Introduction to spatial data analysis

Transcription

1 Introduction to spatial data analysis 2 Doctoral School in Economics (+EDSD), La Sapienza, 2017 Instructors: Filippo Celata (and Luca Salvati) Associate external data about firms owned by foreign born entrepreneurs* to the geocoded firms Table and spatial join - Add zoneurbanistiche.shp to a blank map - Add the table spatial17/vv/immig_dt.dbf and the point layer lspatial17/vv/rm_immig.shp - Open and explore the two tables - Join the dbf table to the point layer - Export to consolidate - Symbolize the provenience of entrepreneurs * Istat, Asia archive of firms, 2008, Rome, Firms owned by foreign born, Industry: firm services > 2 employed

2 Join table data: - Add to the workspace spatial17/vv/rm_immig.shp and /immig_dt.dbf - Find the two analogous columns in the attribute table of rm_immig.shp and of immig_dt.dbf - Join rm_immig.dbf TO the attribute table of the shp: right click ON THE LAYER / Join / Table join (verify) - Export to consolidate: right click on the shp / data / export data - Symbolize the provenience of entrepreneurs: open the Symbology menu of the output Shp / Format: Categories, Unique values / Value field: Area -Spatial join: to associate the value of a layer s attribute table to another layer s attr. tab. based on a spatial relation -To attribute to each firm the name of its zone of location -> join to the firms layer attribute table the name ( denom ) of the zona urbanistica where the firm is located (right click the layer / Join / Join data based on spatial location Selection and queries - Manual selection Table and spatial selection and queries On the table On the map Multiple selections: on the table keep CTRL pressed / on the map keep the shift key (Maiuscolo) pressed

3 - Selection queries: Select by attributes (=, <, > ) Select by location Select by location: Intersect Are within a distance of Are within Are completely within Contain Completely contain Have their centroid in Share a line segment with Touch the boundary of Are identical to Are crossed by the outline of -To create a layer or shapefile including only the selected feature 1. Create new layer from selected features: right click the selected layer / selection / create layer from selected features 2. Create a shapefile of the selected features: right click on the layer of selected features (1) / data / export data (as shapefile) [3. To export as an autonomous layer: right click / save as layer file]

4 Selections - Create a layer of the zona urbanistica centro storico = select centro storico on the map or in the table of zoneurbanistiche.shp / right click on the selected layer / Selection / Create layer from selected feature - Select all non-individual firms (type = individ ) = right click the firms layer / Selection / Select by attributes / + switch selection (= right click the layer / Selection / Switch selection) / Create a new layer including only the selected features - Select firms owned by Bangladeshies (Select by attributes. Field: origine ) - Selected all firms located in the zona urbanistica (ie. source layer ) centro storico = Select by location / Method: Target layers features are completely within the source layer + switch selection and create new layer - Select firms with more than 5 employed (Select by attributes, add08 > 5) - Select all firms located within 5 Kms from the biggest firm = select and create a layer with only the biggest firm / Select by location all other firms (Method: are within a distance ) Proximity analysis and distance decay function Proximity = concentration, agglomeration, interaction, attraction, influence, contagion, interdependency, similarity, clustering, spatial autocorrelation, segregation, etc. Toolbox -Buffering

5 Distance (geographic vs. functional) Euclidean vs. Manhattan vs. Network based Buffering Create a buffer of 500 mt from rm_immig.shp 1) Arctoolbox / Analysis / Proximity / Buffer / input feature: rm_immig.shp, linear unit: 500 mt, dissolve: none 2) Create a shapefile with the buffer output (right click the buffer / Data / Export data) cost-weighted distance: cost raster (eg land use, slope) Distance and spatial interaction: interaction opportunities decrease more than proportionally as the distance between interacting features increases = distance decay function (tiranny of distance) Inverse distance decay: b > 1 = eg. 2 (interaction opportunities are inversely proportional to the square distance)

6 Exponential distance decay: Spatial clustering: events are not randomly or uniformly distributed, but tend to cluster in specific locations (= colocation, attraction, contagion, interdependency, ) random (concentrated*) Identify spatial clusters uniform / inhibitory clustered

7 Example: The clustering of migrant entrepreneurs: segregation /assimilation vs the ethnic enclaves hypothesis (Portes and Wilson. "Immigrant Enclaves, American Journal of Sociology, 1980) Proximity, interaction and firms clusters Atlas of Economic Clusters in London (GaWC): A clustered firm is defined as one whose average distance to its 10 nearest neighbours (in its sector) is less than 100 metres Identifies areas with more than 10 firms owned by foreign born within a buffer of 500 mts: Inputs: -> Point layer of firms owned by foreign born -> Buffer polygonial layer How many points are within each buffer polygon? -> Attribute to any buffer the number of firms within = Associate to the buffer s attribute table the number of points within = Spatial join / Each polygon will be given a summary of the numeric attributes of the point layer (field: Count ) -> Select all firms located in buffers including at least 10 firms = Select by attributes Identifies cluster of firms owned by foreign born 1) Do a Spatial join in order to attribute to any buffer the number of points located within: right click the buffer layer / Join and relates / Join / Join data from another layer base on spatial location / Each polygon will be given a summary 2) In the output shapefile: select all polygons with Count > 10 and create a layer with the selected geatures 3) Select all points within the polygons selected at point 2): Selection / Select by location / Target layer: rm_immig.shp / Source layer: selected buffers / Spatial selection method: Target layer features are within the source layer / Run 4) Create a layer with the selected point (right click the layer / create layer from selected features) -> Create a shapefile with the selected points (Export data)

