Regional Development Scenario Evaluation through Land Use Modelling and Opportunity Mapping
Abstract
:1. Introduction
2. Methodology
- Simulating regional development quantitative scenarios by using a cellular automata (CA) (a class of spatially-disaggregate models, often pictured as being formed on a 2-dimensional lattice of cells, where each cell represents a land use and where embodying processes of change in the cellular state are determined in the local neighbourhood of any and every cell [17])-based land use model, MOLAND;
- Elaborating indicators to identify and define the most suitable indicators for analysis;
- Creating indicator maps within a Geographical Information System (GIS) environment to support policy makers in assessing how those indicators change in the case of different future scenarios.
2.1. Study Area
2.2. Land Use Model MOLAND
2.3. Scenarios
Scenario | Suitability and Transport | Population 2026 | Jobs 2026 | ||
---|---|---|---|---|---|
Industry | Commerce | Service | |||
Pre-recession trends or business as usual Further development of urban patterns as emerged before the economic crisis. | Default suitability (as in Figure 2); New light rail lines are introduced by 2020. | 2,553,149 | 380,573 | 559,519 | 362,129 |
Compact Development Urban growth and regional development in the frame of a strong environmental protection policy with less pressure on natural land uses. | Specific suitability map promoting urban development in the metropolitan area and key towns with restricted zone along coastline. New light rail lines are introduced by 2020. | 2,553,149 | 544,219 | 800,112 | 517,844 |
Managed Dispersed Growth and sprawl of rural town and villages in open countryside particularly along the Dublin-Belfast corridor. | Specific suitability map promoting urban development in metropolitan area, key towns, as well as along the Dublin-Belfast corridor. | 2,553,148 | 418,630 | 615,471 | 398,342 |
Recession Slow urban development due to recession, including a potential recovery by 2016. | Default suitability (as in Figure 2). | 2,133,819 | 300,603 | 372,329 | 244,898 |
2.4. Informing Decision Makers
- (a)
- The model is trying to gauge the land use change caused by specific policies. In this case, the transport network, zoning and suitability maps are key elements of the MOLAND, allowing it to reproduce characteristics of a scenario [10]. Both zoning and suitability maps represent the capacity of a cell to support a particular land use. Transport network updates, such as new light rail lines, might shift the use of land in the future. The customization of these elements together with the fine tuning of other parameters (such as population and job projections) can permit decision makers to explore specific scenarios.
- (b)
- The model can also help policy makers by using a reverse direction argument. This argument assumes certain inputs are out of the control of policy makers (such as population and migration), but those population dynamics will cause land use change in the future. Thus, the model can inform policy makers about which locations will change the most in case of specific population and employment projections and, hence, locations that will demand policies to address higher population density, investment in infrastructure, etc. [22].
2.5. Indicators
N | Indicator (Variable Name) | Description | Effect (Direction Preference) |
---|---|---|---|
1 | Proximity to urban green areas (Dist_Res2Grn) | Mean minimum distance from residential areas in an ED to semi-natural (beaches, sand plains, natural grassland, woodland shrub, etc.), forest and artificially vegetated areas (parks, sport facilities, etc.). | Negative |
2 | Distance to industrial areas (Dist_Res2Ind) | Mean minimum distance from residential areas in an ED to industrial areas. | Positive |
3 | Proximity to commercial areas (Dist_Res2Com) | Mean minimum distance from residential areas in an ED to commercial areas. | Negative |
4 | Proximity to services (Dist_Res2Ser) | Mean minimum distance from residential areas in an ED to service areas. | Negative |
5 | Proximity to main roads (Dist_Res2Rd) | Mean minimum distance from residential areas in an ED to main roads (motorway, national and regional roads). | Negative |
6 | Proximity to coast (Dist_Res2Coast) | Mean minimum distance from residential areas of a neighbourhood to coastline. | Negative |
7 | Proximity to railroad and light rail stations (Dist_Res2RailSt) | Mean minimum distance from residential areas in an ED to railroad and light rail stations. | Negative |
8 | Proportion of residential areas adjacent to major transport routes (Res_inRd200mbuf) | Proportion of residential areas (from overall residential) in an ED within 200-m distance from major roads, railway and light rail. | Negative |
9 | Quantity of potential employers (IndComSer_frmRes15km) | Total area of industrial, commerce and service areas within 15-km radius from residential neighbourhoods. | Positive |
10 | Quantity of nearby tertiary education institutes (TertiaryMean_frmRes13km) | Mean number of tertiary institutes within 13 km from residential neighbourhoods (the average distance travelled by students aged 19+ years is 13 km (Central Statistics Office (CSO), 2007)). | Positive |
11 | Quantity of nearby green areas (SmnatForestArtveg_frmRes5km) | Total green (semi-natural, forest and artificially vegetated areas) areas within 5-km radius from residential areas in an ED. | Positive |
12 | Ratio of green areas to built areas (Ratio_SmnalForestArtveg2Urban) | The ratio of green (semi-natural, forest and artificially vegetated areas) areas to built areas (residential, industrial, commercial, service). | Positive |
2.6. Opportunity Mapping
3. Results and Discussion
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Appendix
Indicators Calculation Methodology
A1. Proximity of Residential Areas to Green/Industrial/Commercial/Service Areas
- Land use raster map is converted to a vector map (ArcToolbox > Conversion Tools > From Raster > Raster to Polygon).
- Resulting land use vector layer is intersected with EDs vector layer (ArcToolbox > Analysis Tools > Overlay > Intersect).
- Centroids of all intersected polygons are generated (ArcToolbox > Data Management Tools > Features > Feature to Point).
