The Professional Geographer

The variations of breast cancer mortality rates from place to place reflect both underlying differences in breast cancer prevalence and differences in diagnosis and treatment that affect the risk of death. This article examines the role of access to health care in explaining the variation of late-stage diagnosis of breast cancer. We use cancer registry data for the state of Illinois by zip code to investigate spatial variation in late diagnosis. Geographic information systems and spatial analysis methods are used to create detailed measures of spatial access to health care such as convenience of visiting primary care physicians and travel time from the nearest mammography facility. The effects of spatial access, in combination with the influences of socioeconomic factors, on late-stage breast cancer diagnosis are assessed using statistical methods. The results suggest that for breast cancer, poor geographical access to primary health care significantly increases the risk of late diagnosis for persons living outside the city of Chicago. Disadvantaged population groups including those with low income and racial and ethnic minorities tend to experience high rates of late diagnosis. In Illinois, poor spatial access to primary health care is more strongly associated with late diagnosis than is spatial access to mammography. This suggests the importance of primary care physicians as gatekeepers in early breast cancer detection.

This article uses accessibility as an analytical tool to examine health care access among immigrants in a multicultural urban setting. It applies and improves on two widely used accessibility models—the gravity model and the two-step floating catchment area model—in measuring spatial accessibility by Mainland Chinese immigrants in the Toronto Census Metropolitan Area. Empirical data on physician-seeking behaviors are collected through two rounds of questionnaire surveys. Attention is focused on journey to physician location and utilization of linguistically matched family physicians. Based on the survey data, a two-zone accessibility model is developed by relaxing the travel threshold and distance impedance parameters that are traditionally treated as a constant in the accessibility models. General linear models are used to identify relationships among spatial accessibility, geography, and socioeconomic characteristics of Mainland Chinese immigrants. The results suggest a spatial mismatch in the supply of and demand for culturally sensitive care, and residential location is the primary factor that determines spatial accessibility to family physicians. The article yields important policy implications.

The objectives are to (1) quantify, map, and analyze vegetation cover distributions and changes across Accra, Ghana, for 2002 and 2010; and (2) examine the statistical relationship between vegetation cover and a housing quality index (HQI) for 2000 at the neighborhood level. Pixel-level vegetation cover maps derived using threshold classification of 2002 and 2010 QuickBird normalized difference vegetation index images have very high overall accuracies and yield an estimate of 5.9 percent vegetation cover reduction over the study area between 2002 and 2010. A high degree of variance in vegetation cover for individual dates is explained by HQI at the neighborhood level, although minimal covariability between absolute or relative vegetation cover change and HQI for 2000 was observed.

Obesity is a growing epidemic in the United States. Walkable neighborhoods, characterized as having the 3Ds of walkability (population Density, land use Diversity, and pedestrian-friendly Design), have been identified as a potentially promising factor to prevent obesity for their residents. Past studies examining the relationship between obesity and walkability vary in geographic scales of neighborhood definitions and methods of measuring the 3Ds. To better understand potential influences of these sometimes arbitrary choices, we test how four types of alternative measures of land use diversity measured at three geographic scales relate to body mass index for 4960 Salt Lake County adults. Generalized estimation equation models demonstrate that optimal diversity measures differed by gender and geographic scale and that integrating walkability measures at different scales improved the overall performance of models.

Edward Jarvis in 1850 first demonstrated that admission rates to mental hospitals decrease with increasing residential distance, a relationship known today as “Jarvis's Law.” His original data are presented, mapped, and examined by regression analysis to better understand spatial and temporal patterns of mid-19th century mental hospital utilization. Distance substantially affected admission rates to a radius of about 60 miles from the institution in Massachusetts; and there was strong distance decay in the other states examined. For all twelve states, there was a positive association between age of the hospitals and admission rates, which also decreased with increasing residential distance.

Many geographic studies use distance as a simple measure of accessibility, risk, or disparity. Straight-line (Euclidean) distance is most often used because of the ease of its calculation. Actual travel distance over a road network is a superior alternative, although historically an expensive and labor-intensive undertaking. This is no longer true, as travel distance and travel time can be calculated directly from commercial Web sites, without the need to own or purchase specialized geographic information system software or street files. Taking advantage of this feature, we compare straight-line and travel distance and travel time to community hospitals from a representative sample of more than 66,000 locations in the fifty states of the United States, the District of Columbia, and Puerto Rico. The measures are very highly correlated (r (2) > 0.9), but important local exceptions can be found near shorelines and other physical barriers. We conclude that for nonemergency travel to hospitals, the added precision offered by the substitution of travel distance, travel time, or both for straight-line distance is largely inconsequential.

