II Environmental Planning: Technical Principles and Analysis
5 Environmental Data and Geospatial Analysis
This chapter begins Part II
, which focuses on the scientific principles and technical analysis involved in environmental land use planning and management. Environmental science and engineering principles and data analysis are critical to the methods presented in the chapters on soils, geology, hydrology, ecology, natural hazards, and climate change (Chapters 6
). Chapter 14
addresses the integration of environmental and spatial information, including measurable indicators that clarify and compare community sustainability and track change and progress.
First we must introduce fundamental concepts, sources, and analysis of information, especially geospatial data. This chapter presents some general considerations in information gathering and data analysis and then reviews the exploding field of geospatial analysis, remote sensing, and geographic information systems: the geospatial revolution. We begin with a vision statement presented by then Vice President Al Gore in his now famous January 1998 speech on “The Digital Earth” at the California Science Center in Los Angeles, a time when few conceived of even Google Earth, which was launched 7 years later. Our current geospatial revolution has progressed far closer to Al Gore’s vision of the Digital Earth than anyone would have predicted in 1998.
Imagine a young child going to a Digital Earth exhibit at a local museum. After donning a head-mounted display, she sees Earth as it appears from space. Using a data glove, she zooms in, using higher and higher levels of resolution, to see continents, then regions, countries, cities, and finally individual houses, trees, and other natural and man-made objects. Having found an area of the planet she is interested in exploring, she takes the equivalent of a “magic carpet ride” through a 3-D visualization of the terrain. Of course, terrain is only one of the many kinds of data with which she can interact. Using the systems’ voice recognition capabilities, she is able to request information on land cover, distribution of plant and animal species, real-time weather, roads, political boundaries, and population. She can also visualize the environmental information that she and other students all over
the world have collected as part of the GLOBE project. This information can be seamlessly fused with the digital map or terrain data. She can get more information on many of the objects she sees by using her data glove to click on a hyperlink. To prepare for her family’s vacation to Yellowstone National Park, for example, she plans the perfect hike to the geysers, bison, and bighorn sheep that she has just read about. In fact, she can follow the trail visually from start to finish before she ever leaves the museum in her hometown.
She is not limited to moving through space, but can also travel through time. After taking a virtual field-trip to Paris to visit the Louvre, she moves backward in time to learn about French history, perusing digitized maps overlaid on the surface of the Digital Earth, newsreel footage, oral history, newspapers and other primary sources. She sends some of this information to her personal e-mail address to study later. The time-line, which stretches off in the distance, can be set for days, years, centuries, or even geological epochs, for those occasions when she wants to learn more about dinosaurs [or forward in time to view scenarios for those occasions when she wants to see what the future may hold and what she can do about it]. (Gore 1998)
Information and Data Analysis in Environmental Planning
Pragmatic environmental problem solving requires science- and fact-based technical information on which to base decisions and actions. Environmental planners are generally the gatekeepers of information (as well as managers of misinformation), and they must be adept at gathering, analyzing, interpreting, integrating, and presenting information. In a land use context, much of this information is spatial and is best represented in maps and spatial images.
The integration of spatial, scientific, engineering, and economic information with normative perceptions and values is challenging because of the wide range of both quantitative and qualitative information. The planning process determines the type and specificity of information needed. As shown in Box 2.1
, Scoping (Step 0) identifies data needs and develops a work plan for collecting and analyzing data. Analysis (Step 2) focuses on information gathering and analysis. This activity continues throughout the planning process.
A Tiered Process
Information gathering and assessment often follow a tiered process, first by looking at readily available and general information, followed by increasing levels of detail (Table 5.1
). Many studies start with rapid assessment,
which takes a quick look at problems and available information and moves quickly to initial action (Sayre et al. 2000). Although moving to action quickly has advantages, rapid assessment should also identify needs for more detailed analysis to follow. This is sometimes referred to as data gap analysis,
or the identification of data gaps in need of filling.
Intermediate and advanced assessment involves increasing levels of detail, more analysis, and more sophisticated data products. The first step is gathering basic data, such as maps or remotely sensed information on topography, soils, geology, and land use/land cover. This inventory may also include more specific information on wetlands, habitats, and culturally significant areas acquired from field monitoring or the local knowledge of citizens.
TABLE 5.1 Tiered Approach to Information Gathering and Analysis
Analytical studies prioritize and interpret the data, and generally aim to make sense of the information. Specific data and mapped products are determined by the planning objectives. For example, assessing environmentally sensitive and critical areas may require information on land use, land ownership, development infrastructure, population growth, and other factors influencing land use change. Methods such as build-out and environmental impact assessment can clarify possible future effects.
Subsequent chapters describe specific data gathering, analysis, and display methods used for planning studies involving soils (Chapter 6
), stormwater quantity and quality (Chapters 7
), groundwater (Chapter 9
), watersheds (Chapters 7
), landscape and urban ecology (Chapters 10
), urban forestry (Chapter 10
), wetlands (Chapter 10
), wildlife habitat (Chapter 11
), energy and climate change (Chapter 12
), natural hazards (Chapter 13
), and land suitability (Chapter 14
Considerations and Pitfalls in Using Data and Information
The proper use, the accuracy, and the documentation of land-related information depend on several data issues that should be considered throughout the planning and analysis processes, especially in the early stages. These issues include data form, scale, accuracy, coverage, completeness, age, confidentiality, maintenance, paper-trail-to-sources, communication, and appropriateness (Hirschman et al. 1992).
1. Form. Are the data digital (e.g., georeferenced in a database), spatial (e.g., on a map), temporal (e.g., plotted on a graph with a time dimension), or a combination of these? Are data qualitative (e.g., groundwater moves rapidly) or quantitative (e.g., flow is 50 ft per day)?
How large or small is the mapped representation of a given land area? If two maps are the same size, the large-scale map will represent less land than the small-scale map. Accordingly, the large-scale map is more detailed. This is important when overlaying maps of different scale.
How well do the data and/or the mapped locations of features reflect their actual existence or location on the land surface (or how much “slop” is there in the mapped representation)? For example, map units in a soil survey (see Chapter 6
) are commonly accurate to 2 acres. Differing accuracies of data sets become important when comparing or overlaying information. Note that accuracy is the degree of agreement between sample or map data and reality, and precision is how well you can reproduce the data values that you measure, monitor, or map.