- Two groups of centroids were selected and exported to new layers: centroids corresponding to residential land use polygons and centroids corresponding to the second relevant land use (green, industrial, commercial, etc.) (ArcToolbox > Analysis Tools > Extract > Select followed by ArcToolbox > Data Management Tools > Features > Copy Features).
- Centroids corresponding to residential land use polygons were spatially joined with ED polygons in which they are located (ArcToolbox > Analysis Tools > Overlay > Spatial Join, using COMPLETELY_WITHIN match option). As a result each “residential” centroid has got corresponding ED code in its attribute table, which will be later used in Summary Statistics.
- The distances between points in the two centroid layers are calculated (ArcToolbox > Analysis Tools > Proximity > Point Distance). This calculates the distances between all possible pairs of points from these two layers.
- Minimum distance from a residential point to the second land use point is identified (ArcToolbox > Analysis Tools > Statistics > Summary Statistics).
- Resulting table is joined with ED layer (ArcToolbox > Data Management Tools > Joins > Add Join).
- Mean minimum distance is calculated for each ED (ArcToolbox > Analysis Tools > Statistics > Summary Statistics).
A2. Proximity to Main Roads/Railway/Coast
- Land use raster map is converted to a vector map (ArcToolbox > Conversion Tools > From Raster > Raster to Polygon).
- Resulting land use vector layer is intersected with EDs vector layer (ArcToolbox > Analysis Tools > Overlay > Intersect).
- Centroids of all intersected polygons are generated (ArcToolbox > Data Management Tools > Features > Feature to Point).
- Centroids corresponding to residential land use polygons are selected and exported to new layer (ArcToolbox > Analysis Tools > Extract > Select followed by ArcToolbox > Data Management Tools > Features > Copy Features).
- “Residential” centroids are spatially joined with ED polygons in which they are located (ArcToolbox > Analysis Tools > Overlay > Spatial Join, using COMPLETELY_WITHIN match option). As a result each “residential” centroid has got corresponding ED code in its attribute table, which will be later used in Summary Statistics.
- The distances from centroids to the nearest object in the “Near” layer (e.g., roads or coastline) are calculated (ArcToolbox > Analysis Tools > Proximity > Near).
- Mean minimum distance is calculated for each ED (ArcToolbox > Analysis Tools > Statistics > Summary Statistics).
A3. Proportion of Residential Areas Adjacent to Major Transport Routes
- A specific (200 m) buffer from major roads is created (ArcToolbox > Analysis Tools > Proximity > Buffer).
- The road buffer is intersected with ED boundaries (ArcToolbox > Analysis Tools > Overlay > Intersect).
- Land use areas are calculated using road buffer and ED intersect polygons as zone feature (ArcToolbox > Spatial Analyst Tools > Zonal > Tabulate Area).
- New field “Residential” is added to the statistics table (ArcToolbox > Data Management Tools > Fields > Add Field).
- The values of Residential field is calculated as a summary of four residential land use types used in the MOLAND land use dataset (ArcToolbox > Data Management Tools > Fields > Calculate Field).
- All other fields in the summary table are deleted.
A4. Quantity of Potential Employers
- Land use raster map is converted to a vector map (ArcToolbox > Conversion Tools > From Raster > Raster to Polygon).
- Resulting land use vector layer is intersected with EDs vector layer (ArcToolbox > Analysis Tools > Overlay > Intersect).
- Centroids of all intersected polygons are generated (ArcToolbox > Data Management Tools > Features > Feature to Point).
- Centroids corresponding to residential land use polygons are selected and exported to new layer (ArcToolbox > Analysis Tools > Extract > Select followed by ArcToolbox > Data Management Tools > Features > Copy Features).
- A buffer with specified distance has created for residential polygon centroids.
- The buffers are dissolved using ED codes.
- Land use areas are calculated using dissolved buffer layer as zone feature (ArcToolbox > Spatial Analyst Tools > Zonal > Tabulate Area).
- Selected land use (industrial, commerce and service) areas are summed.
A5. Quantity of Nearby Tertiary Education Institutes
- 1–6
- steps from A4 model;
- 7.
- The dissolved buffers are spatially joined with tertiary institutes layer (as a result the number of the institutes in each buffer for each ED is counted).
A6. Quantity of Nearby Green Areas
- 1–7
- steps from A4 model using 5 km in step 5.
- 8.
- Semi-natural, forest and artificially vegetated areas are summed.
A7. Ratio of Green Areas to Built Areas
- Land use areas are calculated using ED polygons as zone feature (ArcToolbox > Spatial Analyst Tools > Zonal > Tabulate Area)
- The sum of green (semi-natural, forest and artificially vegetated areas) and built areas (residential, industrial, commercial, service) have been calculated for each ED.
- The ratio of the above mentioned sums is calculated.
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Shahumyan, H.; Williams, B.; Petrov, L.; Foley, W. Regional Development Scenario Evaluation through Land Use Modelling and Opportunity Mapping. Land 2014, 3, 1180-1213. https://doi.org/10.3390/land3031180
Shahumyan H, Williams B, Petrov L, Foley W. Regional Development Scenario Evaluation through Land Use Modelling and Opportunity Mapping. Land. 2014; 3(3):1180-1213. https://doi.org/10.3390/land3031180
Chicago/Turabian StyleShahumyan, Harutyun, Brendan Williams, Laura Petrov, and Walter Foley. 2014. "Regional Development Scenario Evaluation through Land Use Modelling and Opportunity Mapping" Land 3, no. 3: 1180-1213. https://doi.org/10.3390/land3031180
APA StyleShahumyan, H., Williams, B., Petrov, L., & Foley, W. (2014). Regional Development Scenario Evaluation through Land Use Modelling and Opportunity Mapping. Land, 3(3), 1180-1213. https://doi.org/10.3390/land3031180