There is an increasing need for a quick, simple method to represent diurnal population change in metropolitan areas for effective emergency management and risk analysis. Many geographic studies rely on decennial U.S. Census data that assume that urban populations are static in space and time. This has obvious limitations in the context of dynamic geographic problems. The U.S. Department of Transportation publishes population data at the transportation analysis zone level in fifteen-minute increments. This level of spatial and temporal detail allows for improved dynamic population modeling. This article presents a methodology for visualizing and analyzing diurnal population change for metropolitan areas based on this readily available data. Areal interpolation within a geographic information system is used to create twenty-four (one per hour) population surfaces for the larger metropolitan area of Salt Lake County, Utah. The resulting surfaces represent diurnal population change for an average workday and are easily combined to produce an animation that illustrates population dynamics throughout the day. A case study of using the method to visualize population distributions in an emergency management context is provided using two scenarios: a chemical release and a dirty bomb in Salt Lake County. This methodology can be used to address a wide variety of problems in emergency management.

Geographic space demonstrates scaling or hierarchy, implying that there are far more small things than large ones. The scaling pattern of geographic space, if visualized properly (i.e., based on head/tail breaks), can evoke a sense of beauty. This is our central argument. This beauty is a new type of aesthetic at a deeper structural level, and differs in essence from an intuitive sense of harmony, perceived in terms of color, shape, texture, and ratio. This new kind of beauty was initially defined and discovered by Christopher Alexander, and promoted in his master work The Nature of Order. To paraphrase Mandelbrot, this is the beauty for the sake of science rather than for art's sake or for the sake of commerce. Throughout the paper, we attempt to argue and illustrate that the scaling of geographic space possesses this new kind of beauty, which has a positive impact on human well-being. The paper further draws upon the previous work of Nikos Salingaros and Richard Taylor on the beauty in architecture and arts to support our argument. Keywords: Aesthetics, heavy-tailed distributions, head/tail breaks, Zipf's law, and fractals

This paper introduces a new classification scheme - head/tail breaks - in order to find groupings or hierarchy for data with a heavy-tailed distribution. The heavy-tailed distributions are heavily right skewed, with a minority of large values in the head and a majority of small values in the tail, commonly characterized by a power law, a lognormal or an exponential function. For example, a country's population is often distributed in such a heavy-tailed manner, with a minority of people (e.g., 20 percent) in the countryside and the vast majority (e.g., 80 percent) in urban areas. This heavy-tailed distribution is also called scaling, hierarchy or scaling hierarchy. This new classification scheme partitions all of the data values around the mean into two parts and continues the process iteratively for the values (above the mean) in the head until the head part values are no longer heavy-tailed distributed. Thus, the number of classes and the class intervals are both naturally determined. We therefore claim that the new classification scheme is more natural than the natural breaks in finding the groupings or hierarchy for data with a heavy-tailed distribution. We demonstrate the advantages of the head/tail breaks method over Jenks' natural breaks in capturing the underlying hierarchy of the data. Keywords: data classification, head/tail division rule, natural breaks, scaling, and hierarchy

A comprehensive approach to the analysis of point patterns demonstrates the usefulness of second-order methods by exploring population distribution in the Chicago region. The methods are based on the development of a distribution of all interpoint distances representing the total covariation in a pattern. Clustering and inhibition models are explored with regard to the population pattern. Some evidence supports a multi-center city hypothesis for the region. © The Association of American Geographers. All rights reserved.

