Capturing the two dimensions of residential segregation at the neighborhood level for health research

Two conceptual and methodological foundations of segregation studies are that (i) segregation involves more than one group, and (ii) segregation measures need to quantify how different population groups are distributed across space. Therefore, percentage of population belonging to a group is not an appropriate measure of segregation because it does not describe how populations are spread across different areal units or neighborhoods. In principle, evenness and isolation are the two distinct dimensions of segregation that capture the spatial patterns of population groups. To portray people’s daily environment more accurately, segregation measures need to account for the spatial relationships between areal units and to reflect the situations at the neighborhood scale. For these reasons, the use of local spatial entropy-based diversity index (SHi) and local spatial isolation index (Si) to capture the evenness and isolation dimensions of segregation, respectively, are preferable. However, these two local spatial segregation indexes have rarely been incorporated into health research. Rather ineffective and insufficient segregation measures have been used in previous studies. Hence, this paper empirically demonstrates how the two measures can reflect the two distinct dimensions of segregation at the neighborhood level, and argues conceptually and set the stage for their future use to effectively and meaningfully examine the relationships between residential segregation and health.published_or_final_versio

Mapping American community survey data

Maps are a frequently used tool to portray the Census Bureau’s data and highlight spatial patterns that provide context and significance for the characteristics displayed. Maps provide visually what tables and other graphics cannot: a picture of the data, their distribution over geographic areas, and a means for interpreting the data shown by color, symbology, or explanation provided as annotations or as part of the map legend. The value of maps in enhancing an understanding of census data is well established as demonstrated by their frequent use in the media following the release of census data products. Mapping census data is common throughout government, academia, and the private sector. Casual users of maps of statistical data may not look past what is interesting visually to analyze the underlying data that a map depicts. However, that does not absolve the mapmaker of the responsibility for informing users of the statistical qualities associated with the mapped values. The Census Bureau set new standards for communicating the statistical qualities of estimates from the American Community Survey (ACS) by including information on the level of sampling error (specifically, margins of error) associated with every ACS estimate. Now, efforts are underway to develop an operational tool that will make it possible for geographic information systems (GIS) users to communicate this information through map products as well.published_or_final_versio

Fast and robust generation of city scale urban ground plan

Since the introduction of the concept of Digital Earth, almost every major international city has been re-constructed in the virtual world. A large volume of geometric models describing urban objects has become freely available in public domain via software like Google Earth. Although mostly created for visualization, these urban models can benefit many applications beyond visualization including video games, city scale evacuation plan, traffic simulation and earth phenomenon simulations. However, these urban models are mostly loosely structured and implicitly defined and require tedious manual preparation that usually take weeks if not months before they can be used. In this paper, we present a framework that produces well-defined ground plans from these urban models, an important step in the preparation process. Designing algorithms that can robustly and efficiently handle unstructured urban models at city scale is the main technical challenge. In this work, we show both theoretically and empirically that our method is resolution complete, efficient and numerically stable. Based on our review of the related work, we believe this is the first work that attempts to create urban ground plans automatically from 3D architectural meshes at city level. With the goal of providing greater benefit beyond visualization from this large volume of urban models, our initial results are encouraging.published_or_final_versio

Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

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A Semi-analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China

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Spatial measures of segregation and GIS

Traditional segregation measures have limitations in discerning different patterns of population distributions. Spatial measures of segregation have been introduced, but have not been widely adopted partly because of the difficulties in using them. A recent effort is to implement several spatial segregation measures as additional GIS tools in a popular desktop GIS package so that researchers and practitioners not savvy in GIS can use these tools to compute spatial segregation indices. This paper provides a concise review of these measures and elaborates the new tools developed.link_to_subscribed_fulltex

Desktop and internet GIS development for Spatial Segregation Analysis

http://proceedings.esri.com/library/userconf%20/proc01/professional/papers/pap275/p275.htmSeveral geographical measures of segregation have been introduced in the past decade. Because they require certain types of spatial information in their formulations, it is natural to incorporate these spatial measures in a GIS environment. This paper documents an effort in using Avenue scripts in ArcView to implement a set of spatial segregation measures. The results of this development (project files) are available for researchers and practitioners. In addition, segregation is often a concern of local communities. Given the wide-spread access of Internet, tools and data for analyzing spatial segregation are made accessible to the public through Internet GIS.link_to_OA_fulltex

Conceptual and operational issues in incorporating segregation measurements in hedonic price modeling

The essence of hedonic price modeling is to establish the relationship between housing prices and housing attributes. Typically, housing attributes refer to structural characteristics of the unit. However, it is obvious that the price of a house is not just determined by its structural attributes, but also attributes of the neighborhood in which the unit is located. Neighborhood attributes can be physical properties of the neighborhood, such as street condition and proximity of employment centers, or environmental characteristics such as the types of vegetative cover. Another set of neighborhood attributes is associated with the demographic and socioeconomic characteristics of the residents. The intensity and nature of interaction between the population and the physical environment can also be regarded as neighborhood characteristics. The overall objective of this chapter is to evaluate how segregation is relevant in housing price determination and to suggest effective segregation measures that can be incorporated into housing price modeling. In other words, this chapter intends to provide insights on capturing neighborhood population characteristics as inputs to hedonic models. The objective is accomplished through the discussions of different facets of segregation at the conceptual level and various issues in using segregation measures at the operational level. I will first offer taxonomies of segregation based upon several defining dimensions. Then I will discuss the concepts of segregation in residential space in reference to housing price determination. Segregation is generally regarded as undesirable, but specific impacts (positive or negative) of segregation on a neighborhood and the processes have not been concretely addressed. There are also many types of segregation and only those that are relevant to housing price modeling will be discussed. Then I will address several segregation measurement issues that are relevant to housing price modeling in general. The issue of geographical scale and the nature of segregation will be the major emphases. Some measures appropriate for hedonic modeling will be reviewed.link_to_subscribed_fulltex