GIS Fundamentals/ Geographic Database Design Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
GIS Concepts • • • Information cycle: • Data/Information/System/Information System Geographic Information System • Main Components/Characteristics Geographic Database • Data Modeling • Data Representation Spatial Analysis Implementing a GIS Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Information Cycle Territory Data GIS Information DSS Decision Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Data / Information • Information is the result of interpretation of relations existing between a certain number of single elements (called data). • Example: The Museum located at 5 th Avenue, NY, was built in 1898. • • Data: Museum, address, year of construction. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
System • A system is a set organized globally and comprising elements which coordinate for working towards doing a result. • • Example: Water supply system Elements: pipes, valves, hydrants, water meters, pumps, reservoirs, etc. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Information System (IS) • An Information System is a set organized globally and comprising elements (data, equipment, procedures, users) that coordinate for working towards doing a result (information). Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
GIS: “G” & “IS” Definition: • A GIS is a collection of computer hardware and software, geographic data, methods, and personnel assembled to capture, store, analyze and display geographically referenced information in order to resolve complex problems of management and planning. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Co m po ne nt so fa GI S Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Geographic Data Geographic Information Input Output GIS • Maps • Census • Field Data • RS Data • Others Data Capture Manipulation Analysis Display Storage • Reports • Maps • Photo. Products • Statistics • Input Data for models GIS Components Other GIS User Interface Models Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
GIS: Main Characteristics • Integration of Multiple data: • • • - Sources - Scales - Formats • Geographic Database • Spatial Analysis Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Data from multiple sources-at multiple scales-in multiple formats Census/ Tabular data Maps Picture & Multimedia GPS/ air photos/ satellite images Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Referencing map features: Coordinate systems & map projections • To integrate geographic data from many different sources, we need to use a consistent spatial referencing system for all data sets Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
The Latitude/Longitude reference system • latitude φ : angle from the equator to the parallel • longitude λ : angle from Greenwich meridian Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Map Projections • Curved surface of the earth needs to be “flattened” to be presented on a map • Projection is the method by which the curved surface is converted into a flat representation Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Map Projections (Cont. ) • We can think of a projection as a light source located inside the globe which projects the features on the earth’s surface onto a flat map • Point p on the globe becomes point p on the map Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Distortion in Map Projections • Some distortion is inevitable • Less distortion if maps show only small areas, but large if the entire earth is shown • Projections are classified according to which properties they preserve: area, shape, angles, distance Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Compromise projections • Do not preserve any property, but represent a good compromise between the different objectives • e. g. , Robinson’s projection for the World Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Compromise projections Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
UTM: Universal Transverse Mercator • Minimal distortions of area, angles, distance and shape at large and medium scales • Very popular for large and medium scale mapping (e. g. , topographic maps) Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
UTM • Cylindrical projection with a central meridian that is specific to a standard UTM zone • 60 zones around the world Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Space as an indexing system Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
The concept of scale • scale is the ratio between distances on a map and the corresponding distances on the earth’s surface • e. g. , a scale of 1: 100, 000 means that 1 cm on the map corresponds to 100, 000 cm or 1 km in the real world Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
The concept of scale • scale is essentially a ratio or representative fraction • small scale: small fraction such as 1: 10, 000 shows only large features • large scale: large fraction such as 1: 25, 000 shows great detail for a small area • “small scale” versus “large scale” often confused Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Multi-scales • The same feature represented in different scales. • Example: lake Large scale Small scale (1: 25. 000) 1: 500. 000 Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Multi-formats • • Raster Vector Raster-Vector. Raster DXF-DGN-etc. Shapefile KML Etc. