# Presentation: 237429

## Spatially Explicit Modeling  of Land-Use ChangeRich Iovanna, EPA (OPEI/NCEE) & Colin Vance, German Aerospace Center (formerly of EPA)

- Spatially Explicit Modeling  of Land-Use ChangeRich Iovanna, EPA (OPEI/NCEE) & Colin Vance, German Aerospace Center (formerly of EPA)

- Why is land-use change of interest to EPA?
- Land-Use Change (LUC) – low-density urban development in particular – entails a host of environmental consequences, many of which are of considerable interest to EPA programs:
- The environmental footprint of LUC can extend well beyond the location where conversion occurred and has been associated with...
- Increased vehicle miles traveled and mobile source emissions that adversely affect human health and contribute to global climate change.
- Increased runoff and storm sewer overflows that impair receiving waters.
- Severed corridors that had supported wildlife populations by linking otherwise isolated ecosystem patches.
- More directly, LUC can impact visual amenities, recreational opportunities, and cultural heritage.

- What is the magnitude of LUC?
- At the National level, the rural area consumed by development between 1982 and 1997 was the size of Maine and New Hampshire, combined.
- We selected our study site, central North Carolina, in part because Greensboro and Raleigh ranked second and third worst in terms of overall sprawl among 83 metro areas.
- 	Kolankiewicz & Beck, 2001; Ewing et al. 2003

- A land-use change model for EPA?
- A LUC model useful to EPA is one that is policy relevant.  The approach should satisfy these criteria:
- Identifies the determinants of land-use change, particularly socio-economic factors amenable to policy intervention.
- Generates projections of land use/cover.
- Can be estimated in one spatial and temporal context and applied to others.
- Is spatially explicit so that the results provide the basis for estimating the prevalence of related stressors.

- What shape has LUC modeling at EPA taken?

- Simulation exercise
- The predictive capabilities of the model are steadily improving.  At left is the actual pattern of development in central NC between 1976-2001.  At right is the estimated model’s prediction of LUC over the same period.  Pixels developed over this period are indicated in red.

- Empirical model
- While ***i* is unknown, we do know that, for parcels yet undeveloped by *t*, it cannot be less than ******it* and that it must equal ******it* for conversion to occur at *t*.  Making use of the distribution of ***I*, we can model the likelihood conversion occurs in a parcel at *t*, conditional on the fact it has not occurred yet, i.e., the hazard rate.  The particular specification adopted is a complementary log-log proportional hazards model, which has the ability to handle...
- Spatially explicit data, particularly important given an interest in exploring the role of spatial configuration.  We employ a time-series of Landsat imagery with an adjusted 60X60m resolution.
- Interval-censored data generated when a continuous process is measured at discrete intervals, which is the case for satellite imagery.
- Time-varying covariates.

| Parameter | Marginal Effect* | Interpretation |
| --- | --- | --- |
| % Impervious (adjacent) | 5.3% | Unit increase in % impervious in a 1X1km window around a parcel increases hazard. |
| % Impervious (farther) | -2.1% | Unit increase in % impervious between 1km and 2km away from a parcel decreases hazard. |
| Distance to nearest city | -3.1% | Unit increase in distance (km) to the nearest large city decreases hazard. |
| Public land | -9.2% | Public land within 2 km of parcel decreases hazard. |
| Contagion | 6.0% | Unit increase in contagion metric (mean 9.6; s.d. 6.4) increases hazard. |

- Theory
- An open-space parcel is developed when doing so maximizes net present value of the parcel’s stream of economic “rent,” i.e., the time at which rising annual development rent just equals agricultural/forestry rent plus declining marginal development cost.  Adding a term, ***i*, to reflect a parcel’s unobservable idiosyncratic factors, development must occur at time *T*, when

- Selected parameter estimates

- *Bold indicates 95% significance.

- Smart Growth America

- Delivering Science-Based Information to Decision Makers

- Findings to date
- Characteristics of parcels, themselves, and surrounding landscape pattern are highly significant determinants of land-use change.  Accordingly, spatially explicit models are essential for projection and policy simulation.