This paper examines the former location-based social medium Brightkite, over its three-year life span, based on the concept of natural cities. The term 'natural cities' refers to spatially clustered geographic events, such as the agglomerated patches aggregated from individual social media users' locations. We applied the head/tail division rule to derive natural cities. More specifically, we generated a triangulated irregular network, made up of individual unique user locations, and then categorized small triangles (smaller than an average size) as natural cities for the United States (mainland) on a monthly basis. The concept of natural cities provides a powerful means to develop new insights into the evolution of real cities, because there are virtually no data available to track the history of a city across its entire life span and at very fine spatial and temporal scales. Therefore, natural cities can act as a good proxy of real cities, in the sense of understanding underlying interactions, at a global level, rather than of predicting cities, at an individual level. Apart from the data produced and the contributed methods, we established new insights into the structure and dynamics of natural cities, e.g., the idea that natural cities evolve in nonlinear manners at both spatial and temporal dimensions. Keywords: Big data, head/tail breaks, ht-index, power laws, fractal, and nonlinearity

The analysis of clusters has attracted considerable interest over the last few decades. The articulation of clusters into complex networks and systems of innovation – generally known as regional innovation systems – has, in particular, been associated with the delivery of greater innovation and growth. However, despite the growing economic and policy relevance of clusters, little systematic research has been conducted into their association with other factors promoting innovation and economic growth. This paper addresses this issue by looking at the relationship between innovation and economic growth in 152 regions of Europe during the period between 1995 and 2006. Using an econometric model with a static and a dynamic dimension, the results of the analysis highlight that: a) regional growth through innovation in Europe is fundamentally connected to the presence of an adequate socioeconomic environment and, in particular, to the existence of a well-trained and educated pool of workers; b) the presence of clusters matters for regional growth, but only in combination with a good ‘social filter’, and this association wanes in time; c) more traditional R&D variables have a weak initial connection to economic development, but this connection increases over time and, is, once again, contingent on the existence of adequate socioeconomic conditions.

The movements of ideas and content between locations and languages are unquestionably crucial concerns to researchers of the information age, and Twitter has emerged as a central, global platform on which hundreds of millions of people share knowledge and information. A variety of research has attempted to harvest locational and linguistic metadata from tweets in order to understand important questions related to the 300 million tweets that flow through the platform each day. However, much of this work is carried out with only limited understandings of how best to work with the spatial and linguistic contexts in which the information was produced. Furthermore, standard, well-accepted practices have yet to emerge. As such, this paper studies the reliability of key methods used to determine language and location of content in Twitter. It compares three automated language identification packages to Twitter's user interface language setting and to a human coding of languages in order to identify common sources of disagreement. The paper also demonstrates that in many cases user-entered profile locations differ from the physical locations users are actually tweeting from. As such, these open-ended, user-generated, profile locations cannot be used as useful proxies for the physical locations from which information is published to Twitter.

This paper explores the application of geographic information systems (GIS) in the evaluation of the accuracy of early maps through a case study of The Map of the Prefectural Capital of 1261. The evaluation of the accuracy of early maps is an important aspect of the study of the history of cartography, but no standard methodology has been generally accepted. The purpose of this paper is to assess the positional accuracy and the relative relations of the spatial objects on The Map of the Prefectural Capital using GIS. The procedure of the study includes identification of locations of the points and features of The Map of the Prefectural Capital on a modern base map, digitization of the early map and the modern base map, overlays of the digitized early map and modern base map, and an analysis of the absolute and relative distortion of the early map. The results of the analysis show that The Map of the Prefectural Capital of 1261 is reasonably accurate considering the technical ability of the thirteenth century, although it contains a considerable amount of positional displacement. In contrast to the amount of positional displacement, the relative relations among the objects are depicted much more precisely.

This article provides an analysis of a wetland site in southern Illinois from presettlement to the present. The study area is part of the Cache River-Cypress Creek Wetland, which has international importance, as recognized by the Ramsar Convention on Wetlands. Land-cover data for 1807, 1938, and 1993 were created and analyzed with a geographic information system (GIS). Land-use change by topographic setting (uplands, transitional, and bottomlands) and soil productivity was quantified and studied. Interviews with local experts informed this analysis. Results illustrate the complexity of environmental change and its driving forces. First, notable forest and swamp acreage was converted to cropland between 1807 and 1938 and, to a lesser degree, from 1938 to 1993. Second, there were land-use variations by topographic region. Between 1807 and 1938, the largest transformation occurred in the uplands, with substantial acreage converted from forest to cropland. Between 1938 and 1993, however, agriculture decreased in the upland areas as hilly areas reverted to forest cover. At the same time, agriculture expanded in the bottomlands as this land was drained for farming. Third, there are interesting patterns within these categories of land-use change, as soil productivity is an indicator of what lands were taken out of cropland and converted back to grassland and forest.