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Geographic Database • Geographic Data • Characteristics • Examples • Geographic Dataset • Geographic Database Concepts • Spatial entity • Data Modeling Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Descriptive Data vs Geographic Data • General Data: • Descriptive attributes • Geographic Data: • Descriptive attributes • Spatial attributes • Location • Form Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Geographic Data Characteristics : Position: explicit geographic reference ˜ Cartesian coordinates : X, Y, Z ˜ Geographic coordinates (lat, log) implicit geographic reference ˜ Address ˜ Place-name ˜ Etc. Geometric Form: ˜ ex: a polygon representing a parcel of land Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Example 1: Parcel of land • Attribute (descriptive) Data • Landowner • Area • Etc. • Spatial data • Position • Located at 100 Nelson Mandela Ave • X= a; Y=b within system (X, Y) • Form • dimensions (sides and arcs, constituting a polygon) Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Example 2: District • Attribute (Descriptive) data: • District-Code • District-Name • Population 1990 • Population 2000 • Population 2010 • Spatial data: • Geographical Position • Polygon Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Geographic Database • • Definition Components: • Spatial Entity/Attribute/Dataset • Data Modeling/Data Dictionary • Spatial Representation • Vector/Raster • Topology • Standard Spatial Operations Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Spatial entity • We use the term entity to refer to a phenomenon that can not be subdivided into like units. Example: a house is not divisible into houses, but can be split into rooms. Others: a lake, a statistical unit, a school, etc. • In database management systems, the collection of objects that share the same attributes. • An entity is referenced by a single identifier, perhaps a placename, or just a code number Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Attribute • Each spatial entity has one or more attributes that identify what the entity is, and describe it. Example: you can categorize roads by whether they are local roads, highways, etc; by their length; their width; their pavement; etc. • The type of analysis you plan to do depends on the type of attributes you are working with. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Dataset “A dataset is a single collection of values or objects without any particular requirement as to form of organization. ” Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Geographic Database • “A geographic database is a collection of spatial data and related descriptive data organized for efficient storage, manipulation and analysis by many users. ” • It supports all the different types of data that can be used by a GIS such as: • Attribute tables • Geographic features • Satellite and aerial imagery • Surface modeling data • Survey measurements Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Data Modeling • Data Approach • Modeling Process • Entity/Relationship Approach • Example Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Modeling Process Abstracting the Real World Reality Modeling (data & treat. ) Geographic Database Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
ANSI/SPARC: Study Group on Data Base Management Systems (1975) External Model 1 “Real World” Different users have different views of the world External Model 2 External Model 3 Conceptual Model Logical Model Physical Model Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Conceptual Model • A synthesis of all external models (user’s views). • Schematic representations of phenomena and how they are related. • Information content of the database (not the physical storage) so that the same conceptual model may be appropriate for diverse physical implementations. • Therefore, the conceptual model is independent from technology. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Conceptual Model (cont. ) • Easy to read • Conceived for the analyst or designer • Objective representation of the reality, therefore independently from the selected GDB System • One conceptual model for the Database Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Data Logical Model & Physical Model • We transform the conceptual model into a new modeling level which is more computing oriented: the logical model (Example: the Relational Database approach) • We transform the logical model into an internal model (physical model) which is concerned with the byte-level data structure of the database. • Whereas the logical model is concerned with tables and data records, the physical model deals with storage devices, file structure, access methods, and locations of data. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Several types of data organization • Hierarchical model - Hierarchical relationships between data (parent- child) • Network Model - Focus on connections • Relational model - Based on relations (tables) • Object-Oriented model - Focus on Objects Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Entity-relationship Formalism Entity name Attributes ENTITY_NAME -attribute 1 -attribute 2 … 0 -N Identifier (key-attribute) 0 -1 Maximum cardinality Association (relationship) Minimum cardinality Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
An example of land parcels Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
The E/R diagram for land parcels STREET A -name 2 -N B SEGMENT 0 -1 A: Streets have edges (segments) B: parcels have boundaries (segments) PARCEL -number 1 -2 3 -N 2 -2 1 -N C D 2 -N C: line have two endpoints POINT D: parcels have owners, and people own land. -number -x, y 1 -N LANDOWNER -name -date-of-birth Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Data Tables Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Data Dictionary • Definition: A data catalog that describes the contents of a database. Information is listed about each field in the attribute table and about the format, definitions and structures of the attribute tables. A data dictionary is an essential component of metadata information. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Example: Census GIS database • - Basic elements • Entity: administrative or census units • enumeration areas • Entity type / Relations • Components of a digital spatial census database: • Boundary database • Geographic attribute tables • Census data tables Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Relations EA entity can be linked to the entity crew leader area. The table for this entity could have attributes such as the name of the crew leader, the regional office responsible, contact information, and the crew leader code (CL code) as primary code, which is also present in the EA entity. R EA EA-code Area Pop. 1 -1 Crew leader area 1 -N CL-code Name RO responsible Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Entity: Enumeration areas Type (attributes) EA-code Area 50101 50102 50103 50104 50201 50202 50203 50204 … 28. 5 20. 2 18. 1 22. 4 19. 3 17. 6 25. 7 26. 8 … Pop. 988 708 590 812 677 907 879 591 … CL-code 78 78 79 79 Identifier Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Components of a digital spatial census database Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Data Representation Raster Vector Real World Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Two Fundamental Types of Data • • • GIS work with two fundamentally different types of geographic information • Vector • Raster (or Grid) Both types have unique advantages and disadvantages A GIS should be able to handle both types Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Vector vs Raster or Discrete vs Continuous Raster Vector River x 1, y 1 xn, yn Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Raster Data • • • A raster image is a collection of grid cells - like a scanned map or picture Raster data is extremely useful for continuous data representation • elevation • slope • modeling surfaces Satellite imagery and aerial photos are commonly used raster data sets Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Vector Data • • Vector data are stored as a series of x, y coordinates Good for discrete data representation • points: wells, town centroids • lines: roads, rivers, contours • polygons: enumeration areas, districts, town boundaries, building footprints Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Raster-Vector conversion (“vectorization”) Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Vector to Raster Conversion: Polygons a b c Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Vector to Raster Conversion: Lines Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Raster to Vector Conversion: Polygons Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Raster to Vector Conversion: Polygons Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Vector data + image (raster) Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Vector: Points, lines, polygons Set of geometric primitives: • points y lines polygons node vertex x Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Vector Structure • • • Spaghetti Topology I II Network (graph) Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Spaghetti File No Topology = raw file or ‘spagehetti file’ Lines not connected; have no ‘intelligence’ Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Example of “Spaghetti” data structure 6 5 4 3 2 1 B Poly coordinates A (1, 4), (1, 6), (6, 4), (4, 4), (1, 4) B (1, 4), (4, 1), (1, 4) C (4, 4), (6, 1), (4, 4) A 1 2 3 C 4 5 6 Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Topology • Data structure in which each point, line and piece or whole of a polygon : • “knows” where it is • “knows” what is around it • “understands” its environment • “knows” how to get around Helps answer the question what is where? Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Topology: Spatial Relationships Left Polygon = A Right Polygon = B Adjacency Node 1 = Chains A, B, C Chain A is connected to chains B & C Connectivity Polygon B Contained within polygon A Containment Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Example of Topological data structure 1 6 5 4 3 I 2 A 4 B 1 2 Node I II IV 1 2 II 6 IV 3 4 5 C 5 III 3 6 O = “outside” polygon X 1 4 6 4 Y 4 4 4 1 From Line Node 1 I 2 I 3 III 4 I 5 II 6 II Lines 1, 2, 4 4, 5, 6 1, 3, 5 2, 3, 6 Poly A B C To Left Node Poly III O IV B IV O II A IV C Lines 1, 4, 5 2, 4, 6 3, 5, 6 Right Poly A O C B Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Encoding Topology (not): CAD Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Encoding Topology: GIS Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Comparison Advantages: Spaghetti -Set of independent objects - Representation of heterogonous objects within the same model -Appropriate to CAD Topology -Pre-calculation of topological relations -Maintenance of topological constraints - correspondence with exchange formats Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Comparison (cont. ) Disavantages Spaghetti -Spatial Relationships calculated - Risk of incoherence (duplication of common boundaries) Topology -High cost of up-to-date -Many levels of indirections for complex objects -Maintenance Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Some well known Topological models üTIGER: Topologically Integrated Geographic Encoding and Referencing (Census Bureau of the USA) Line is the principal element to which are related points and area features üARC/INFO model: ESRI Point, Line, Polygon Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
TIGER Data: Polygon Cities Zip Codes Census MCD’s Block Counties Voting Tracts Groups Districts Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
TIGER Data: Line Railroads Streets Streams Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