8 Density maps Densità di unità locali nell area di Prato, 2008 Densità di unità condotte da imprenditori cinesi, 2008 Density maps Fonte: elaborazione su dati Istat * Kernel density, raggio: mt Fonte: elaborazione su dati Istat * Kernel density, raggio: mt Kernel density Silverman, Density estimation for statistics and data analysis, 1986 Measure the density for each point in the map, based on the number of features (points, or weighted points) which are located within a certain ray or threshold, by performing a spatial moving average Spatial analyst / density / kernel density Input: point layer (or lines) Population field: weight Output raster: output raster file Search radius: max distance of points whose weight will be considered for calculating densities, in map units (default: min. extent / 250) Area units: how densities will be expressed in the legend Cell size: the dimension of pixels in the output raster (default: min. extent / 30 -> average distance between all points, or it depends)

9 Set the extent of the output raster in env. settings / processing extent equal to that of the whole area and/or use the area layer (zoneurbanistiche.shp) as a mask (in raster analysis/mask) Create a density maps of ALL firms owned by foreign born: spatial analyst / density / kernel density (Input: rm_immigdt.shp (or similar); Population field: CNT; cell size: default or set; search radius: meters / in the environments : set the extent and raster analysis/mask to zoneurbanistiche.shp Mapping: Set the output raster symbology (-> quantiles) and customize and export the map as an image file in layout view: file/export map (300 dpi) Proximity and spatial autocorrelation First law of geography (Tobler) = "Everything is related to everything else, but near things are more related than distant things." Apparent contagion (or attraction): clustering is due to chance or a reaction to exogenous conditions vs. real contagion (or attraction): concentration is due to attraction or interdependency of the clustering features (Anselin) SPATIAL AUTO CORRELATION: the degree to which nearby geographical features are similar (vs. the complete spatial randomness hypothesis: CSR)

10 Coordinate systems and progjections Coordinate systems and projections How to represent on a twodimensional surface the threedimensional world with the least distortion Coordinate systems -Projected coordinate systems vs. geographic coordinate systems -Equidistant / equivalent / isogonic projections Universal Transverse Mercator (UTM) 60 -Dataframe coordinate system vs. each layer s coordinate system The golden rule : dataframe coordinate system = each layers coordinate system = output map coordinate system Cylindric, equidistant and conform projected coordinate system: topographic maps, wide scales (= small areas), spatial analysis...

11 Set the coordinate system - To set the dataframe coordinate system: layers / properties / coordinate system - To change the coordinate system of layers/shapefil es: data / export data / same coordinate system as the data frame Work with shapefiles geometry: geodata editing and geoprocessing Editing: to modify the geometry of geodata Editing / reshape existing features -Right click the layer to be edited / start editing -Eg. To create new points (manually)

12 Add new fields (columns) to the attribute table Field calculator Fields formats: String (text) Integer (eg. 3) Double (3,21) Etc. Calculate geometry Area Perimeter Centroid Lenght Ecc. Geprocessing Merge - Calculate the area of zone urbanistiche in square miles: add field (name: areamiles ; type: double; precision: 12, scale: 2) / right click the field / calculate geometry (area in sq miles)

13 Dissolve Fai: utilizzando il layer delle zone urbanistiche, si crei uno shapefile dei Municipi di Roma, tramite dissolve field: Municipi Others

14 Split Geodata geometry conversion (points <-> polygons <-> lines) 1) To convert polygons into points (pesati/marcati): CENTROIDS (problems in case of irregular areas). - On zoneurbanistiche.shp, in the attribute table: Table properties/add field (field X, field Y ; format: double, precision: 12 / scale: 4). - Right click on field X (and Y) / calculate geometry to extract longitude and latitude of the centroid / export the table in.dbf - Create a point layer with the centroids by adding the table exported above through the Tool Add x,y Data. - Export data to convert the layer into a shapefile

15 2) To convert points into polygons: create THIESSEN POLYGONS (triangulated irregular network (TIN) that meets the Delaunay criterion) FISHNET: to create a regular polygonal gridded shapefile -Arctoolbox/Analysis/Proximity/Create Thiessen Polygons FISHNET (2) Create a linear layer connecting several points of origin or destination (point-to-point network) -Points to Line (1 to 1) -(Spider) (1 to All) Set the extent of cells in the grid (in map units, eg. meters) / numbero of rows or columns= 0 (or viceversa) All to All: EC calculate (and draw) / ET Geo Wizard / etc.