The role of cadastral surveying and GIS in the management of restitution and land rights claims, a case study of Lady Selborne
Bibliographic record
Abstract
Twenty six years into democracy, the issue of land redistribution as promised by the government of South Africa is still far from being resolved. The government promised to correct the injustices of the past which left many black people stripped of their land and poor. The state passed the Restitution of Land Rights Act 22 of 1994 to bring back the land to the rightful owners, but to date some claimants who lodged their claims before 1998 have not seen the ‘promised land’ yet. The time that has passed since independence clearly indicates that there are challenges that are hindering the process. The Commission on Restitution of Land Rights (CRLR) is facing challenges with regard to the shortage of skilled staff to collect evidence because project officers, who are supposed to assist the claimants, seem confused by the maps. In addition, some of the rural land for expropriation has not been surveyed yet. The aim of the study was to discuss and show the importance of the involvement of Cadastral Surveying and Geographical Information Systems (GIS) in the processing of restitution of land rights claims in improving the turnaround time of claims and to show how that can be done. The case study was conducted at lot 408 Lady Selborne township situated in Pretoria North (25˚41ʹ27ʹʹS and 28˚07ʹ42ʹʹE) City of Tshwane Metropolitan Municipality (CTMM), Gauteng Province (GP). Qualitative research methodology was used to discuss the role of cadastral surveying and GIS. Articles, journals, books, reports and legislation documents were used to gather the required information. Another methodology used was overlay methodology which superimposes different data (cadastral documents, historical images and shape files) with common coordinates reference systems. Geomatics is regarded as a scarce skill specialising in measuring the earth size, boundary positioning, analysing structure and physical features. In this study a combination of cadastral surveying and Geographical Information System (GIS) technology was used. The focus was to investigate and locate the actual piece of land that was claimed. The role of cadastral surveying and GIS in the restitution and land claims is to paint a clear picture to those who were not there during dispossession, by giving historical background data in terms of size, historical images and maps. Another role is to serve as a mediator to resolve boundary disputes, encroachment disputes, overlapping claims and multiple claims on one piece of land, and by locating of the correct boundary beacons. This process provides an objective truth regarding land details, thereby providing clarity regarding the claimed land. Open-source software Quantum GIS (QGIS), Microsoft excel and Base Map Google satellite were used for the analysis of data. In this study, the land reform process of the Republic of South Africa (RSA) was compared with Australia, New Zealand and the Canada. These countries were chosen because they are rectifying past discriminatory injustices by allowing black citizens to claim back their land. The focus was to compare their structure in terms of specialists involved in collecting data for the purpose of compensation to the claimants. The findings revealed that competent and experienced skilled personnel can extract, analyse and manipulate data in order to collect data on claimed land. A combination of cadastral surveying and GIS can be used for research on un-surveyed land, complex overlapping claims, and to manage change detections on claimed land. Recalculation of old coordinates (points), conversions of coordinates, sides, reconstruction of parcels and other related data can be accomplished. The Department of Agriculture, Land Reform and Rural Development (DALRRD) has land specialists but they are not involved in the process. The DALRRD hires external experts to resolve complex claims, while they have experts within the department. The DALRRD has resources, equipment and employees who are being underutilised. Recommendations are that research and investigations based on locating the claimed land must be conducted by professional people with the relevant experience and skills to perform the job. This will prevent disputes with landowners and help in shortening the period taken to complete research, as claimants tend to lose patience and take the Commission to court. The DALRRD also needs to understand that the most important part of a land claim is the piece of land that is being claimed, hence there should be no mistakes regarding finding enough information about each piece of land.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".