TIGER Data: Point Zip+4 Place Names Landmarks Key Locations Centroids Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Recapitulation on spatial models • Transformations between models: • “vectorization” of raster images (costly) • topology toward spaghetti (easy) • spaghetti toward topology (possible but costly) • The vector model most used, essentially topology; it’s useful to integrate raster and vector Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Spatial Analysis: Query • select features by their attributes: • “find all districts with literacy rates < 60%” • select features by geographic relationships • “find all family planning clinics within this district” • combined attributes/geographic queries • “find all villages within 10 km of a health facility that have high child mortality” Query operations are based on the SQL (Structured Query Language) concept Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Examples: What is at…? Features that meet a set of criteria Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Spatial Analysis (cont. ) • Buffer: find all settlements that are more than 10 km from a health clinic • Point-in-polygon operations: identify for all villages into which vegetation zone they fall • Polygon overlay: combine administrative records with health district data • Network operations: find the shortest route from village to hospital Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Modeling/Geoprocessing • modeling: identify or predict a process that has created or will create a certain spatial pattern • diffusion: how is the epidemic spreading in the province? • interaction: where do people migrate to? • what-if scenarios: if the dam is built, how many people will be displaced? Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Spatial relationships • • Logical connections between spatial objects represented by points, lines and polygons e. g. , - point-in-polygon - line-line - polygon-polygon Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Spatial Operations • “adjacent to” • “connected to” • “near to” • “intersects with” • “within” • “overlaps” • etc. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
“is nearest to” • Point/point • Which family planning clinic is closest to the village? • Point/line • Which road is nearest to the village • Same with other combinations of spatial features Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
“is nearest to”: Thiessen Polygons Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
“is near to”: Buffer Operations • Point buffer • Affected area around a polluting facility • Catchment area of a water source Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Buffer Operations • Line buffer • How many people live near the polluted river? • What is the area impacted by highway noise Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Buffet Operations • Polygon buffer • Area around a reservoir where development should not be permitted Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
“ is within”: point in polygon • Which of the cholera cases are within the containment area Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Problem: We may have a set of point coordinates representing clusters from a demographic survey and we would like to combine the survey information with data from the census that is available by enumeration areas. Solution: “Point-in-Polygon” operation will identify for each point the EA area into which it falls and will attach the census data to the attribute record of that survey point. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
“overlaps”: Polygon overlay Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Polygon Overlay Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Data Layers Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Spatial aggregation • Example of Spatial aggregation: • fusion of many provinces constituting an economic region Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Spatial data transformation: interpolation Example 1: Based on a set of station precipitation surface estimates, we can create a raster surface that shows rainfall in the entire region 13. 5 12. 7 20. 1 15. 9 26. 0 27. 2 24. 5 26. 1 Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
GIS capabilities: Visualization Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Implementing a GIS • • • Consider the strategic purpose Plan for the planning Determine technology requirements Determine the end products Define the system scope Create a data design Choose a data model Determine system requirements Analyze benefits and costs Make an implementation plan Source: Thinking About GIS, Third Edition Geographic Information System Planning for Managers Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
GIS: Enables us to handle very large amounts of data • • Example: census data – thousands of EAs – hundreds of variables – many complementary data layers (roads, rivers, public facilities) Example: remote sensing – satellites send huge amounts of data that need to be processed, interpreted and stored Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
GIS: Helps to make data re-usable and useful to many more users • Census geography – EA maps do not have to be redrawn every time, only updated – census information can be used for many more applications – data sharing among agencies Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
In Conclusion • GIS for inventory/visualization • GIS creates maps from data pulled from databases anytime to any scale for anyone • GIS for database management • GIS for spatial analysis/modeling • GIS a tool to query, analyze, and map data in support of the decision making process. Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
What is Not GIS • GPS – Global Positioning System • …not just software! • …not just for making maps! • Maps are an input data to and a “product” of a GIS • A way to visualize the analysis Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Literature related to Census Mapping & GIS • • US National Research Council: • Tools and Methods for Estimating Populations At Risk David Martin (1996) • Geographic Information Systems: Socioeconomic Applications Longley and al, Wiley (2005) • Geographic Information Systems and Science, second edition ESRI Press: • Unlocking the Census with GIS • Mapping the Census 2000 Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
Contact Information: Demographic Statistics Section UN Statistics Division New York globalcensus 2010@un. org Workshop on Census Cartography and Management, Lusaka, Zambia, 08– 12 October 2007
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