Geospatial Data Collection… · SHS3 Geography

Lesson notes

Learning Objectives

Indicator: 3.2.2.LI.1 - Analyse and interpret geospatial data using dot maps

By the end of the lesson, learners can:

Define a dot map and state its purpose in representing geospatial data.
Convert a given data table (e.g., regional maize production in tonnes) into a dot map using an appropriate dot value.
Analyse a dot map to identify spatial patterns, such as areas of high, moderate, and low concentration.
Interpret relationships revealed by dot maps, drawing conclusions about the distribution of the mapped phenomenon.
Apply positive self-talk and self-management strategies before and during analytical tasks, recognising their readiness to succeed.

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Curriculum details

Strand: Navigating Our Environment (Strand 2)
Sub-strand: Geospatial Data Collection, Representation and Interpretation (2.2)
Content standard: 3.2.2.CS.1 - 3.2.2.CS.1 Demonstrate skills in basic geospatial data representation and interpretation using diagrams 3.2.2.LO.1 Represent geospatial data using dot maps and flow charts
Indicator: 3.2.2.LI.1 - Analyse and interpret geospatial data using dot maps
Suggested placement: Semester 1, Week 17 (Week 17 of the year)
Our suggestion, laid out in curriculum order across three terms of twelve weeks. NaCCA does not fix the week, so follow your school's scheme of learning.
Curriculum reference: NaCCA curriculum document, p. 108

Exemplars (from the NaCCA curriculum)

Activity-Based Learning: In an all-inclusive and gender-responsive classroom, analyse and interpret geospatial data using dot maps and discuss the relationships revealed in the datasets. During class task and especially before tests, ask learners should think positively about themselves, confirming that they are prepared and have everything in their power to succeed.

Assessment (3.2.2.AS.1). The document marks these depth-of-knowledge levels for this indicator: Level 4 Extended critical thinking and reasoning.

Teaching and Learning Resources:
- Geospatial datasets e.g. national, regional, district and local agricultural production such as maize and groundnuts in tonnes
- Geospatial datasets e.g. regional and district migration, trade or agricultural production (in tonnes)