1. Patterns and Trends CE/ENVE 424/524

2. Classroom Situation Option 1: Stay in Lopata House 22 pros: spacious room desks with chairs built in projector cons: at the edge of campus Option 2: Move to Lopata Hall 301 pros:conveniently located cons:small room chair with attached small desk top no built-in projector

3. Environmental Pattern and Trend Analysis When analyzing environmental data we examine: We are particularly interested in changes in these patterns and trends and relationships with other patterns and trends The analysis also strives to determine why we see these patterns and trends – what are the casual factors and what are their impacts. Temporal Trends Spatial Patterns

4. Spatiotemporal Analysis

5. Spatial and Temporal Data Analysis Turns raw data into useful information –by adding greater informative content and value Wisdom Knowledge Evidence Information Data

6. Changing Role of Maps in Analysis

7. Spatial Data Analysis A method of analysis is spatial if the results depend on the locations of the objects or phenomena being analyzed - change the spatial configuration of your data or sampling and the analysis results change - results are not invariant under relocation Spatial analysis requires both attributes and locations of the environmental parameter of interest - about 85% of environmental data is spatial What is spatial analysis such a big deal?

8. Special Spatial Nomenclature Geographic – Limited to phenomena and problems relating to Earth’s surface and near-surface Spatial – Any space, including geographic, but not restricted to geographic coordinate space, e.g. medical imaging Geospatial – A recent term to represent the subset of spatial applied specificially to the Earth’s surface. (synonymous with geographic)

9. Types of Spatial Analysis There are literally thousands of techniques Bailey and Gatrell, 1995 offer four spatial data analysis classes: –Point Data Analysis Do the locations of point data and the relationship among the points represent a ‘significant’ pattern –Continuous Data Analysis What are the spatial pattern and characteristics over a region given a set of samples –Area Data Analysis Analysis of data that have been aggregated over a spatial area –Modeling Spatial Interaction Data Analysis of flow patterns

10. The Snow Map A classic example of the use of location to draw inferences 1854 cholera outbreak in London Point data map indicated some spatial clustering Overlaying a map of water pump locations showed many cases were concentrated around a single pump

11. Legionare’s Disease Statistical point data analysis indicated significant clustering in Legionaire’s disease cases Are outbreaks of Legionare’s disease sporadic (random) or linked in space and time?

12. Temperature Data Point Data Analysis High spatial density and coverage High temporal coverage (every few minutes)

13. Temperature Data Continuous Data Analysis Temperature data is well suited for converting from point to continuous data - It has high spatial density - Ambient temperature is relatively spatially homogenous (no sharp gradients)

14. Tropospheric Ozone Continuous Data Analysis Ozone concentrations are not as homogenous as temperature and display “hot spots” The data can still be converted from point to continuous form but requires more advanced methods

15. Temperature Data Modeling / Forecasting

16. Temperature Data Network Design Network design analysis answers questions related to the spatial configuration of a monitoring network - Where are new monitors needed? - Are some monitors redundant and/or unnecessary?

17. County Level Data Area Data Also known as chloropleth plot

18. Spatial Analysis Types Summary Point Data Analysis Consists of discrete ‘events’ in space and/or time, e.g. analysis of volcanic eruptions Continuous Data Analysis A continuous parameter exists over an area but is known (measured) at select locations Area Data Analysis Area data is aggregated or filtered for a collection of spatial zones, e.g. census blocks Modeling Spatial Interaction Data Emphasizes relationships among parameters to better understand the spatial pattern Note: A data set can be analyzed using more than one of the above analysis classes

19. Scale The most appropriate analysis method to use depends on the spatial and temporal scales of the problem. The spatial variability of temperature at a ‘local’ scale is not necessarily significant when conducting an analysis over at the ‘regional’ or ‘global’ scale.

20. Scale Dependent Measurements How long is Maine’s coastline? length=340 km length=355 km length=415 km

21. Data Types Two general views to organizing spatial data: –Entities or objects Point measurements, rivers, structures Have attributes or features attached to them that Point, vector or area format Values exist at entity locations –Fields Continuous data such as temperature gradient fields and satellite imagery Values exist over an area Raster format (grids)

22. Data Types Entities and fields can be transformed to the other type

23. Reading for Tuesday Burrough and McDonnell, Principles of Geographical Information Systems, Chp. 2 (pp. 17-28) Longley et al., Geographical Information Systems and Science, Chp. 3 Environmental Data Types