identify such inequalities among Rajshahi, Pabna and Sirajganj districts. For this study, the methodological tool used is Gini coefficient. The gross domestic product (GDP) for years 1995-96 and 1999-2000, distribution of utility facilities like water, sanitary and electricity for the year 2001 and 2011 have been used as variables to identify the disparity among the districts. The study reveals that for disparity in GDP distribution, calculated Gini index value expressing disparity is 0.044 in 1995-96 and 0.047 in 1999-2000 which is very low. Moreover, Gini index value for utility service facilities (e.g. water, sanitary, electricity) decreases from 2001 to 2011 which is a positive change and indicates that the inequality of service facilities among three districts is almost close to the equity. The study further recommends to broaden the scope of the research taking all 64 districts in order to draw a more comprehensive picture of disparity.
Key words: Gini coefficient, Lorenz curve, location quotient, disparity, GDP, service facilities.

Dhaka, the capital of Bangladesh is one of the most populated megacity in the world and the population growth in this city is extremely high. To support growing food demand of increasing population, food supply should be secure and sustainable. On the other hand, with the pace of urbanization built-up areas are increasing; hence supply of roof space is also increasing. Rooftop farming can provide solution to increased food demand and also can promote a sustainable and livable city. Local fresh and safe food can be ensured through roof gardens in Dhaka city. The aim of the study is to explore the present practice and challenges of rooftop farming that was encountered by practitioners. Mirpur and Mohammadpur areas have been selected as study areas. Two practitioners are interviewed and 60 non-practitioners are surveyed. Results show that rooftop farming can support environment by improving air quality, reducing carbon in the atmosphere and can benefit society by reducing storm water management cost. One of the significant findings from the non-practitioner survey is that maximum people are willing to practice rooftop farming and want to provide at least 50% of roof space for rooftop farming. Finally some recommendations have been suggested to improve rooftop farming practice and encourage more people to practice rooftop farming in future.